|
Covid-19 Interest1 #704045
| |
+Citations (227) - CitationsAdd new citationList by: CiterankMapLink[1] Relative Virulence of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Among Vaccinated and Unvaccinated Individuals Hospitalized With SARS-CoV-2
Author: Alicia A Grima, Kiera R Murison, Alison E Simmons, Ashleigh R Tuite, David N Fisman Publication date: 1 February 2023 Publication info: Clinical Infectious Diseases, Volume 76, Issue 3, 1 February 2023, Pages e409âe415 Cited by: David Price 1:03 AM 9 December 2023 GMT Citerank: (4) 679755Ashleigh TuiteAshleigh Tuite is an Assistant Professor in the Epidemiology Division at the Dalla Lana School of Public Health at the University of Toronto.10019D3ABAB, 679777David FismanI am a Professor in the Division of Epidemiology at Division of Epidemiology, Dalla Lana School of Public Health at the University of Toronto. I am a Full Member of the School of Graduate Studies. I also have cross-appointments at the Institute of Health Policy, Management and Evaluation and the Department of Medicine, Faculty of Medicine. I serve as a Consultant in Infectious Diseases at the University Health Network.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1093/cid/ciac412
| Excerpt / Summary [Clinical Infectious Diseases, 1 February 2023]
Background: The rapid development of safe and effective vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a singular scientific achievement. Confounding due to health-seeking behaviors, circulating variants, and differential testing by vaccination status may bias analyses toward an apparent increase in infection severity following vaccination.
Methods: We used data from the Ontario, Canada, Case and Contact Management Database and a provincial vaccination dataset (COVaxON) to create a time-matched cohort of individuals who were hospitalized with SARS-CoV-2 infection. Vaccinated individuals were matched to up to 5 unvaccinated individuals based on test date. Risk of intensive care unit (ICU) admission and death were evaluated using conditional logistic regression.
Results: In 20 064 individuals (3353 vaccinated and 16 711 unvaccinated) hospitalized with infection due to SARS-CoV-2 between 1 January 2021 and 5 January 2022, vaccination with 1, 2, or 3 doses significantly reduced the risk of ICU admission and death. An inverse doseâresponse relationship was observed between vaccine doses received and both outcomes (adjusted odds ratio [aOR] per additional dose for ICU admission, 0.66; 95% confidence interval [CI], .62 to .71; aOR for death, 0.78; 95% CI, .72 to .84).
Conclusions: We identified decreased virulence of SARS-CoV-2 infections in vaccinated individuals, even when vaccines failed to prevent infection sufficiently severe to cause hospitalization. Even with diminished efficacy of vaccines against infection with novel variants of concern, vaccines remain an important tool for reduction of ICU admission and mortality. |
Link[2] Severity of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection in Pregnancy in Ontario: A Matched Cohort Analysis
Author: Kiera R Murison, Alicia A Grima, Alison E Simmons, Ashleigh R Tuite, David N Fisman Publication date: 19 August 2022 Publication info: Clinical Infectious Diseases, Volume 76, Issue 3, 1 February 2023, Pages e200âe206, Cited by: David Price 1:03 AM 9 December 2023 GMT Citerank: (3) 679755Ashleigh TuiteAshleigh Tuite is an Assistant Professor in the Epidemiology Division at the Dalla Lana School of Public Health at the University of Toronto.10019D3ABAB, 679777David FismanI am a Professor in the Division of Epidemiology at Division of Epidemiology, Dalla Lana School of Public Health at the University of Toronto. I am a Full Member of the School of Graduate Studies. I also have cross-appointments at the Institute of Health Policy, Management and Evaluation and the Department of Medicine, Faculty of Medicine. I serve as a Consultant in Infectious Diseases at the University Health Network.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0 URL: DOI: https://doi.org/10.1093/cid/ciac544
| Excerpt / Summary [Clinical Infectious Diseases, 1 February 2023]
Background: Pregnancy represents a physiological state associated with increased vulnerability to severe outcomes from infectious diseases, both for the pregnant person and developing infant. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic may have important health consequences for pregnant individuals, who may also be more reluctant than nonpregnant people to accept vaccination.
Methods: We sought to estimate the degree to which increased severity of SARS-CoV-2 outcomes can be attributed to pregnancy using a population-based SARS-CoV-2 case file from Ontario, Canada. Because of varying propensity to receive vaccination, and changes in dominant circulating viral strains over time, a time-matched cohort study was performed to evaluate the relative risk of severe illness in pregnant women with SARS-CoV-2 compared to other SARS-CoV-2 infected women of childbearing age (10â49 years old). Risk of severe SARS-CoV-2 outcomes was evaluated in pregnant women and time-matched nonpregnant controls using multivariable conditional logistic regression.
Results: Compared with the rest of the population, nonpregnant women of childbearing age had an elevated risk of infection (standardized morbidity ratio, 1.28), whereas risk of infection was reduced among pregnant women (standardized morbidity ratio, 0.43). After adjustment for confounding, pregnant women had a markedly elevated risk of hospitalization (adjusted odds ratio, 4.96; 95% confidence interval, 3.86â6.37) and intensive care unit admission (adjusted odds ratio, 6.58; 95% confidence interval, 3.29â13.18). The relative increase in hospitalization risk associated with pregnancy was greater in women without comorbidities than in those with comorbidities (P for heterogeneity, .004).
Conclusions: Given the safety of SARS-CoV-2 vaccines in pregnancy, risk-benefit calculus strongly favors SARS-CoV-2 vaccination in pregnant women. |
Link[3] Clinical Severity of Severe Acute Respiratory Syndrome Coronavirus 2 Omicron Variant Relative to Delta in British Columbia, Canada: A Retrospective Analysis of Whole-Genome Sequenced Cases
Author: Sean P Harrigan, James Wilton, Mei Chong, Younathan Abdia, Hector Velasquez Garcia, Caren Rose, Marsha Taylor, Sharmistha Mishra, Beate Sander, Linda Hoang, John Tyson, Mel Krajden, Natalie Prystajecky, Naveed Z Janjua, Hind Sbihi Publication date: 30 August 2022 Publication info: Clinical Infectious Diseases, Volume 76, Issue 3, 1 February 2023, Pages e18âe25 Cited by: David Price 1:14 AM 9 December 2023 GMT Citerank: (6) 679757Beate SanderCanada Research Chair in Economics of Infectious Diseases and Director, Health Modeling & Health Economics and Population Health Economics Research at THETA (Toronto Health Economics and Technology Assessment Collaborative).10019D3ABAB, 679854Natalie Anne PrystajeckyNatalie Prystajecky is the program head for the Environmental Microbiology program at the BCCDC Public Health Laboratory. She is also a clinical associate professor in the Department of Pathology & Laboratory Medicine at UBC.10019D3ABAB, 679856Naveed Zafar JanjuaDr. Naveed Zafar Janjua is an epidemiologist and senior scientist at the BC Centre for Disease Control and Clinical Associate Professor at School of Population and Public Health, University of British Columbia. Dr. Janjua is a Medical Doctor (MBBS) with a Masters of Science (MSc) degree in Epidemiology & Biostatistics and Doctorate in Public Health (DrPH). 10019D3ABAB, 679880Sharmistha MishraSharmistha Mishra is an infectious disease physician and mathematical modeler and holds a Tier 2 Canadian Research Chair in Mathematical Modeling and Program Science.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 708734Genomics859FDEF6 URL: DOI: https://doi.org/10.1093/cid/ciac705
| Excerpt / Summary [Clinical Infectious Diseases, 1 February 2023]
Background: In late 2021, the Omicron severe acute respiratory syndrome coronavirus 2 variant emerged and rapidly replaced Delta as the dominant variant. The increased transmissibility of Omicron led to surges in case rates and hospitalizations; however, the true severity of the variant remained unclear. We aimed to provide robust estimates of Omicron severity relative to Delta.
Methods: This retrospective cohort study was conducted with data from the British Columbia COVID-19 Cohort, a large provincial surveillance platform with linkage to administrative datasets. To capture the time of cocirculation with Omicron and Delta, December 2021 was chosen as the study period. Whole-genome sequencing was used to determine Omicron and Delta variants. To assess the severity (hospitalization, intensive care unit [ICU] admission, length of stay), we conducted adjusted Cox proportional hazard models, weighted by inverse probability of treatment weights (IPTW).
Results: The cohort was composed of 13 128 individuals (7729 Omicron and 5399 Delta). There were 419 coronavirus disease 2019 hospitalizations, with 118 (22%) among people diagnosed with Omicron (crude rate = 1.5% Omicron, 5.6% Delta). In multivariable IPTW analysis, Omicron was associated with a 50% lower risk of hospitalization compared with Delta (adjusted hazard ratio [aHR] = 0.50, 95% confidence interval [CI] = 0.43 to 0.59), a 73% lower risk of ICU admission (aHR = 0.27, 95% CI = 0.19 to 0.38), and a 5-day shorter hospital stay (aĂ = â5.03, 95% CI = â8.01 to â2.05).
Conclusions: Our analysis supports findings from other studies that have demonstrated lower risk of severe outcomes in Omicron-infected individuals relative to Delta. |
Link[4] Comparison of influenza and COVID-19 hospitalisations in British Columbia, Canada: a population-based study
Author: Solmaz Setayeshgar, James Wilton, Hind Sbihi, Moe Zandy, Naveed Janjua, Alexandra Choi, Kate Smolina Publication date: 2 February 2023 Publication info: BMJ Open Respiratory Research 2023;10:e001567 Cited by: David Price 1:19 AM 9 December 2023 GMT Citerank: (4) 679856Naveed Zafar JanjuaDr. Naveed Zafar Janjua is an epidemiologist and senior scientist at the BC Centre for Disease Control and Clinical Associate Professor at School of Population and Public Health, University of British Columbia. Dr. Janjua is a Medical Doctor (MBBS) with a Masters of Science (MSc) degree in Epidemiology & Biostatistics and Doctorate in Public Health (DrPH). 10019D3ABAB, 685420Hospitals16289D5D4, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 703974Influenza859FDEF6 URL: DOI: https://doi.org/10.1136/bmjresp-2022-001567
| Excerpt / Summary [BMJ Open Respiratory Research, 2 February 2023]
Introduction: We compared the population rate of COVID-19 and influenza hospitalisations by age, COVID-19 vaccine status and pandemic phase, which was lacking in other studies.
Method: We conducted a population-based study using hospital data from the province of British Columbia (population 5.3âmillion) in Canada with universal healthcare coverage. We created two cohorts of COVID-19 hospitalisations based on date of admission: annual cohort (March 2020 to February 2021) and peak cohort (Omicron era; first 10 weeks of 2022). For comparison, we created influenza annual and peak cohorts using three historical periods years to capture varying severity and circulating strains: 2009/2010, 2015/2016 and 2016/2017. We estimated hospitalisation rates per 100â000 population.
Results: COVID-19 and influenza hospitalisation rates by age group were âJâ shaped. The population rate of COVID-19 hospital admissions in the annual cohort (mostly unvaccinated; public health restrictions in place) was significantly higher than influenza among individuals aged 30â69 years, and comparable to the severe influenza year (2016/2017) among 70+. In the peak COVID-19 cohort (mostly vaccinated; few restrictions in place), the hospitalisation rate was comparable with influenza 2016/2017 in all age groups, although rates among the unvaccinated population were still higher than influenza among 18+. Among people aged 5â17 years, COVID-19 hospitalisation rates were lower than/comparable to influenza years in both cohorts. The COVID-19 hospitalisation rate among 0â4 years old, during Omicron, was higher than influenza 2015/2016 and 2016/2017 and lower than 2009/2010 pandemic.
Conclusions: During first Omicron wave, COVID-19 hospitalisation rates were significantly higher than historical influenza hospitalisation rates for unvaccinated adults but were comparable to influenza for vaccinated adults. For children, in the context of high infection levels, hospitalisation rates for COVID-19 were lower than 2009/2010âH1N1 influenza and comparable (higher for 0â4) to non-pandemic years, regardless of the vaccine status. |
Link[6] The Impact of Mask Mandates on Face Mask Use During the COVID-19 Pandemic: Longitudinal Survey Study
Author: Mawuena Binka, Prince Asumadu Adu, Dahn Jeong, Nirma Khatri Vadlamudi, HĂ©ctor Alexander VelĂĄsquez GarcĂa, Bushra Mahmood, Terri Buller-Taylor, Michael Otterstatter, Naveed Zafar Janjua Publication date: 11 January 2023 Publication info: JMIR Public Health Surveill 2023;9:e42616 Cited by: David Price 1:25 AM 9 December 2023 GMT Citerank: (3) 679856Naveed Zafar JanjuaDr. Naveed Zafar Janjua is an epidemiologist and senior scientist at the BC Centre for Disease Control and Clinical Associate Professor at School of Population and Public Health, University of British Columbia. Dr. Janjua is a Medical Doctor (MBBS) with a Masters of Science (MSc) degree in Epidemiology & Biostatistics and Doctorate in Public Health (DrPH). 10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6 URL: DOI: https://doi.org/10.2196/42616
| Excerpt / Summary [JMIR Public Health and Surveillance, 11 January 2023]
Background: Face mask use has been associated with declines in COVID-19 incidence rates worldwide. A handful of studies have examined the factors associated with face mask use in North America during the COVID-19 pandemic; however, much less is known about the patterns of face mask use and the impact of mask mandates during this time. This information could have important policy implications, now and in the event of future pandemics.
Objective: To address existing knowledge gaps, we assessed face mask usage patterns among British Columbia COVID-19 Population Mixing Patterns (BC-Mix) survey respondents and evaluated the impact of the provincial mask mandate on these usage patterns.
Methods: Between September 2020 and July 2022, adult British Columbia residents completed the web-based BC-Mix survey, answering questions on the circumstances surrounding face mask use or lack thereof, movement patterns, and COVID-19ârelated beliefs. Trends in face mask use over time were assessed, and associated factors were evaluated using multivariable logistic regression. A stratified analysis was done to examine effect modification by the provincial mask mandate.
Results: Of the 44,301 respondents, 81.9% reported wearing face masks during the 23-month period. In-store and public transit mask mandates supported monthly face mask usage rates of approximately 80%, which was further bolstered up to 92% with the introduction of the provincial mask mandate. Face mask users mostly visited retail locations (51.8%) and travelled alone by car (49.6%), whereas nonusers mostly traveled by car with others (35.2%) to their destinationsâmost commonly parks (45.7%). Nonusers of face masks were much more likely to be male than female, especially in retail locations and restaurants, bars, and cafĂ©s. In a multivariable logistic regression model adjusted for possible confounders, factors associated with face mask use included age, ethnicity, health region, mode of travel, destination, and time period. The odds of face mask use were 3.68 times greater when the provincial mask mandate was in effect than when it was not (adjusted odds ratio [aOR] 3.68, 95% CI 3.33-4.05). The impact of the mask mandate was greatest in restaurants, bars, or cafĂ©s (mandate: aOR 7.35, 95% CI 4.23-12.78 vs no mandate: aOR 2.81, 95% CI 1.50-5.26) and in retail locations (mandate: aOR 19.94, 95% CI 14.86-26.77 vs no mandate: aOR 7.71, 95% CI 5.68-10.46).
Conclusions: Study findings provide added insight into the dynamics of face mask use during the COVID-19 pandemic. Mask mandates supported increased and sustained high face mask usage rates during the first 2 years of the pandemic, having the greatest impact in indoor public locations with limited opportunity for physical distancing targeted by these mandates. These findings highlight the utility of mask mandates in supporting high face mask usage rates during the COVID-19 pandemic. |
Link[7] Hands off the Mink! Using Environmental Sampling for SARS-CoV-2 Surveillance in American Mink
Author: Ellen Boyd, Michelle Coombe, Natalie Prystajecky, Jessica M. Caleta, Inna Sekirov, John Tyson, Chelsea Himsworth Publication date: 10 January 2023 Publication info: Int. J. Environ. Res. Public Health 2023, 20(2), 1248; Cited by: David Price 1:39 AM 9 December 2023 GMT Citerank: (2) 679854Natalie Anne PrystajeckyNatalie Prystajecky is the program head for the Environmental Microbiology program at the BCCDC Public Health Laboratory. She is also a clinical associate professor in the Department of Pathology & Laboratory Medicine at UBC.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0 URL: DOI: https://doi.org/10.3390/ijerph20021248
| Excerpt / Summary [Int. J. Environ. Res. Public Health, 10 January 2023]
Throughout the COVID-19 pandemic, numerous non-human species were shown to be susceptible to natural infection by SARS-CoV-2, including farmed American mink. Once infected, American mink can transfer the virus from mink to human and mink to mink, resulting in a high rate of viral mutation. Therefore, outbreak surveillance on American mink farms is imperative for both mink and human health. Historically, disease surveillance on mink farms has consisted of a combination of mortality and live animal sampling; however, these methodologies have significant limitations. This study compared PCR testing of both deceased and live animal samples to environmental samples on an active outbreak premise, to determine the utility of environmental sampling. Environmental sampling mirrored trends in both deceased and live animal sampling in terms of percent positivity and appeared more sensitive in some low-prevalence instances. PCR CT values of environmental samples were significantly different from live animal samplesâ CT values and were consistently high (mean CT = 36.2), likely indicating a low amount of viral RNA in the samples. There is compelling evidence in favour of environmental sampling for the purpose of disease surveillance, specifically as an early warning tool for SARS-CoV-2; however, further work is needed to ultimately determine whether environmental samples are viable sources for molecular epidemiology investigations. |
Link[8] How time-scale differences in asymptomatic and symptomatic transmission shape SARS-CoV-2 outbreak dynamics
Author: Jeremy D. Harris, Sang Woo Park, Jonathan Dushoff, Joshua S. Weitz Publication date: 25 January 2023 Publication info: Epidemics, Volume 42, March 2023, 100664, ISSN 1755-4365, Cited by: David Price 1:55 AM 9 December 2023 GMT Citerank: (2) 679814Jonathan DushoffProfessor in the Department Of Biology at McMaster University.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0 URL: DOI: https://doi.org/10.1016/j.epidem.2022.100664
| Excerpt / Summary [Epidemics, 25 January 2023]
Asymptomatic and symptomatic SARS-CoV-2 infections can have different characteristic time scales of transmission. These time-scale differences can shape outbreak dynamics as well as bias population-level estimates of epidemic strength, speed, and controllability. For example, prior work focusing on the initial exponential growth phase of an outbreak found that larger time scales for asymptomatic vs. symptomatic transmission can lead to under-estimates of the basic reproduction number as inferred from epidemic case data. Building upon this work, we use a series of nonlinear epidemic models to explore how differences in asymptomatic and symptomatic transmission time scales can lead to changes in the realized proportion of asymptomatic transmission throughout an epidemic. First, we find that when asymptomatic transmission time scales are longer than symptomatic transmission time scales, then the effective proportion of asymptomatic transmission increases as total incidence decreases. Moreover, these time-scale-driven impacts on epidemic dynamics are enhanced when infection status is correlated between infector and infectee pairs (e.g., due to dose-dependent impacts on symptoms). Next we apply these findings to understand the impact of time-scale differences on populations with age-dependent assortative mixing and in which the probability of having a symptomatic infection increases with age. We show that if asymptomatic generation intervals are longer than corresponding symptomatic generation intervals, then correlations between age and symptoms lead to a decrease in the age of infection during periods of epidemic decline (whether due to susceptible depletion or intervention). Altogether, these results demonstrate the need to explore the role of time-scale differences in transmission dynamics alongside behavioral changes to explain outbreak features both at early stages (e.g., in estimating the basic reproduction number) and throughout an epidemic (e.g., in connecting shifts in the age of infection to periods of changing incidence). |
Link[9] Generating simple classification rules to predict local surges in COVID-19 hospitalizations
Author: Reza Yaesoubi, Shiying You, Qin Xi, Nicolas A. Menzies, Ashleigh Tuite, Yonatan H. Grad, Joshua A. Salomon Publication date: 24 January 2023 Publication info: Health Care Management Science (2023) Cited by: David Price 1:57 AM 9 December 2023 GMT Citerank: (3) 679755Ashleigh TuiteAshleigh Tuite is an Assistant Professor in the Epidemiology Division at the Dalla Lana School of Public Health at the University of Toronto.10019D3ABAB, 685420Hospitals16289D5D4, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0 URL: DOI: https://doi.org/10.1007/s10729-023-09629-4
| Excerpt / Summary [Health Care Management Science, 24 January 2023]
Low rates of vaccination, emergence of novel variants of SARS-CoV-2, and increasing transmission relating to seasonal changes and relaxation of mitigation measures leave many US communities at risk for surges of COVID-19 that might strain hospital capacity, as in previous waves. The trajectories of COVID-19 hospitalizations differ across communities depending on their age distributions, vaccination coverage, cumulative incidence, and adoption of risk mitigating behaviors. Yet, existing predictive models of COVID-19 hospitalizations are almost exclusively focused on national- and state-level predictions. This leaves local policymakers in urgent need of tools that can provide early warnings about the possibility that COVID-19 hospitalizations may rise to levels that exceed local capacity. In this work, we develop a framework to generate simple classification rules to predict whether COVID-19 hospitalization will exceed the local hospitalization capacity within a 4- or 8-week period if no additional mitigating strategies are implemented during this time. This framework uses a simulation model of SARS-CoV-2 transmission and COVID-19 hospitalizations in the US to train classification decision trees that are robust to changes in the data-generating process and future uncertainties. These generated classification rules use real-time data related to hospital occupancy and new hospitalizations associated with COVID-19, and when available, genomic surveillance of SARS-CoV-2. We show that these classification rules present reasonable accuracy, sensitivity, and specificity (allââ„â80%) in predicting local surges in hospitalizations under numerous simulated scenarios, which capture substantial uncertainties over the future trajectories of COVID-19. Our proposed classification rules are simple, visual, and straightforward to use in practice by local decision makers without the need to perform numerical computations. |
Link[10] Cross-Canada Variability in Blood Donor SARS-CoV-2 Seroprevalence by Social Determinants of Health
Author: Sheila F. OâBrien, Niamh Caffrey, Qi-Long Yi, Shelly Bolotin, Naveed Z. Janjua, Mawuena Binka, Caroline Quach Thanh, Steven J. Drews Publication date: 10 January 2023 Publication info: Clinical Microbiology, 10 January 2023 Cited by: David Price 2:01 AM 9 December 2023 GMT Citerank: (2) 679856Naveed Zafar JanjuaDr. Naveed Zafar Janjua is an epidemiologist and senior scientist at the BC Centre for Disease Control and Clinical Associate Professor at School of Population and Public Health, University of British Columbia. Dr. Janjua is a Medical Doctor (MBBS) with a Masters of Science (MSc) degree in Epidemiology & Biostatistics and Doctorate in Public Health (DrPH). 10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0 URL: DOI: https://doi.org/10.1128/spectrum.03356-22
| Excerpt / Summary [Clinical Microbiology, 10 January 2023]
We compared the seroprevalence of SARS-CoV-2 anti-nucleocapsid antibodies in blood donors across Canadian regions in 2021. The seroprevalence was the highest in Alberta and the Prairies, and it was so low in Atlantic Canada that few correlates were observed. Being male and of young age were predictive of seropositivity. Racialization was associated with higher seroprevalence in British Columbia and Ontario but not in Alberta and the Prairies. Living in a materially deprived neighborhood predicted higher seroprevalence, but it was more linear across quintiles in Alberta and the Prairies, whereas in British Columbia and Ontario, the most affluent 60% were similarly low and the most deprived 40% similarly elevated. Living in a more socially deprived neighborhood (more single individuals and one parent families) was associated with lower seroprevalence in British Columbia and Ontario but not in Alberta and the Prairies. These data show striking variability in SARS-CoV-2 seroprevalence across regions by social determinants of health. |
Link[12] Clinical severity of Omicron subvariants BA.1, BA.2, and BA.5 in a population-based cohort study in British Columbia, Canada
Author: Shannon L. Russell, Braeden R. A. Klaver, Sean P. Harrigan, Kimia Kamelian, John Tyson, Linda Hoang, Marsha Taylor, Beate Sander, Sharmistha Mishra, Natalie Prystajecky, Naveed Z. Janjua, James E. A. Zlosnik, Hind Sbihi Publication date: 22 December 2022 Publication info: Journal of Medical Virology, Volume 95, Issue 1 e28423 Cited by: David Price 2:05 AM 9 December 2023 GMT Citerank: (1) 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0 URL: DOI: https://doi.org/10.1002/jmv.28423
| Excerpt / Summary [Journal of Medical Virology, 22 December 2022]
The SARS-CoV-2 variant Omicron emerged in late 2021. In British Columbia (BC), Canada, and globally, three genetically distinct subvariants of Omicron, BA.1, BA.2, and BA.5, emerged and became dominant successively within an 8-month period. SARS-CoV-2 subvariants continue to circulate in the population, acquiring new mutations that have the potential to alter infectivity, immunity, and disease severity. Here, we report a propensity-matched severity analysis from residents of BC over the course of the Omicron wave, including 39,237 individuals infected with BA.1, BA.2, or BA.5 based on paired high-quality sequence data and linked to comprehensive clinical outcomes data between December 23, 2021 and August 31, 2022. Relative to BA.1, BA.2 cases were associated with a 15% and 28% lower risk of hospitalization and intensive care unit (ICU) admission (aHRhospitalâ=â1.17; 95% confidence interval [CI]â=â1.096â1.252; aHRICUâ=â1.368; 95% CIâ=â1.152â1.624), whereas BA.5 infections were associated with an 18% higher risk of hospitalization (aHRhospitalâ=â1.18; 95% CIâ=â1.133â1.224) after accounting for age, sex, comorbidities, vaccination status, geography, and social determinants of health. Phylogenetic analysis revealed no specific subclades associated with more severe clinical outcomes for any Omicron subvariant. In summary, BA.1, BA.2, and BA.5 subvariants were associated with differences in clinical severity, emphasizing how variant-specific monitoring programs remain critical components of patient and population-level public health responses as the pandemic continues. |
Link[13] Risk factors for COVID-19 hospitalization after COVID-19 vaccination: a population-based cohort study in Canada
Author: HĂ©ctor A. VelĂĄsquez GarcĂa, Prince A. Adu, Sean Harrigan, Hind Sbihi, Kate Smolina, Naveed Z. Janjua Publication date: 7 December 2022 Publication info: International Journal of Infectious Diseases, VOLUME 127, P116-123, FEBRUARY 2023 Cited by: David Price 2:09 AM 9 December 2023 GMT Citerank: (4) 679856Naveed Zafar JanjuaDr. Naveed Zafar Janjua is an epidemiologist and senior scientist at the BC Centre for Disease Control and Clinical Associate Professor at School of Population and Public Health, University of British Columbia. Dr. Janjua is a Medical Doctor (MBBS) with a Masters of Science (MSc) degree in Epidemiology & Biostatistics and Doctorate in Public Health (DrPH). 10019D3ABAB, 685420Hospitals16289D5D4, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1016/j.ijid.2022.12.001
| Excerpt / Summary [International Journal of Infectious Diseases, February 2023]
Objectives: With the uptake of COVID-19 vaccines, there is a need for population-based studies to assess risk factors for COVID-19-related hospitalization after vaccination and how they differ from unvaccinated individuals.
Methods: We used data from the British Columbia COVID-19 Cohort, a population-based cohort that includes all individuals (aged â„18 years) who tested positive for SARS-CoV-2 by real-time reverse transcription-polymerase chain reaction from January 1, 2021 (after the start of vaccination program) to December 31, 2021. We used multivariable logistic regression models to assess COVID-19-related hospitalization risk by vaccination status and age group among confirmed COVID-19 cases.
Results: Of the 162,509 COVID-19 cases included in the analysis, 8,546 (5.3%) required hospitalization. Among vaccinated individuals, an increased odds of hospitalization with increasing age was observed for older age groups, namely those aged 50-59 years (odds ratio [OR] = 2.95, 95% confidence interval [CI]: 2.01-4.33), 60-69 years (OR = 4.82, 95% CI: 3.29, 7.07), 70-79 years (OR = 11.92, 95% CI: 8.02, 17.71), and â„80 years (OR = 24.25, 95% CI: 16.02, 36.71). However, among unvaccinated individuals, there was a graded increase in odds of hospitalization with increasing age, starting at age group 30-39 years (OR = 2.14, 95% CI: 1.90, 2.41) to â„80 years (OR = 41.95, 95% CI: 35.43, 49.67). Also, comparing all the age groups to the youngest, the observed magnitude of association was much higher among unvaccinated individuals than vaccinated ones.
Conclusion: Alongside a number of comorbidities, our findings showed a strong association between age and COVID-19-related hospitalization, regardless of vaccination status. However, age-related hospitalization risk was reduced two-fold by vaccination, highlighting the need for vaccination in reducing the risk of severe disease and subsequent COVID-19-related hospitalization across all population groups. |
Link[14] Observed versus expected rates of myocarditis after SARS-CoV-2 vaccination: a population-based cohort study
Author: Zaeema Naveed, Julia Li, Michelle Spencer, James Wilton, Monika Naus, HĂ©ctor Alexander VelĂĄsquez GarcĂa, Michael Otterstatter, Naveed Zafar Janjua Publication date: 21 November 2022 Publication info: CMAJ November 21, 2022 194 (45) E1529-E1536; Cited by: David Price 2:13 AM 9 December 2023 GMT Citerank: (4) 679856Naveed Zafar JanjuaDr. Naveed Zafar Janjua is an epidemiologist and senior scientist at the BC Centre for Disease Control and Clinical Associate Professor at School of Population and Public Health, University of British Columbia. Dr. Janjua is a Medical Doctor (MBBS) with a Masters of Science (MSc) degree in Epidemiology & Biostatistics and Doctorate in Public Health (DrPH). 10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1503/cmaj.220676
| Excerpt / Summary [CMAJ, 21 November 2022]
Background: Postmarketing evaluations have linked myocarditis to SARS-CoV-2 mRNA vaccines. We sought to estimate the incidence of myocarditis after mRNA vaccination against SARS-CoV-2, and to compare the incidence with expected rates based on historical background rates in British Columbia.
Methods: We conducted an observational study using population health administrative data from the BC COVID-19 Cohort from Dec. 15, 2020, to Mar. 10, 2022. The primary exposure was any dose of an mRNA vaccine against SARS-CoV-2. The primary outcome was incidence of hospital admission or emergency department visit for myocarditis or myopericarditis within 7 and 21 days postvaccination, calculated as myocarditis rates per 100 000 mRNA vaccine doses, expected rates of myocarditis cases and observedto-expected ratios. We stratified analyses by age, sex, vaccine type and dose number.
Results: We observed 99 incident cases of myocarditis within 7 days (0.97 cases per 100 000 vaccine doses; observed v. expected ratio 14.81, 95% confidence interval [CI] 10.83â16.55) and 141 cases within 21 days (1.37 cases per 100 000 vaccine doses; observed v. expected ratio 7.03, 95% CI 5.92â8.29) postvaccination. Cases of myocarditis per 100 000 vaccine doses were higher for people aged 12â17 years (2.64, 95% CI 1.54â4.22) and 18â29 years (2.63, 95% CI 1.94â3.50) than for older age groups, for males compared with females (1.64, 95% CI 1.30â2.04 v. 0.35, 95% CI 0.21â0.55), for those receiving a second dose compared with a third dose (1.90, 95% CI 1.50â2.39 v. 0.76, 95% CI 0.45â1.30) and for those who received the mRNA-1273 (Moderna) vaccine compared with the BNT162b2 (Pfizer-BioNTech) vaccine (1.44, 95% CI 1.06â1.91 v. 0.74, 95% CI 0.56â0.98). The highest observed-to-expected ratio was seen after the second dose among males aged 18â29 years who received the mRNA-1273 vaccine (148.32, 95% CI 95.03â220.69).
Interpretation: Although absolute rates of myocarditis were low, vaccine type, age and sex are important factors to consider when strategizing vaccine administration to reduce the risk of postvaccination myocarditis. Our findings support the preferential use of the BNT162b2 vaccine over the mRNA-1273 vaccine for people aged 18â29 years.
As of September 2022, more than 32 million people in Canada, including around 4.5 million in British Columbia, have received a vaccine to prevent SARS-CoV-2 infection.1 With any novel vaccine, safety and effectiveness are important to public health and may determine the success of achieving the targeted immunization coverage. According to a recent systematic review, the overall rate of SARS-CoV-2 vaccination acceptance ranges from 53.6% to 84.4% in the United States.2 One of the key reasons for vaccine hesitancy is the fear of adverse effects.3,4
As large populations are vaccinated, certain uncommon events may be observed that were not detected during the premarketing clinical trials, whether or not these events are related to the vaccine. The same is the case with SARS-CoV-2 vaccination. The prelicensure study data did not suggest any risk of postvaccination myocarditis. However, postmarketing studies have suggested an association between mRNA SARS-CoV-2 vaccines (BNT162b2 [Pfizer-BioNTech] and mRNA-1273 [Moderna]) and myocarditis, among other adverse events after immunization, which has raised concern regarding the safety of mRNA vaccines, specifically among younger populations.5â7 Most evidence comes from case reports and case series. Earlier data have suggested higher rates of myocarditis among young adults after the mRNA-1273 compared with the BNT162b2 vaccine. Limited data are available on the rate of myocarditis after the third dose, which is relevant as further boosters are planned. Given the important economic and health consequences of COVID-19, it is vital to further evaluate the likelihood of this signal.
One of the pharmacoepidemiologic methods that refine a previously detected signal is an observed-to-expected analysis, which compares the number of cases observed or reported to a calculated number of cases expected under the null hypothesis of no association between the intervention and the disease.8 Thus, the primary objective of this study was to determine the incidence of patients who visited the emergency department or were admitted to the hospital with myocarditis after mRNA SARS-CoV-2 vaccination, and to compare these observed results to expected numbers based on historical rates before the rollout of SARS-CoV-2 vaccination. |
Link[15] Evaluation of Real-life Use of Point-of-care Rapid Antigen Testing for SARS-CoV-2 in Schools (EPOCRATES): a cohort study
Author: Ana C. Blanchard, Marc Desforges, Annie-Claude LabbĂ©, Cat Tuong Nguyen, Yves Petit, Dominic Besner, Kate Zinszer, Olivier SĂ©guin, Zineb Laghdir, Kelsey Adams, Marie-Ăve Benoit, GeneviĂšve Leduc, Jean Longtin, Jiannis Ragoussis, David L. Buckeridge, Caroline Quach Publication date: 6 December 2022 Publication info: CMAJ OPEN, December 06, 2022 10 (4) E1027-E1033 Cited by: David Price 2:15 AM 9 December 2023 GMT Citerank: (2) 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 715831Diagnostic testing859FDEF6 URL: DOI: https://doi.org/10.9778/cmajo.20210327
| Excerpt / Summary [CMAJ OPEN, 6 December 2022]
Background: SARS-CoV-2 transmission has an impact on education. In this study, we assessed the performance of rapid antigen detection tests (RADTs) versus polymerase chain reaction (PCR) for the diagnosis of SARS-CoV-2 infection in school settings, and RADT use for monitoring exposed contacts.
Methods: In this real-world, prospective observational cohort study, high-school students and staff were recruited from 2 high schools in Montréal, Canada, and followed from Jan. 25 to June 10, 2021. Twenty-five percent of asymptomatic participants were tested weekly by RADT (nasal) and PCR (gargle). Class contacts of cases were tested. Symptomatic participants were tested by RADT (nasal) and PCR (nasal and gargle). The number of cases and outbreaks were compared with those of other high schools in the same area.
Results: Overall, 2099 students and 286 school staff members consented to participate. The overall specificity of RADTs varied from 99.8% to 100%, with a lower sensitivity, varying from 28.6% in asymptomatic to 83.3% in symptomatic participants. Secondary cases were identified in 10 of 35 classes. Returning students to school after a 7-day quarantine, with a negative PCR result on days 6â7 after exposure, did not lead to subsequent outbreaks. Of cases for whom the source was known, 37 of 51 (72.5%) were secondary to household transmission, 13 (25.5%) to intraschool transmission, and 1 to community contacts between students in the same school.
Interpretation: Rapid antigen detection tests did not perform well compared with PCR in asymptomatic individuals. Reinforcing policies for symptom screening when entering schools and testing symptomatic individuals with RADTs on the spot may avoid subsequent substantial exposures in class. Preprint: medRxiv â doi.org/10.1101/2021.10.13.21264960
Timely diagnosis of infection enables outbreak control through rapid isolation of index cases and subsequent contact tracing.1,2 Diagnosis of SARS-CoV-2 infection is predominantly based on polymerase chain reaction (PCR), which has a turnaround time of 24â48 hours. Rapid antigen detection tests (RADTs) are inexpensive and can be used at the point of care. They usually have high specificity and moderate sensitivity compared with PCR.3â6 Given their rapid turnaround time, RADTs allow for efficient triage and management of exposed individuals.7 The potential use of RADTs is especially relevant in schools, where outbreaks of SARS-CoV-2 infection can interrupt in-person teaching and negatively affect learning.8â11
Rapid antigen detection tests perform best in the early stages of infection, when viral load is generally high.12â15 Reported RADT sensitivity ranges from 28.9% to 98.3%, with improved sensitivity in samples with high viral loads and in symptomatic individuals.16,17 The usual limits of detection for PCR is 600â1000 viral RNA copies/mL, whereas RADTs usually have limits of detection 2â3 logs higher (105 to 106).18 Many studies have indicated the importance of high viral load dynamics with infectiousness. 19,20 For each unit increase in cycle threshold (Ct) value, the odds of recovering infectious virus decreased by 0.67, being under 10% when Ct values were greater than 35. Cycle threshold values of 17 to 32 corresponded to 105 and 101 SARS-CoV-2 RNA copies/ÎŒL, respectively.21
We aimed to determine the performance characteristics of RADTs for SARS-CoV-2 compared with PCR in high-school students and staff, and to determine whether serial testing of COVID-19 contacts would allow for safe faster return to school. |
Link[16] Effectiveness of COVID-19 vaccines in people living with HIV in British Columbia and comparisons with a matched HIV-negative cohort: a test-negative design
Author: Adeleke Fowokan, Hasina Samji, Joseph H. Puyat, Ann N. Burchell, Aslam Anis, COVAXHIV study team Publication date: 30 November 2022 Publication info: International Journal of Infectious Diseases, VOLUME 127, P162-170, FEBRUARY 2023 Cited by: David Price 2:17 AM 9 December 2023 GMT Citerank: (4) 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6, 704041Vaccination859FDEF6, 708761HIV859FDEF6 URL: DOI: https://doi.org/10.1016/j.ijid.2022.11.035
| Excerpt / Summary [International Journal of Infectious Diseases, February 2023]
Objectives: We estimated the effectiveness of COVID-19 vaccines against laboratory-confirmed SARS-CoV-2 infection among people living with HIV (PLWH) and compared the estimates with a matched HIV-negative cohort.
Methods: We used the British Columbia COVID-19 Cohort, a population-based data platform, which integrates COVID-19 data on SARS-CoV-2 tests, laboratory-confirmed cases, and immunizations with provincial health services data. The vaccine effectiveness (VE) was estimated with a test-negative design using the multivariable logistic regression.
Results: The adjusted VE against SARS-CoV-2 infection was 71.1% (39.7, 86.1%) 7-59 days after two doses, rising to 89.3% (72.2, 95.9%) between 60 and 89 days. VE was preserved 4-6 months after the receipt of two doses, after which noticeable waning was observed (51.3% [4.8, 75.0%]). In the matched HIV-negative cohort (n = 375,043), VE peaked at 91.4% (90.9, 91.8%) 7-59 days after two doses and was sustained for up to 4 months, after which evidence of waning was observed, dropping to 84.2% (83.4, 85.0%) between 4 and 6 months.
Conclusion: The receipt of two COVID-19 vaccine doses was effective against SARS-CoV-2 infection among PLWH pre-Omicron. VE estimates appeared to peak later in PLWH than in the matched HIV-negative cohort and the degree of waning was relatively quicker in PLWH; however, peak estimates were comparable in both populations. |
Link[17] Two-Dose Severe Acute Respiratory Syndrome Coronavirus 2 Vaccine Effectiveness With Mixed Schedules and Extended Dosing Intervals: Test-Negative Design Studies From British Columbia and Quebec, Canada
Author: Danuta M Skowronski, Yossi Febriani, Manale Ouakki, et al. - Solmaz Setayeshgar, Shiraz El Adam, Macy Zou, Denis Talbot, Natalie Prystajecky, John R Tyson, Rodica Gilca, Nicholas Brousseau, GeneviĂšve Deceuninck, Eleni Galanis, Chris D Fjell, Hind Sbihi, Elise Fortin, Sapha Barkati, Chantal Sauvageau, Monika Naus, David M Patrick, Bonnie Henry, Linda M N Hoang, Philippe De Wals, Christophe Garenc, Alex Carignan, MĂ©lanie Drolet, Agatha N Jassem, Manish Sadarangani, Marc Brisson, Mel Krajden, Gaston De Serres Publication date: 19 April 2022 Publication info: Clinical Infectious Diseases, Volume 75, Issue 11, 1 December 2022, Pages 1980â1992 Cited by: David Price 2:20 AM 9 December 2023 GMT Citerank: (5) 679839Marc BrissonDr. Marc Brisson is full professor at Laval University where he leads the Research Group in Mathematical Modeling and Health Economics of Infectious Diseases.10019D3ABAB, 679854Natalie Anne PrystajeckyNatalie Prystajecky is the program head for the Environmental Microbiology program at the BCCDC Public Health Laboratory. She is also a clinical associate professor in the Department of Pathology & Laboratory Medicine at UBC.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1093/cid/ciac290
| Excerpt / Summary [Clinical Infectious Diseases, December 2022]
Background: The Canadian coronavirus disease 2019 (COVID-19) immunization strategy deferred second doses and allowed mixed schedules. We compared 2-dose vaccine effectiveness (VE) by vaccine type (mRNA and/or ChAdOx1), interval between doses, and time since second dose in 2 of Canadaâs larger provinces.
Methods: Two-dose VE against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection or hospitalization among adults â„18 years, including due to Alpha, Gamma, and Delta variants of concern (VOCs), was assessed â„14 days postvaccination by test-negative design studies separately conducted in British Columbia and Quebec, Canada, between 30 May and 27 November (epi-weeks 22â47) 2021.
Results: In both provinces, all homologous or heterologous mRNA and/or ChAdOx1 2-dose schedules were associated with â„90% reduction in SARS-CoV-2 hospitalization risk for â„7 months. With slight decline from a peak of >90%, VE against infection was â„80% for â„6 months following homologous mRNA vaccination, lower by âŒ10% when both doses were ChAdOx1 but comparably high following heterologous ChAdOx1 + mRNA receipt. Findings were similar by age group, sex, and VOC. VE was significantly higher with longer 7â8-week versus manufacturer-specified 3â4-week intervals between mRNA doses.
Conclusions: Two doses of any mRNA and/or ChAdOx1 combination gave substantial and sustained protection against SARS-CoV-2 hospitalization, spanning Delta-dominant circulation. ChAdOx1 VE against infection was improved by heterologous mRNA series completion. A 7â8-week interval between first and second doses improved mRNA VE and may be the optimal schedule outside periods of intense epidemic surge. Findings support interchangeability and extended intervals between SARS-CoV-2 vaccine doses, with potential global implications for low-coverage areas and, going forward, for children. |
Link[18] Medical Masks Versus N95 Respirators for Preventing COVID-19 Among Health Care Workers
Author: David N. Fisman, Raina Macintyre Publication date: 18 July 2023 Publication info: Annals of Internal Medicine, July 2023, Volume 176, Issue 7 Cited by: David Price 2:25 AM 9 December 2023 GMT Citerank: (4) 679777David FismanI am a Professor in the Division of Epidemiology at Division of Epidemiology, Dalla Lana School of Public Health at the University of Toronto. I am a Full Member of the School of Graduate Studies. I also have cross-appointments at the Institute of Health Policy, Management and Evaluation and the Department of Medicine, Faculty of Medicine. I serve as a Consultant in Infectious Diseases at the University Health Network.10019D3ABAB, 685420Hospitals16289D5D4, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6 URL: DOI: https://doi.org/10.7326/L23-0073
| Excerpt / Summary [Annals of Internal Medicine, 18 July 2023]
Regarding their trial, Loeb and colleagues (1) state, âthe overall estimates rule out a doubling in hazardâ; however, no such conclusion is possible. The trial used an extraordinary threshold (a hazard ratio of 2, or a 100% relative increase in risk) for noninferiority and was underpowered to find smaller but still important risks. (We estimate that a 4-fold increase in sample size would have been needed to identify a 50% increase in relative hazard.) Power aside, design flaws biased the study toward the null result that was obtained.
The intervention under study was incorrect use of N95 respiratorsâintermittently rather than continuously. SARS-CoV-2 is an airborne pathogen (2). Infection occurs via inhalation of shared air, and infective aerosols accumulate over time in closed indoor settings. As such, only continuous use of N95 respirators protects health care workers against respiratory infection; intermittent use of medical masks and respirators is equally ineffective (3). Unplanned crossover (those randomly assigned to medical masks could reassign themselves to the N95 group on the basis of unrecorded risk assessment) and contamination due to failure to use a cluster design further biased study results toward the null (4).
Notwithstanding lack of power and multiple biasesâand although only 21 infections developed among 301 participants recruited in Canada and Israel through May 2021âanalysis according to the registered protocol reveals a doubling of risk for infection for medical masks (relative risk, 2.05 [95% CI, 0.85 to 4.95]; P = 0.10) in these participants. The study had come close to showing inferiority after recruiting only a fraction of its prespecified sample size. Around this time, the authors recalculated their required sample size (in July 2021) as 1010 participants and began recruiting participants in Pakistan at a site not mentioned in the trial's registered protocol. Six months later, recruitment in Pakistan was discontinued and was begun in Egypt (also not registered in the protocol). Final results were heavily influenced by the inclusion of the sites in Egypt, with more than 70% of infections originating there. As Altman and associates note, âwhen authors substitute other outcomes after the trial has started there must be concern that such changes were done with knowledge of the data. That casts doubt on the reliability and integrity of the resultsâ (5).
Lastly, the performance of this trial lacked equipoise in the face of clear engineering evidence of the superiority of respirators for airborne pathogens. The fact that this trial was done in a flawed manner that could not provide valid results means that participants were endangered for no reason. |
Link[19] Effect of the incremental protection of previous infection against Omicron infection among individuals with a hybrid of infection- and vaccine-induced immunity: a population-based cohort study in Canada
Author: Shishi Wu, Yanhong Li, Sharmistha Mishra, Korryn Bodner, Stefan Baral, Jeffrey C. Kwong, Xiaolin Wei Publication date: 28 November 2022 Publication info: International Journal of Infectious Diseases, Volume 127, P69-76, FEBRUARY 2023 Cited by: David Price 2:28 AM 9 December 2023 GMT Citerank: (3) 679880Sharmistha MishraSharmistha Mishra is an infectious disease physician and mathematical modeler and holds a Tier 2 Canadian Research Chair in Mathematical Modeling and Program Science.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1016/j.ijid.2022.11.028
| Excerpt / Summary [International Journal of Infectious Diseases, 28 November 2022]
Objectives: We examined the incremental protection and durability of infection-acquired immunity against Omicron infection in individuals with hybrid immunity in Ontario, Canada.
Methods: We followed up 6 million individuals with at least one multiplex reverse transcriptaseâpolymerase chain reaction test before November 21, 2021, until an Omicron infection. Protection via infection-acquired immunity was assessed by comparing Omicron infection risk between previously infected individuals and those without documented infection under different vaccination scenarios and stratified by time since the last infection or vaccination.
Results: A previous infection was associated with 68% (95% CI 61-73) and 43% (95% CI 27-56) increased protection against Omicron infection in individuals with two and three doses, respectively. Among individuals with two-dose vaccination, the incremental protection of infection-induced immunity decreased from 79% (95% CI 75-81) within 3 months after vaccination or infection to 27% (95% CI 14-37) at 9-11 months. In individuals with three-dose vaccination, it decreased from 57% (95% CI 50-63) within 3 months to 37% (95% CI 19-51) at 3-5 months after vaccination or infection.
Conclusion: Previous SARS-CovV-2 infections provide added cross-variant immunity to vaccination. Given the limited durability of infection-acquired protection in individuals with hybrid immunity, its influence on shield-effects at the population level and reinfection risks at the individual level may be limited. |
Link[20] COVID-19 endgame: From pandemic to endemic? Vaccination, reopening and evolution in low- and high-vaccinated populations
Author: Elisha B. Are, Yexuan Song, Jessica E. Stockdale, Paul Tupper, Caroline Colijn Publication date: 20 December 2022 Publication info: Journal of Theoretical Biology, Volume 559, 2023, 111368, ISSN 0022-5193, Cited by: David Price 2:30 AM 9 December 2023 GMT Citerank: (4) 679761Caroline ColijnDr. Caroline Colijn works at the interface of mathematics, evolution, infection and public health, and leads the MAGPIE research group. She joined SFU's Mathematics Department in 2018 as a Canada 150 Research Chair in Mathematics for Infection, Evolution and Public Health. She has broad interests in applications of mathematics to questions in evolution and public health, and was a founding member of Imperial College London's Centre for the Mathematics of Precision Healthcare.10019D3ABAB, 679862Paul TupperProfessor in the Department of Mathematics at Simon Fraser University.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1016/j.jtbi.2022.111368
| Excerpt / Summary [Journal of Theoretical Biology, 20 December 2022]
COVID-19 remains a major public health concern, with large resurgences even where there has been widespread uptake of vaccines. Waning immunity and the emergence of new variants will shape the long-term burden and dynamics of COVID-19. We explore the transition to the endemic state, and the endemic incidence in British Columbia (BC), Canada and South Africa (SA), to compare low and high vaccination coverage settings with differing public health policies, using a combination of modelling approaches. We compare reopening (relaxation of public health measures) gradually and rapidly as well as at different vaccination levels. We examine how the eventual endemic state depends on the duration of immunity, the rate of importations, the efficacy of vaccines and the transmissibility. These depend on the evolution of the virus, which continues to undergo selection. Slower reopening leads to a lower peak level of incidence and fewer overall infections in the wave following reopening: as much as a 60% lower peak and a 10% lower total in some illustrative simulations; under realistic parameters, reopening when 70% of the population is vaccinated leads to a large resurgence in cases. The long-term endemic behaviour may stabilize as late as January 2023, with further waves of high incidence occurring depending on the transmissibility of the prevalent variant, duration of immunity, and antigenic drift. We find that long term endemic levels are not necessarily lower than current pandemic levels: in a population of 100,000 with representative parameter settings (Reproduction number 5, 1-year duration of immunity, vaccine efficacy at 80% and importations at 3 cases per 100K per day) there are over 100 daily incident cases in the model. Predicted prevalence at endemicity has increased more than twofold after the emergence and spread of Omicron. The consequent burden on health care systems depends on the severity of infection in immunized or previously infected individuals. |
Link[21] Cytomegalovirus Seropositivity in Older Adults Changes the T Cell Repertoire but Does Not Prevent Antibody or Cellular Responses to SARS-CoV-2 Vaccination
Author: Jessica A. Breznik, Angela Huynh, Ali Zhang, Lucas Bilaver, Hina Bhakta, Hannah D. Stacey, Jann C. Ang; Jonathan L. Bramson, Ishac Nazy, Matthew S. Miller, Judah Denburg, Andrew P. Costa, Dawn M. E. Bowdish, COVID-in-LTC Investigator Group Publication date: 15 November 2022 Publication info: J Immunol (2022) 209 (10): 1892â1905, 15 November 2022 Cited by: David Price 2:32 AM 9 December 2023 GMT Citerank: (2) 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.4049/jimmunol.2200369
| Excerpt / Summary [The Journal of Immunology, 15 November 2022]
Chronic infection with human CMV may contribute to poor vaccine efficacy in older adults. We assessed the effects of CMV serostatus on Ab quantity and quality, as well as cellular memory recall responses, after two and three SARS-CoV-2 mRNA vaccine doses, in older adults in assisted living facilities. CMV serostatus did not affect anti-Spike and antiâreceptor-binding domain IgG Ab levels, nor neutralization capacity against wild-type or ÎČ variants of SARS-CoV-2 several months after vaccination. CMV seropositivity altered T cell expression of senescence-associated markers and increased effector memory re-expressing CD45RA T cell numbers, as has been previously reported; however, this did not impact Spike-specific CD4+ T cell memory recall responses. CMV-seropositive individuals did not have a higher incidence of COVID-19, although prior infection influenced humoral immunity. Therefore, CMV seropositivity may alter T cell composition but does not impede the durability of humoral protection or cellular memory responses after SARS-CoV-2 mRNA vaccination in older adults. |
Link[22] Global SARS-CoV-2 seroprevalence from January 2020 to April 2022: A systematic review and meta-analysis of standardized population-based studies
Author: Isabel Bergeri, Mairead G. Whelan, Harriet Ware, et al. Unity Studies Collaborator Group - Lorenzo Subissi, Anthony Nardone, Hannah C. Lewis, Zihan Li,Xiaomeng Ma, Marta Valenciano, Brianna Cheng, Lubna Al Ariqi, Arash Rashidian, Joseph Okeibunor, Tasnim Azim, Pushpa Wijesinghe, Linh-Vi Le, Aisling Vaughan, Richard Pebody, Andrea Vicari, Tingting Yan, Mercedes Yanes-Lane, Christian Cao, David A. Clifton, Matthew P. Cheng, Jesse Papenburg, David Buckeridge, Niklas Bobrovitz, Rahul K. Arora, Maria D. Van Kerkhove Publication date: 10 November 2022 Publication info: PLoS Med 19(11): e1004107 Cited by: David Price 2:32 AM 9 December 2023 GMT Citerank: (3) 679775David BuckeridgeDavid is a Professor in the School of Population and Global Health at McGill University, where he directs the Surveillance Lab, an interdisciplinary group that develops, implements, and evaluates novel computational methods for population health surveillance. He is also the Chief Digital Health Officer at the McGill University Health Center where he directs strategy on digital transformation and analytics and he is an Associate Member with the Montreal Institute for Learning Algorithms (Mila).10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 715376Serosurveillance859FDEF6 URL: DOI: https://doi.org/10.1371/journal.pmed.1004107
| Excerpt / Summary [PLoS Medicine, 10 November 2022]
Background: Our understanding of the global scale of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection remains incomplete: Routine surveillance data underestimate infection and cannot infer on population immunity; there is a predominance of asymptomatic infections, and uneven access to diagnostics. We meta-analyzed SARS-CoV-2 seroprevalence studies, standardized to those described in the World Health Organizationâs Unity protocol (WHO Unity) for general population seroepidemiological studies, to estimate the extent of population infection and seropositivity to the virus 2 years into the pandemic.
Methods and findings: We conducted a systematic review and meta-analysis, searching MEDLINE, Embase, Web of Science, preprints, and grey literature for SARS-CoV-2 seroprevalence published between January 1, 2020 and May 20, 2022. The review protocol is registered with PROSPERO (CRD42020183634). We included general population cross-sectional and cohort studies meeting an assay quality threshold (90% sensitivity, 97% specificity; exceptions for humanitarian settings). We excluded studies with an unclear or closed population sample frame. Eligible studiesâthose aligned with the WHO Unity protocolâwere extracted and critically appraised in duplicate, with risk of bias evaluated using a modified Joanna Briggs Institute checklist. We meta-analyzed seroprevalence by country and month, pooling to estimate regional and global seroprevalence over time; compared seroprevalence from infection to confirmed cases to estimate underascertainment; meta-analyzed differences in seroprevalence between demographic subgroups such as age and sex; and identified national factors associated with seroprevalence using meta-regression. We identified 513 full texts reporting 965 distinct seroprevalence studies (41% low- and middle-income countries [LMICs]) sampling 5,346,069 participants between January 2020 and April 2022, including 459 low/moderate risk of bias studies with national/subnational scope in further analysis. By September 2021, global SARS-CoV-2 seroprevalence from infection or vaccination was 59.2%, 95% CI [56.1% to 62.2%]. Overall seroprevalence rose steeply in 2021 due to infection in some regions (e.g., 26.6% [24.6 to 28.8] to 86.7% [84.6% to 88.5%] in Africa in December 2021) and vaccination and infection in others (e.g., 9.6% [8.3% to 11.0%] in June 2020 to 95.9% [92.6% to 97.8%] in December 2021, in European high-income countries [HICs]). After the emergence of Omicron in March 2022, infection-induced seroprevalence rose to 47.9% [41.0% to 54.9%] in Europe HIC and 33.7% [31.6% to 36.0%] in Americas HIC. In 2021 Quarter Three (July to September), median seroprevalence to cumulative incidence ratios ranged from around 2:1 in the Americas and Europe HICs to over 100:1 in Africa (LMICs). Children 0 to 9 years and adults 60+ were at lower risk of seropositivity than adults 20 to 29 (p < 0.001 and p = 0.005, respectively). In a multivariable model using prevaccination data, stringent public health and social measures were associated with lower seroprevalence (p = 0.02). The main limitations of our methodology include that some estimates were driven by certain countries or populations being overrepresented.
Conclusions: In this study, we observed that global seroprevalence has risen considerably over time and with regional variation; however, over one-third of the global population are seronegative to the SARS-CoV-2 virus. Our estimates of infections based on seroprevalence far exceed reported Coronavirus Disease 2019 (COVID-19) cases. Quality and standardized seroprevalence studies are essential to inform COVID-19 response, particularly in resource-limited regions. |
Link[23] Importance of occupation for SARS-CoV-2 seroprevalence and COVID-19 vaccination among correctional workers in Quebec, Canada: A cross-sectional study
Author: Nadine Kronfli, Camille Dussault, Mathieu Maheu-Giroux, Alexandros Halavrezos, Sylvie Chalifoux, Hyejin Park, Lina Del Balso, Matthew P. Cheng, Joseph Cox Publication date: 9 November 2022 Publication info: Frontiers in Public Health, Volume 10 - 2022, 9 November 2022 Cited by: David Price 2:33 AM 9 December 2023 GMT Citerank: (3) 679844Mathieu Maheu-GirouxCanada Research Chair (Tier 2) in Population Health Modeling and Associate Professor, McGill University.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 715376Serosurveillance859FDEF6 URL: DOI: https://doi.org/10.3389/fpubh.2022.1021871
| Excerpt / Summary [Frontiers in Public Health, 9 November 2022]
Background: Correctional workers are at increased risk of SARS-CoV-2 infection. We examined the seroprevalence of SARS-CoV-2, determined the effects of carceral and occupational exposures on seropositivity, and explored predictors of COVID-19 vaccine uptake among correctional workers in Quebec, Canada.
Methods: We conducted a cross-sectional seroprevalence study in three provincial prisons. The primary and secondary outcomes were SARS-CoV-2 antibody seropositivity (Roche ElecsysÂź serology test) and self-reported COVID-19 vaccination status (âfully vaccinatedâ defined as two doses or prior infection plus one dose), respectively. Poisson regression models with robust standard error were used to examine the effect of occupational variables with SARS-CoV-2 seropositivity and predictors of COVID-19 vaccine uptake. Estimates are presented as crude and adjusted prevalence ratios (aPR) with 95% confidence intervals (95% CI).
Results: From 14 July to 15 November 2021, 105/600 (18%) correctional workers tested positive across three prisons (range 11â21%); 76% were fully vaccinated. Seropositivity was affected by prison occupation (aPR 1.59, 95% CI 1.11â2.27 for correctional officers vs. all other occupations) and low perceived concern of SARS-CoV-2 acquisition (aPR 1.62, 95% CI 1.11â2.38 for not/hardly worried vs. somewhat/extremely worried). Predictors of being fully vaccinated included race/ethnicity (aPR 0.86, 95% CI 0.76â0.99 for visible minority vs. White), presence of comorbidities (aPR 1.14, 95% CI 1.02â1.28 for > 2 vs. none), and prison occupation (aPR 0.82, 95% CI 0.73â0.92 for correctional officers vs. all other occupations).
Conclusions: Correctional officers were most likely to have acquired SARS-CoV-2, but least likely to be vaccinated, underscoring the importance of addressing both occupational risks and COVID-19 vaccine hesitancy to mitigate future outbreaks. |
Link[24] Targeted genomic sequencing with probe capture for discovery and surveillance of coronaviruses in bats
Author: Kevin S Kuchinski, Kara D Loos, Andrew DS Cameron, et al. - Danae M Suchan, Jennifer N Russell, Ashton N Sies, Charles Kumakamba, Francisca Muyembe, Placide Mbala Kingebeni, Ipos Ngay Lukusa, Frida NâKawa, Joseph Atibu Losoma, Maria Makuwa, Amethyst Gillis, Matthew LeBreton, James A Ayukekbong, Nicole A Lerminiaux, Corina Monagin, Damien O Joly, Karen Saylors, Nathan D Wolfe, Edward M Rubin, Jean J Muyembe Tamfum, Natalie A Prystajecky, David J McIver, Christian E Lange Publication date: 8 November 2022 Publication info: eLife 11:e79777 Cited by: David Price 2:34 AM 9 December 2023 GMT Citerank: (3) 679854Natalie Anne PrystajeckyNatalie Prystajecky is the program head for the Environmental Microbiology program at the BCCDC Public Health Laboratory. She is also a clinical associate professor in the Department of Pathology & Laboratory Medicine at UBC.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 708734Genomics859FDEF6 URL: DOI: https://doi.org/10.7554/eLife.79777
| Excerpt / Summary [eLife, 8 November 2022]
Public health emergencies like SARS, MERS, and COVID-19 have prioritized surveillance of zoonotic coronaviruses, resulting in extensive genomic characterization of coronavirus diversity in bats. Sequencing viral genomes directly from animal specimens remains a laboratory challenge, however, and most bat coronaviruses have been characterized solely by PCR amplification of small regions from the best-conserved gene. This has resulted in limited phylogenetic resolution and left viral genetic factors relevant to threat assessment undescribed. In this study, we evaluated whether a technique called hybridization probe capture can achieve more extensive genome recovery from surveillance specimens. Using a custom panel of 20,000 probes, we captured and sequenced coronavirus genomic material in 21 swab specimens collected from bats in the Democratic Republic of the Congo. For 15 of these specimens, probe capture recovered more genome sequence than had been previously generated with standard amplicon sequencing protocols, providing a median 6.1-fold improvement (ranging up to 69.1-fold). Probe capture data also identified five novel alpha- and betacoronaviruses in these specimens, and their full genomes were recovered with additional deep sequencing. Based on these experiences, we discuss how probe capture could be effectively operationalized alongside other sequencing technologies for high-throughput, genomics-based discovery and surveillance of bat coronaviruses. |
Link[25] Comparative Risk of Myocarditis/Pericarditis Following Second Doses of BNT162b2 and mRNA-1273 Coronavirus Vaccines
Author: Zaeema Naveed, Julia Li, James Wilton, Michelle Spencer, Monika Naus, HĂ©ctor A. VelĂĄsquez GarcĂa, Jeffrey C. Kwong, Caren Rose, Michael Otterstatter, Naveed Z. Janjua Publication date: 7 November 2023 Publication info: Journal of the American College of Cardiology, Volume 80, Issue 20, 2022, Pages 1900-1908, ISSN 0735-1097, Cited by: David Price 2:34 AM 9 December 2023 GMT Citerank: (3) 679856Naveed Zafar JanjuaDr. Naveed Zafar Janjua is an epidemiologist and senior scientist at the BC Centre for Disease Control and Clinical Associate Professor at School of Population and Public Health, University of British Columbia. Dr. Janjua is a Medical Doctor (MBBS) with a Masters of Science (MSc) degree in Epidemiology & Biostatistics and Doctorate in Public Health (DrPH). 10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1016/j.jacc.2022.08.799
| Excerpt / Summary [Journal of the American College of Cardiology, 7 November 2022]
Background: Postmarketing evaluations have linked myocarditis to COVID-19 mRNA vaccines. However, few population-based analyses have directly compared the safety of the 2 mRNA COVID-19 vaccines.
Objectives: This study aimed to compare the risk of myocarditis, pericarditis, and myopericarditis between BNT162b2 and mRNA-1273.
Methods: We used data from the British Columbia COVID-19 Cohort (BCC19C), a population-based cohort study. The exposure was the second dose of an mRNA vaccine. The outcome was diagnosis of myocarditis, pericarditis, or myopericarditis during a hospitalization or an emergency department visit within 21 days of the second vaccination dose. We performed multivariable logistic regression to assess the association between vaccine product and the outcomes of interest.
Results: The rates of myocarditis and pericarditis per million second doses were higher for mRNA-1273 (n = 31, rate 35.6; 95% CI: 24.1-50.5; and n = 20, rate 22.9; 95% CI: 14.0-35.4, respectively) than BNT162b2 (n = 28, rate 12.6; 95% CI: 8.4-18.2 and n = 21, rate 9.4; 95% CI: 5.8-14.4, respectively). mRNA-1273 vs BNT162b2 had significantly higher odds of myocarditis (adjusted OR [aOR]: 2.78; 95% CI: 1.67-4.62), pericarditis (aOR: 2.42; 95% CI: 1.31-4.46) and myopericarditis (aOR: 2.63; 95% CI: 1.76-3.93). The association between mRNA-1273 and myocarditis was stronger for men (aOR: 3.21; 95% CI: 1.77-5.83) and younger age group (18-39 years; aOR: 5.09; 95% CI: 2.68-9.66).
Conclusions: Myocarditis/pericarditis following mRNA COVID-19 vaccines is rare, but we observed a 2- to 3-fold higher odds among individuals who received mRNA-1273 vs BNT162b2. The rate of myocarditis following mRNA-1273 receipt is highest among younger men (age 18-39 years) and does not seem to be present at older ages. Our findings may have policy implications regarding the choice of vaccine offered. |
Link[26] Characterizing Longitudinal Antibody Responses in Recovered Individuals Following COVID-19 Infection and Single-Dose Vaccination: A Prospective Cohort Study
Author: Andrea D. Olmstead, Aidan M. Nikiforuk, Sydney Schwartz, Ana Citlali MĂĄrquez, Tahereh Valadbeigy, Eri Flores, Monika Saran, David M. Goldfarb, Althea Hayden, Shazia Masud, Shannon L. Russell, Natalie Prystajecky, Agatha N. Jassem, Muhammad Morshed, Inna Sekirov Publication date: 31 October 2022 Publication info: Viruses 2022, 14(11), 2416; Cited by: David Price 2:35 AM 9 December 2023 GMT Citerank: (3) 679854Natalie Anne PrystajeckyNatalie Prystajecky is the program head for the Environmental Microbiology program at the BCCDC Public Health Laboratory. She is also a clinical associate professor in the Department of Pathology & Laboratory Medicine at UBC.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.3390/v14112416
| Excerpt / Summary [Viruses, 31 October 2022]
Background: Investigating antibody titers in individuals who have been both naturally infected with SARS-CoV-2 and vaccinated can provide insight into antibody dynamics and correlates of protection over time.
Methods: Human coronavirus (HCoV) IgG antibodies were measured longitudinally in a prospective cohort of qPCR-confirmed, COVID-19 recovered individuals (k = 57) in British Columbia pre- and post-vaccination. SARS-CoV-2 and endemic HCoV antibodies were measured in serum collected between Nov. 2020 and Sept. 2021 (n = 341). Primary analysis used a linear mixed-effects model to understand the effect of single dose vaccination on antibody concentrations adjusting for biological sex, age, time from infection and vaccination. Secondary analysis investigated the cumulative incidence of high SARS-CoV-2 anti-spike IgG seroreactivity equal to or greater than 5.5 log10 AU/mL up to 105 days post-vaccination. No re-infections were detected in vaccinated participants, post-vaccination by qPCR performed on self-collected nasopharyngeal specimens.
Results: Bivariate analysis (complete data for 42 participants, 270 samples over 472 days) found SARS-CoV-2 spike and RBD antibodies increased 14â56 days post-vaccination (p < 0.001) and vaccination prevented waning (regression coefficient, B = 1.66 [95%CI: 1.45â3.46]); while decline of nucleocapsid antibodies over time was observed (regression coefficient, B = â0.24 [95%CI: â1.2-(â0.12)]). A positive association was found between COVID-19 vaccination and endemic human ÎČ-coronavirus IgG titer 14â56 days post vaccination (OC43, p = 0.02 & HKU1, p = 0.02). On average, SARS-CoV-2 anti-spike IgG concentration increased in participants who received one vaccine dose by 2.06 log10 AU/mL (95%CI: 1.45â3.46) adjusting for age, biological sex, and time since infection. Cumulative incidence of high SARS-CoV-2 spike antibodies (>5.5 log10 AU/mL) was 83% greater in vaccinated compared to unvaccinated individuals.
Conclusions: Our study confirms that vaccination post-SARS-CoV-2 infection provides multiple benefits, such as increasing anti-spike IgG titers and preventing decay up to 85 days post-vaccination. |
Link[27] COVID-19 cluster size and transmission rates in schools from crowdsourced case reports
Author: Paul Tupper, Shraddha Pai, COVID Schools Canada, Caroline Colijn Publication date: 30 November 2022 Publication info: eLife, 30 November 2022 Cited by: David Price 2:35 AM 9 December 2023 GMT Citerank: (4) 679761Caroline ColijnDr. Caroline Colijn works at the interface of mathematics, evolution, infection and public health, and leads the MAGPIE research group. She joined SFU's Mathematics Department in 2018 as a Canada 150 Research Chair in Mathematics for Infection, Evolution and Public Health. She has broad interests in applications of mathematics to questions in evolution and public health, and was a founding member of Imperial College London's Centre for the Mathematics of Precision Healthcare.10019D3ABAB, 679862Paul TupperProfessor in the Department of Mathematics at Simon Fraser University.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 715617Schools859FDEF6 URL: DOI: https://doi.org/10.7554/eLife.76174
| Excerpt / Summary [eLife, 30 November 2022]
The role of schools in the spread of SARS-CoV-2 is controversial, with some claiming they are an important driver of the pandemic and others arguing that transmission in schools is negligible. School cluster reports that have been collected in various jurisdictions are a source of data about transmission in schools. These reports consist of the name of a school, a date, and the number of students known to be infected. We provide a simple model for the frequency and size of clusters in this data, based on random arrivals of index cases at schools who then infect their classmates with a highly variable rate, fitting the overdispersion evident in the data. We fit our model to reports from four Canadian provinces, providing estimates of mean and dispersion for cluster size, as well as the distribution of the instantaneous transmission parameter ÎČ, whilst factoring in imperfect ascertainment. According to our model with parameters estimated from the data, in all four provinces (i) more than 65% of non-index cases occur in the 20% largest clusters, and (ii) reducing instantaneous transmission rate and the number of contacts a student has at any given time are effective in reducing the total number of cases, whereas strict bubbling (keeping contacts consistent over time) does not contribute much to reduce cluster sizes. We predict strict bubbling to be more valuable in scenarios with substantially higher transmission rates. |
Link[28] Timeliness of reporting of SARS-CoV-2 seroprevalence results and their utility for infectious disease surveillance
Author: Claire Donnici, Natasha Ilincic, Christian Cao, Caseng Zhang, Gabriel Deveaux, David Clifton, David Buckeridge, Niklas Bobrovitz, Rahul K. Arora Publication date: 26 October 2022 Publication info: Epidemics, Volume 41, 2022, 100645, ISSN 1755-4365 Cited by: David Price 2:36 AM 9 December 2023 GMT Citerank: (3) 679775David BuckeridgeDavid is a Professor in the School of Population and Global Health at McGill University, where he directs the Surveillance Lab, an interdisciplinary group that develops, implements, and evaluates novel computational methods for population health surveillance. He is also the Chief Digital Health Officer at the McGill University Health Center where he directs strategy on digital transformation and analytics and he is an Associate Member with the Montreal Institute for Learning Algorithms (Mila).10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 715376Serosurveillance859FDEF6 URL: DOI: https://doi.org/10.1016/j.epidem.2022.100645
| Excerpt / Summary [Epidemics, 26 October 2022]
Seroprevalence studies have been used throughout the COVID-19 pandemic to monitor infection and immunity. These studies are often reported in peer-reviewed journals, but the academic writing and publishing process can delay reporting and thereby public health action. Seroprevalence estimates have been reported faster in preprints and media, but with concerns about data quality. We aimed to (i) describe the timeliness of SARS-CoV-2 serosurveillance reporting by publication venue and study characteristics and (ii) identify relationships between timeliness, data validity, and representativeness to guide recommendations for serosurveillance efforts. We included seroprevalence studies published between January 1, 2020 and December 31, 2021 from the ongoing SeroTracker living systematic review. For each study, we calculated timeliness as the time elapsed between the end of sampling and the first public report. We evaluated data validity based on serological test performance and correction for sampling error, and representativeness based on the use of a representative sample frame and adequate sample coverage. We examined how timeliness varied with study characteristics, representativeness, and data validity using univariate and multivariate Cox regression. We analyzed 1844 studies. Median time to publication was 154 days (IQR 64â255), varying by publication venue (journal articles: 212 days, preprints: 101 days, institutional reports: 18 days, and media: 12 days). Multivariate analysis confirmed the relationship between timeliness and publication venue and showed that general population studies were published faster than special population or health care worker studies; there was no relationship between timeliness and study geographic scope, geographic region, representativeness, or serological test performance. Seroprevalence studies in peer-reviewed articles and preprints are published slowly, highlighting the limitations of using the academic literature to report seroprevalence during a health crisis. More timely reporting of seroprevalence estimates can improve their usefulness for surveillance, enabling more effective responses during health emergencies. |
Link[29] Incidence of SARS-CoV-2 Infection Among People Experiencing Homelessness in Toronto, Canada
Author: Lucie Richard, Rosane Nisenbaum, Michael Brown, Michael Liu, Cheryl Pedersen, Jesse I. R. Jenkinson, Sharmistha Mishra, Stefan Baral, Karen Colwill, Anne-Claude Gingras, Allison McGeer, Stephen W. Hwang Publication date: 13 March 2023 Publication info: JAMA Netw Open. 2023;6(3):e232774 Cited by: David Price 2:36 AM 9 December 2023 GMT Citerank: (3) 679880Sharmistha MishraSharmistha Mishra is an infectious disease physician and mathematical modeler and holds a Tier 2 Canadian Research Chair in Mathematical Modeling and Program Science.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 708809Homelessness859FDEF6 URL: DOI: https://doi.org/10.1001/jamanetworkopen.2023.2774
| Excerpt / Summary [JAMA Network Open, 13 March 2023]
Importance: People experiencing homelessness are at high risk of SARS-CoV-2 infection. Incident infection rates have yet to be established in these communities and are needed to inform infection prevention guidance and related interventions.
Objective: To quantify the SARS-CoV-2 incident infection rate among people experiencing homelessness in Toronto, Canada, in 2021 and 2022 and to assess factors associated with incident infection.
Design, Setting, and Participants: This prospective cohort study was conducted among individuals aged 16 years and older who were randomly selected between June and September 2021 from 61 homeless shelters, temporary distancing hotels, and encampments in Toronto, Canada.
Exposures: Self-reported housing characteristics, such as number sharing living space.
Main Outcomes and Measures: Prevalence of prior SARS-CoV-2 infection in summer 2021, defined as self-reported or polymerase chain reaction (PCR)â or serology-confirmed evidence of infection at or before the baseline interview, and SARS-CoV-2 incident infection, defined as self-reported or PCR- or serology-confirmed infection among participants without history of infection at baseline. Factors associated with infection were assessed using modified Poisson regression with generalized estimating equations.
Results: The 736 participants (415 of whom did not have SARS-CoV-2 infection at baseline and were included in the primary analysis) had a mean (SD) age of 46.1 (14.6) years; 486 (66.0%) self-identified as male. Of these, 224 (30.4% [95% CI, 27.4%-34.0%]) had a history of SARS-CoV-2 infection by summer 2021. Of the remaining 415 participants with follow-up, 124 experienced infection within 6 months, representing an incident infection rate of 29.9% (95% CI, 25.7%-34.4%), or 5.8% (95% CI, 4.8%-6.8%) per person-month. Report after onset of the SARS-CoV-2 Omicron variant was associated with incident infection, with an adjusted rate ratio (aRR) of 6.28 (95% CI, 3.94-9.99). Other factors associated with incident infection included recent immigration to Canada (aRR, 2.74 [95% CI, 1.64-4.58]) and alcohol consumption over the past interval (aRR, 1.67 [95% CI, 1.12-2.48]). Self-reported housing characteristics were not significantly associated with incident infection.
Conclusions and Relevance: In this longitudinal study of people experiencing homelessness in Toronto, SARS-CoV-2 incident infection rates were high in 2021 and 2022, particularly once the Omicron variant became dominant in the region. Increased focus on homelessness prevention is needed to more effectively and equitably protect these communities. |
Link[30] Cov2clusters: genomic clustering of SARS-CoV-2 sequences
Author: Benjamin Sobkowiak, Kimia Kamelian, James E. A. Zlosnik, John Tyson, Anders Gonçalves da Silva, Linda M. N. Hoang, Natalie Prystajecky, Caroline Colijn Publication date: 19 October 2022 Publication info: BMC Genomics volume 23, Article number: 710 (2022) Cited by: David Price 2:37 AM 9 December 2023 GMT Citerank: (4) 679761Caroline ColijnDr. Caroline Colijn works at the interface of mathematics, evolution, infection and public health, and leads the MAGPIE research group. She joined SFU's Mathematics Department in 2018 as a Canada 150 Research Chair in Mathematics for Infection, Evolution and Public Health. She has broad interests in applications of mathematics to questions in evolution and public health, and was a founding member of Imperial College London's Centre for the Mathematics of Precision Healthcare.10019D3ABAB, 679854Natalie Anne PrystajeckyNatalie Prystajecky is the program head for the Environmental Microbiology program at the BCCDC Public Health Laboratory. She is also a clinical associate professor in the Department of Pathology & Laboratory Medicine at UBC.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 708734Genomics859FDEF6 URL: DOI: https://doi.org/10.1186/s12864-022-08936-4
| Excerpt / Summary [BMC Genomics, 19 October 2022]
Background: The COVID-19 pandemic remains a global public health concern. Advances in sequencing technologies has allowed for high numbers of SARS-CoV-2 whole genome sequence (WGS) data and rapid sharing of sequences through global repositories to enable almost real-time genomic analysis of the pathogen. WGS data has been used previously to group genetically similar viral pathogens to reveal evidence of transmission, including methods that identify distinct clusters on a phylogenetic tree. Identifying clusters of linked cases can aid in the regional surveillance and management of the disease. In this study, we present a novel method for producing stable genomic clusters of SARS-CoV-2 cases, cov2clusters, and compare the accuracy and stability of our approach to previous methods used for phylogenetic clustering using real-world SARS-CoV-2 sequence data obtained from British Columbia, Canada.
Results: We found that cov2clusters produced more stable clusters than previously used phylogenetic clustering methods when adding sequence data through time, mimicking an increase in sequence data through the pandemic. Our method also showed high accuracy when predicting epidemiologically informed clusters from sequence data.
Conclusions: Our new approach allows for the identification of stable clusters of SARS-CoV-2 from WGS data. Producing high-resolution SARS-CoV-2 clusters from sequence data alone can a challenge and, where possible, both genomic and epidemiological data should be used in combination. |
Link[31] Coronavirus disease 2019 vaccine effectiveness among a population-based cohort of people living with HIV
Author: Catharine Chambers, Hasina Samji, Curtis Cooper, et al. Publication date: 1 December 2022 Publication info: AIDS 36(15):p F17-F26, December 1, 2022. Cited by: David Price 2:37 AM 9 December 2023 GMT Citerank: (2) 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 708761HIV859FDEF6 URL: DOI: https://doi.org/10.1097/QAD.0000000000003405
| Excerpt / Summary [AIDS, 1 December 2022]
Objective: People with HIV were underrepresented in coronavirus disease 2019 (COVID-19) vaccine clinical trials. We estimated vaccine effectiveness (VE) against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection for the BNT162b2, mRNA-1273, and ChAdOx1 vaccines among a population-based cohort of people with HIV in Ontario, Canada.
Design: Test-negative design
Methods: We identified people with HIV aged â„19 years who were tested for SARS-CoV-2 by RT-PCR between December 14, 2020 (first availability of COVID-19 vaccines) and November 21, 2021 (pre-Omicron circulation). Outcomes included any infection, symptomatic infection, and COVID-19-related hospitalization/death. We compared the odds of vaccination between test-positive cases and test-negative controls using multivariable logistic regression with adjustment for age, sex, region, calendar time, SARS-CoV-2 test histories, influenza vaccination, comorbidities, and neighborhood-level socio-economic status. VE was derived as (1 â adjusted odds ratio) Ă 100%.
Results: Among 21 023 adults living with HIV, there were 801 (8.3%) test-positive cases and 8,879 (91.7%) test-negative controls. 20.1% cases and 47.8% of controls received â„1 COVID-19 vaccine dose; among two-dose recipients, 93.4% received â„1 mRNA dose. Two-dose VE â„7 days before specimen collection was 82% (95% confidence interval [CI] = 74â87%) against any infection, 94% (95% CI = 82â98%) against symptomatic infection, and 97% (95% CI = 85â100%) against hospitalization/death. Against any infection, VE declined from 86% (95% CI = 77â92%) within 7â59 days after the second dose to 66% (95% CI = â15â90%) after â„180 days; we did not observe evidence of waning protection for other outcomes.
Conclusion: Two doses of COVID-19 vaccine offered substantial protection against symptomatic illness and hospitalization/death in people with HIV prior to the emergence of the Omicron variant. Our findings do not support a broad conclusion that COVID-19 VE is lower among people with HIV in populations that, for the most part, are attending HIV care, taking antiretroviral medication, and are virally suppressed. |
Link[32] Estimated Protection of Prior SARS-CoV-2 Infection Against Reinfection With the Omicron Variant Among Messenger RNAâVaccinated and Nonvaccinated Individuals in Quebec, Canada
Author: Sara Carazo, Danuta M. Skowronski, Marc Brisson, et al. Publication date: 14 October 2022 Publication info: JAMA Netw Open. 2022;5(10):e2236670. Cited by: David Price 2:38 AM 9 December 2023 GMT Citerank: (3) 679839Marc BrissonDr. Marc Brisson is full professor at Laval University where he leads the Research Group in Mathematical Modeling and Health Economics of Infectious Diseases.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1001/jamanetworkopen.2022.36670
| Excerpt / Summary [JAMA Network Open, 14 October 2022]
Importance: The Omicron variant is phylogenetically and antigenically distinct from earlier SARS-CoV-2 variants and the original vaccine strain. Protection conferred by prior SARS-CoV-2 infection against Omicron reinfection, with and without vaccination, requires quantification.
Objective: To estimate the protection against Omicron reinfection and hospitalization conferred by prior heterologous non-Omicron SARS-CoV-2 infection and/or up to 3 doses of an ancestral, Wuhan-like messenger RNA (mRNA) vaccine.
Design, Setting, and Participants: This test-negative, population-based case-control study was conducted between December 26, 2021, and March 12, 2022, and included community-dwelling individuals aged 12 years or older who were tested for SARS-CoV-2 infection in the province of Quebec, Canada.
Exposures: Prior laboratory-confirmed SARS-CoV-2 infection with or without mRNA vaccination.
Main Outcomes and Measures: The main outcome was laboratory-confirmed SARS-CoV-2 reinfection and associated hospitalization, presumed to be associated with the Omicron variant according to genomic surveillance. The odds of prior infection with or without vaccination were compared for case participants with Omicron infection and associated hospitalizations vs test-negative control participants. Estimated protection was derived as 1 â the odds ratio, adjusted for age, sex, testing indication, and epidemiologic week. Analyses were stratified by severity and time since last non-Omicron infection or vaccine dose.
Results: This study included 696 439 individuals (224 007 case participants and 472 432 control participants); 62.2% and 63.9% were female and 87.4% and 75.5% were aged 18 to 69 years, respectively. Prior non-Omicron SARS-CoV-2 infection was detected for 9505 case participants (4.2%) and 29âŻ712 control participants (6.3%). Among nonvaccinated individuals, prior non-Omicron infection was associated with a 44% reduction (95% CI, 38%-48%) in Omicron reinfection risk, which decreased from 66% (95% CI, 57%-73%) at 3 to 5 months to 35% (95% CI, 21%-47%) at 9 to 11 months postinfection and was below 30% thereafter. The more severe the prior infection, the greater the risk reduction. Estimated protection (95% CI) against Omicron infection was consistently significantly higher among vaccinated individuals with prior infection compared with vaccinated infection-naive individuals, with 65% (63%-67%) vs 20% (16%-24%) for 1 dose, 68% (67%-70%) vs 42% (41%-44%) for 2 doses, and 83% (81%-84%) vs 73% (72%-73%) for 3 doses. For individuals with prior infection, estimated protection (95% CI) against Omicron-associated hospitalization was 81% (66%-89%) and increased to 86% (77%-99%) with 1, 94% (91%-96%) with 2, and 97% (94%-99%) with 3 mRNA vaccine doses, without signs of waning.
Conclusions and Relevance: The findings of this study suggest that vaccination with 2 or 3 mRNA vaccine doses among individuals with prior heterologous SARS-CoV-2 infection provided the greatest protection against Omicron-associated hospitalization. In the context of program goals to prevent severe outcomes and preserve health care system capacity, a third mRNA vaccine dose may add limited protection in twice-vaccinated individuals with prior SARS-CoV-2 infection. |
Link[33] Colchicine and aspirin in community patients with COVID-19 (ACT): an open-label, factorial, randomised, controlled trial
Author: John W Eikelboom, Sanjit S Jolly, Emilie P Belley-Cote, Richard P Whitlock, et al. Publication date: 10 October 2022 Publication info: The Lancet Respiratory Medicine, December 2022, VOLUME 10, ISSUE 12, P1160-1168. Cited by: David Price 2:38 AM 9 December 2023 GMT Citerank: (3) 679843Mark LoebProfessor at Pathology and Molecular Medicine (primary), Clinical Epidemiology and Biostatistics in the Department of Pathology and Molecular Medicine at McMaster University. Associate Member, Medicine and Michael G. DeGroote Chair in Infectious Diseases.10019D3ABAB, 679843Mark LoebProfessor at Pathology and Molecular Medicine (primary), Clinical Epidemiology and Biostatistics in the Department of Pathology and Molecular Medicine at McMaster University. Associate Member, Medicine and Michael G. DeGroote Chair in Infectious Diseases.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0 URL: DOI: https://doi.org/10.1016/S2213-2600(22)00299-5
| Excerpt / Summary [The Lancet Respiratory Medicine, December 2022]
Background: The large number of patients worldwide infected with the SARS-CoV-2 virus has overwhelmed health-care systems globally. The Anti-Coronavirus Therapies (ACT) outpatient trial aimed to evaluate anti-inflammatory therapy with colchicine and antithrombotic therapy with aspirin for prevention of disease progression in community patients with COVID-19.
Methods: The ACT outpatient, open-label, 2âĂâ2 factorial, randomised, controlled trial, was done at 48 clinical sites in 11 countries. Patients in the community aged 30 years and older with symptomatic, laboratory confirmed COVID-19 who were within 7 days of diagnosis and at high risk of disease progression were randomly assigned (1:1) to receive colchicine 0·6 mg twice daily for 3 days and then 0·6 mg once daily for 25 days versus usual care, and in a second (1:1) randomisation to receive aspirin 100 mg once daily for 28 days versus usual care. Investigators and patients were not masked to treatment allocation. The primary outcome was assessed at 45 days in the intention-to-treat population; for the colchicine randomisation it was hospitalisation or death, and for the aspirin randomisation it was major thrombosis, hospitalisation, or death. The ACT outpatient trial is registered at ClinicalTrials.gov, NCT04324463 and is ongoing.
Findings: Between Aug 27, 2020, and Feb 10, 2022, 3917 patients were randomly assigned to colchicine or control and to aspirin or control; after excluding 36 patients due to administrative reasons 3881 individuals were included in the analysis (n=1939 colchicine vs n=1942 control; n=1945 aspirin vs 1936 control). Follow-up was more than 99% complete. Overall event rates were 5 (0·1%) of 3881 for major thrombosis, 123 (3·2%) of 3881 for hospitalisation, and 23 (0·6%) of 3881 for death; 66 (3·4%) of 1939 patients allocated to colchicine and 65 (3·3%) of 1942 patients allocated to control experienced hospitalisation or death (hazard ratio [HR] 1·02, 95% CI 0·72â1·43, p=0·93); and 59 (3·0%) of 1945 of patients allocated to aspirin and 73 (3·8%) of 1936 patients allocated to control experienced major thrombosis, hospitalisation, or death (HR 0·80, 95% CI 0·57â1·13, p=0·21). Results for the primary outcome were consistent in all prespecified subgroups, including according to baseline vaccination status, timing of randomisation in relation to onset of symptoms (post-hoc analysis), and timing of enrolment according to the phase of the pandemic (post-hoc analysis). There were more serious adverse events with colchicine than with control (34 patients [1·8%] of 1939 vs 27 [1·4%] of 1942) but none in either group that led to discontinuation of study interventions. There was no increase in serious adverse events with aspirin versus control (31 [1·6%] vs 31 [1·6%]) and none that led to discontinuation of study interventions.
Interpretation: The results provide no support for the use of colchicine or aspirin to prevent disease progression or death in outpatients with COVID-19. |
Link[34] Colchicine and the combination of rivaroxaban and aspirin in patients hospitalised with COVID-19 (ACT): an open-label, factorial, randomised, controlled trial
Author: John W Eikelboom, Sanjit S Jolly, Emilie P Belley-Cote, et al. Publication date: 10 October 2022 Publication info: The Lancet Respiratory Medicine, VOLUME 10, ISSUE 12, P1169-1177, DECEMBER 2022 Cited by: David Price 2:39 AM 9 December 2023 GMT Citerank: (1) 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0 URL: DOI: https://doi.org/10.1016/S2213-2600(22)00298-3
| Excerpt / Summary [The Lancet Respiratory Medicine, December 2022]
Background: COVID-19 disease is accompanied by a dysregulated immune response and hypercoagulability. The Anti-Coronavirus Therapies (ACT) inpatient trial aimed to evaluate anti-inflammatory therapy with colchicine and antithrombotic therapy with the combination of rivaroxaban and aspirin for prevention of disease progression in patients hospitalised with COVID-19.
Methods: The ACT inpatient, open-label, 2âĂâ2 factorial, randomised, controlled trial was done at 62 clinical centres in 11 countries. Patients aged at least 18 years with symptomatic, laboratory confirmed COVID-19 who were within 72 h of hospitalisation or worsening clinically if already hospitalised were randomly assigned (1:1) to receive colchicine 1·2 mg followed by 0·6 mg 2 h later and then 0·6 mg twice daily for 28 days versus usual care; and in a second (1:1) randomisation, to the combination of rivaroxaban 2·5 mg twice daily plus aspirin 100 mg once daily for 28 days versus usual care. Investigators and patients were not masked to treatment allocation. The primary outcome, assessed at 45 days in the intention-to-treat population, for the colchicine randomisation was the composite of the need for high-flow oxygen, mechanical ventilation, or death; and for the rivaroxaban plus aspirin randomisation was the composite of major thrombosis (myocardial infarction, stroke, acute limb ischaemia, or pulmonary embolism), the need for high-flow oxygen, mechanical ventilation, or death. The trial is registered at www.clinicaltrials.gov, NCT04324463 and is ongoing.
Findings: Between Oct 2, 2020, and Feb 10, 2022, at 62 sites in 11 countries, 2749 patients were randomly assigned to colchicine or control and the combination of rivaroxaban and aspirin or to the control. 2611 patients were included in the analysis of colchicine (n=1304) versus control (n=1307); 2119 patients were included in the analysis of rivaroxaban and aspirin (n=1063) versus control (n=1056). Follow-up was more than 98% complete. Overall, 368 (28·2%) of 1304 patients allocated to colchicine and 356 (27·2%) of 1307 allocated to control had a primary outcome (hazard ratio [HR] 1·04, 95% CI 0·90â1·21, p=0·58); and 281 (26·4%) of 1063 patients allocated to the combination of rivaroxaban and aspirin and 300 (28·4%) of 1056 allocated to control had a primary outcome (HR 0·92, 95% CI 0·78â1·09, p=0·32). Results were consistent in subgroups defined by vaccination status, disease severity at baseline, and timing of randomisation in relation to onset of symptoms. There was no increase in the number of patients who had at least one serious adverse event for colchicine versus control groups (87 [6·7%] of 1304 vs 90 [6·9%] of 1307) or with rivaroxaban and aspirin versus control groups (85 [8·0%] vs 91 [8·6%]). Among patients assigned to colchicine, 8 (0·61%) had adverse events that led to discontinuation of study drug, mostly gastrointestinal in nature. 17 (1·6%) patients assigned to the combination of rivaroxaban and aspirin had bleeding compared with seven (0·66%) of those allocated to control (p=0·042); the number of serious bleeding events was two (0·19%) versus six (0·57%), respectively (p=0·18). No patients assigned to rivaroxaban and aspirin had serious adverse events that led to discontinuation of study drug.
Interpretation: Among patients hospitalised with COVID-19, neither colchicine nor the combination of rivaroxaban and aspirin prevent disease progression or death. |
Link[35] COVID-19 Cases Among Congregate Care Facility Staff by Neighborhood of Residence and Social and Structural Determinants: Observational Study
Author: Huiting Ma, Kristy C Y Yiu, Sharmistha Mishra, et al. Publication date: 4 October 2022 Publication info: JMIR Public Health Surveill 2022;8(10):e34927 Cited by: David Price 2:39 AM 9 December 2023 GMT Citerank: (2) 679880Sharmistha MishraSharmistha Mishra is an infectious disease physician and mathematical modeler and holds a Tier 2 Canadian Research Chair in Mathematical Modeling and Program Science.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0 URL: DOI: https://doi.org/10.2196/34927
| Excerpt / Summary [JMIR Public Health and Surveillance, 4 October 2022]
Background: Disproportionate risks of COVID-19 in congregate care facilities including long-term care homes, retirement homes, and shelters both affect and are affected by SARS-CoV-2 infections among facility staff. In cities across Canada, there has been a consistent trend of geographic clustering of COVID-19 cases. However, there is limited information on how COVID-19 among facility staff reflects urban neighborhood disparities, particularly when stratified by the social and structural determinants of community-level transmission.
Objective: This study aimed to compare the concentration of cumulative cases by geography and social and structural determinants across 3 mutually exclusive subgroups in the Greater Toronto Area (population: 7.1 million): community, facility staff, and health care workers (HCWs) in other settings.
Methods: We conducted a retrospective, observational study using surveillance data on laboratory-confirmed COVID-19 cases (January 23 to December 13, 2020; prior to vaccination rollout). We derived neighborhood-level social and structural determinants from census data and generated Lorenz curves, Gini coefficients, and the Hoover index to visualize and quantify inequalities in cases.
Results: The hardest-hit neighborhoods (comprising 20% of the population) accounted for 53.87% (44,937/83,419) of community cases, 48.59% (2356/4849) of facility staff cases, and 42.34% (1669/3942) of other HCW cases. Compared with other HCWs, cases among facility staff reflected the distribution of community cases more closely. Cases among facility staff reflected greater social and structural inequalities (larger Gini coefficients) than those of other HCWs across all determinants. Facility staff cases were also more likely than community cases to be concentrated in lower-income neighborhoods (Gini 0.24, 95% CI 0.15-0.38 vs 0.14, 95% CI 0.08-0.21) with a higher household density (Gini 0.23, 95% CI 0.17-0.29 vs 0.17, 95% CI 0.12-0.22) and with a greater proportion working in other essential services (Gini 0.29, 95% CI 0.21-0.40 vs 0.22, 95% CI 0.17-0.28).
Conclusions: COVID-19 cases among facility staff largely reflect neighborhood-level heterogeneity and disparities, even more so than cases among other HCWs. The findings signal the importance of interventions prioritized and tailored to the home geographies of facility staff in addition to workplace measures, including prioritization and reach of vaccination at home (neighborhood level) and at work. |
Link[36] Older Adults Mount Less Durable Humoral Responses to Two Doses of COVID-19 mRNA Vaccine but Strong Initial Responses to a Third Dose
Author: Francis Mwimanzi, Hope R Lapointe, Peter K Cheung, et al. Publication date: 11 May 2022 Publication info: The Journal of Infectious Diseases, Volume 226, Issue 6, 15 September 2022, Pages 983â994, Cited by: David Price 2:39 AM 9 December 2023 GMT Citerank: (3) 679854Natalie Anne PrystajeckyNatalie Prystajecky is the program head for the Environmental Microbiology program at the BCCDC Public Health Laboratory. She is also a clinical associate professor in the Department of Pathology & Laboratory Medicine at UBC.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1093/infdis/jiac199
| Excerpt / Summary [The Journal of Infectious Diseases, 15 September 2022]
Background: Third coronavirus disease 2019 (COVID-19) vaccine doses are broadly recommended, but immunogenicity data remain limited, particularly in older adults.
Methods: We measured circulating antibodies against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein receptor-binding domain, ACE2 displacement, and virus neutralization against ancestral and omicron (BA.1) strains from prevaccine up to 1 month following the third dose, in 151 adults aged 24â98 years who received COVID-19 mRNA vaccines.
Results: Following 2 vaccine doses, humoral immunity was weaker, less functional, and less durable in older adults, where a higher number of chronic health conditions was a key correlate of weaker responses and poorer durability. One month after the third dose, antibody concentrations and function exceeded postâsecond-dose levels, and responses in older adults were comparable in magnitude to those in younger adults at this time. Humoral responses against omicron were universally weaker than against the ancestral strain after both the second and third doses. Nevertheless, after 3 doses, anti-omicron responses in older adults reached equivalence to those in younger adults. One month after 3 vaccine doses, the number of chronic health conditions, but not age, was the strongest consistent correlate of weaker humoral responses.
Conclusions: Results underscore the immune benefits of third COVID-19 vaccine doses, particularly in older adults. |
Link[37] Protection against omicron (B.1.1.529) BA.2 reinfection conferred by primary omicron BA.1 or pre-omicron SARS-CoV-2 infection among health-care workers with and without mRNA vaccination: a test-negative case-control study
Author: Sara Carazo, Danuta M Skowronski, Marc Brisson, et al. Publication date: 21 September 2022 Publication info: The Lancet Infectious Diseases, VOLUME 23, ISSUE 1, P45-55, JANUARY 2023 Cited by: David Price 2:40 AM 9 December 2023 GMT Citerank: (3) 679839Marc BrissonDr. Marc Brisson is full professor at Laval University where he leads the Research Group in Mathematical Modeling and Health Economics of Infectious Diseases.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1016/S1473-3099(22)00578-3
| Excerpt / Summary [The Lancet Infectious Diseases, 21 September 2022]
Background: There is a paucity of data on vaccine-induced or infection-induced (hybrid or natural) immunity against omicron (B.1.1.529) subvariant BA.2, particularly in comparing the effects of previous SARS-CoV-2 infection with the same or different genetic lineage. We aimed to estimate the protection against omicron BA.2 associated with previous primary infection with omicron BA.1 or pre-omicron SARS-CoV-2, among health-care workers with and without mRNA vaccination.
Methods: We conducted a test-negative case-control study among health-care workers aged 18 years or older who were tested for SARS-CoV-2 in Quebec, Canada, between March 27 and June 4, 2022, when BA.2 was the predominant variant and was presumptively diagnosed with a positive test result. We identified cases (positive test during study period) and controls (negative test during study period) using the provincial laboratory database that records all nucleic acid amplification testing for SARS-CoV-2 in Quebec, and used the provincial immunisation registry to determine vaccination status. Logistic regression models compared the likelihood of BA.2 infection or reinfection (second positive test â„30 days after primary infection) among health-care workers who had previous primary infection and none to three mRNA vaccine doses versus unvaccinated health-care workers with no primary infection.
Findings: 258â007 SARS-CoV-2 tests were done during the study period. Among those with a valid result and that met the inclusion criteria, there were 37â732 presumed BA.2 cases (2521 [6·7%] reinfections following pre-omicron primary infection and 659 [1·7%] reinfections following BA.1 primary infection) and 73â507 controls (7360 [10·0%] had pre-omicron primary infection and 12â315 [16·8%] had BA.1 primary infection). Pre-omicron primary infection was associated with a 38% (95% CI 19â53) reduction in BA.2 infection risk, with higher BA.2 protection among those who had also received one (56%, 95% CI 47â63), two (69%, 64â73), or three (70%, 66â74) mRNA vaccine doses. Omicron BA.1 primary infection was associated with greater protection against BA.2 infection (risk reduction of 72%, 95% CI 65â78), and protection was increased further among those who had received two doses of mRNA vaccine (96%, 95â96), but was not improved with a third dose (96%, 95â97).
Interpretation: Health-care workers who had received two doses of mRNA vaccine and had previous BA.1 infection were subsequently well protected for a prolonged period against BA.2 reinfection, with a third vaccine dose conferring no improvement to that hybrid protection. If this protection also pertains to future variants, there might be limited benefit from additional vaccine doses for people with hybrid immunity, depending on timing and variant. |
Link[38] Utilization of the Abbott SARS-CoV-2 IgG II Quant Assay To Identify High-Titer Anti-SARS-CoV-2 Neutralizing Plasma against Wild-Type and Variant SARS-CoV-2 Viruses
Author: Yi-Chan J. Lin, David H. Evans, Ninette F. Robbins, Guillermo Orjuela, et al. Publication date: 20 September 2022 Publication info: Clinical Microbiology, 20 September 2022 Cited by: David Price 2:40 AM 9 December 2023 GMT Citerank: (2) 679755Ashleigh TuiteAshleigh Tuite is an Assistant Professor in the Epidemiology Division at the Dalla Lana School of Public Health at the University of Toronto.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0 URL: DOI: https://doi.org/10.1128/spectrum.02811-22
| Excerpt / Summary [Clinical Microbiology, 20 September 2022]
There is evidence that COVID-19 convalescent plasma may improve outcomes of patients with impaired immune systems; however, more clinical trials are required. Although we have previously used a 50% plaque reduction/neutralization titer (PRNT50) assay to qualify convalescent plasma for clinical trials and virus-like particle (VLP) assays to validate PRNT50 methodologies, these approaches are time-consuming and expensive. Here, we characterized the ability of the Abbott severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) IgG II Quant assay to identify high- and low-titer plasma for wild-type and variant (Alpha, Beta, Gamma, and Delta) SARS-CoV-2 characterized by both VLP assays and PRNT50. Plasma specimens previously tested in wild-type, Alpha, Beta, Gamma, and Delta VLP neutralization assays were selected based on availability. Selected specimens were evaluated by the Abbott SARS-CoV-2 IgG II Quant assay [Abbott anti-Spike (S); Abbott, Chicago, IL], and values in units per milliliter were converted to binding antibody units (BAU) per milliliter. Sixty-three specimens were available for analysis. Abbott SARS-CoV-2 IgG II Quant assay values in BAU per milliliter were significantly different between high- and low-titer specimens for wild-type (Mann-Whitney U = 42, P < 0.0001), Alpha (Mann-Whitney U = 38, P < 0.0001), Beta (Mann-Whitney U = 29, P < 0.0001), Gamma (Mann-Whitney U = 0, P < 0.0001), and Delta (Mann-Whitney Uâ=â42, P < 0.0001). A conservative approach using the highest 95% confidence interval (CI) values from wild-type and variant of concern (VOC) SARS-CoV-2 experiments would identify a potential Abbott SARS-CoV-2 IgG II Quant assay cutoff of â„7.1âĂâ103 BAU/mL.
IMPORTANCE: The United States Food and Drug Administration (FDA) issued an Emergency Use Authorization (EUA) for the use of COVID-19 convalescent plasma (CCP) to treat hospitalized patients with COVID-19 in August 2020. However, by 4 February 2021, the FDA had revised the convalescent plasma EUA. This revision limited the authorization for high-titer COVID-19 convalescent plasma and restricted patient groups to hospitalized patients with COVID-19 early in their disease course or hospitalized patients with impaired humoral immunity. Traditionally our group utilized 50% plaque reduction/neutralization titer (PRNT50) assays to qualify CCP in Canada. Since that time, the Abbott SARS-CoV-2 IgG II Quant assay (Abbott, Chicago IL) was developed for the qualitative and quantitative determination of IgG against the SARS-CoV-2. Here, we characterized the ability of the Abbott SARS-CoV-2 IgG II Quant assay to identify high- and low-titer plasma for wild-type and variant (Alpha, Beta, Gamma, and Delta) SARS-CoV-2. |
Link[39] Pandemic fatigue or enduring precautionary behaviours? Canadiansâ long-term response to COVID-19 public health measures
Author: Gabrielle Brankston, Eric Merkley, Peter J. Loewen, Brent P. Avery, Carolee A. Carson, Brendan P. Dougherty, David N. Fisman, Ashleigh R. Tuite, Zvonimir Poljak, Amy L. Greer Publication date: 20 September 2022 Publication info: Preventive Medicine Reports, Volume 30, 2022, 101993, ISSN 2211-3355 Cited by: David Price 2:40 AM 9 December 2023 GMT Citerank: (5) 679751Amy GreerCanada Research Chair in Population Disease Modelling and an associate professor in the Department of Population Medicine, Ontario Veterinary College at the University of Guelph.10019D3ABAB, 679755Ashleigh TuiteAshleigh Tuite is an Assistant Professor in the Epidemiology Division at the Dalla Lana School of Public Health at the University of Toronto.10019D3ABAB, 679777David FismanI am a Professor in the Division of Epidemiology at Division of Epidemiology, Dalla Lana School of Public Health at the University of Toronto. I am a Full Member of the School of Graduate Studies. I also have cross-appointments at the Institute of Health Policy, Management and Evaluation and the Department of Medicine, Faculty of Medicine. I serve as a Consultant in Infectious Diseases at the University Health Network.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6 URL: DOI: https://doi.org/10.1016/j.pmedr.2022.101993
| Excerpt / Summary [Preventive Medicine Reports, December 2022]
The long-term dynamics of COVID-19 disease incidence and public health measures may impact individualsâ precautionary behaviours as well as support for measures. The objectives of this study were to assess longitudinal changes in precautionary behaviours and support for public health measures. Survey data were collected online from 1030 Canadians in each of 5 cycles in 2020: June 15-July 13; July 22-Aug 8; Sept 7â15; Oct 14â21; and Nov 12â17. Precautionary behaviour increased over the study period in the context of increasing disease incidence. When controlling for the stringency of public health measures and disease incidence, mixed effects logistic regression models showed these behaviours did not significantly change over time. Odds ratios for avoiding contact with family and friends ranged from 0.84 (95% CI 0.59â1.20) in September to 1.25 (95% CI 0.66â2.37) in November compared with July 2020. Odds ratios for attending an indoor gathering ranged from 0.86 (95% CI 0.62â1.20) in August to 1.71 (95% CI 0.95â3.09) in October compared with July 2020. Support for non-essential business closures increased over time with 2.33 (95% CI 1.14â4.75) times higher odds of support in November compared to July 2020. Support for school closures declined over time with lower odds of support in September (OR 0.66 [95% CI 0.45â0.96]), October (OR 0.48 [95% CI 0.26â0.87]), and November (OR 0.39 [95% CI 0.19â0.81]) compared with July 2020. In summary, respondentsâ behaviour mirrored government guidance between July and November 2020 and supported individual precautionary behaviour and limitations on non-essential businesses over school closures. |
Link[40] The complexity of examining laboratory-based biological markers associated with mortality in hospitalized patients during early phase of the COVID-19 pandemic: A systematic review and evidence map
Author: Lauren E. Griffith, Muhammad Usman Ali, Alessandra Andreacchi, Mark Loeb, Meghan Kenny, Divya Joshi, Vishal Mokashi, Ahmed Irshad, Angela K. Ulrich, Nicole E. Basta, Parminder Raina, Laura Anderson, Cynthia Balion Publication date: 9 September 2022 Publication info: PLoS ONE 17(9): e0273578. Cited by: David Price 2:42 AM 9 December 2023 GMT Citerank: (4) 679843Mark LoebProfessor at Pathology and Molecular Medicine (primary), Clinical Epidemiology and Biostatistics in the Department of Pathology and Molecular Medicine at McMaster University. Associate Member, Medicine and Michael G. DeGroote Chair in Infectious Diseases.10019D3ABAB, 685420Hospitals16289D5D4, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 715390Mortality859FDEF6 URL: DOI: https://doi.org/10.1371/journal.pone.0273578
| Excerpt / Summary [PLoS ONE, 9 September 2022]
Importance: The measurement of laboratory biomarkers plays a critical role in managing patients with COVID-19. However, to date most systematic reviews examining the association between laboratory biomarkers and mortality in hospitalized patients early in the pandemic focused on small sets of biomarkers, did not account for multiple studies including patients within the same institutions during overlapping timeframes, and did not include a significant number of studies conducted in countries other than China.
Objective: To provide a comprehensive summary and an evidence map examining the relationship between a wide range of laboratory biomarkers and mortality among patients hospitalized with COVID-19 during the early phase of the pandemic in multiple countries.
Evidence review: MEDLINE, EMBASE, and Web of Science were searched from Dec 2019 to March 9, 2021. A total of 14,049 studies were identified and screened independently by two raters; data was extracted by a single rater and verified by a second. Quality was assessed using the Joanna Briggs Institute (JBI) Case Series Critical Appraisal tool. To allow comparison across biomarkers, standardized mean differences (SMD) were used to quantify the relationship between laboratory biomarkers and hospital mortality. Meta-regression was conducted to account for clustering within institutions and countries.
Results: Our systematic review included 94 case-series studies from 30 countries. Across all biomarkers, the largest and most precise SMDs were observed for cardiac (troponin (1.03 (95% CI 0.86 to 1.21)), and BNP/NT-proBNP (0.93 (0.52 to 1.34)), inflammatory (IL-6 (0.97 (0.67 to 1.28) and Neutrophil-to-lymphocyte ratio (0.94 (0.59 to 1.29)), and renal biomarkers (blood urea nitrogen (1.01 (0.79 to 1.23)) and estimated glomerular filtration rate (-0.96 (-1.42 to -0.50)). There was heterogeneity for most biomarkers across countries with studies conducted in China generally having larger effect sizes.
Conclusions and relevance: The results of this study provide an early pandemic summary of the relationship between biomarkers and mortality in hospitalized patients. We found our estimated ESs were generally attenuated compared to previous systematic reviews which predominantly included studies conducted in China. Despite using sophisticated methodology to examine studies across countries, heterogeneity in reporting of case-series studies early in the pandemic limits clinical interpretability. |
Link[41] Serial infection with SARS-CoV-2 Omicron BA.1 and BA.2 following three-dose COVID-19 vaccination
Author: Hope R. Lapointe, Francis Mwimanzi, Peter K. Cheung, et al. Publication date: 6 September 2022 Publication info: Frontiers in Immunology, 6 September 2022, Volume 13 - 2022 Cited by: David Price 2:43 AM 9 December 2023 GMT Citerank: (3) 679854Natalie Anne PrystajeckyNatalie Prystajecky is the program head for the Environmental Microbiology program at the BCCDC Public Health Laboratory. She is also a clinical associate professor in the Department of Pathology & Laboratory Medicine at UBC.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.3389/fimmu.2022.947021
| Excerpt / Summary [Frontiers in Immunology, 6 September 2022]
SARS-CoV-2 Omicron infections are common among individuals who are vaccinated or have recovered from prior variant infection, but few reports have immunologically assessed serial Omicron infections. We characterized SARS-CoV-2 humoral responses in an individual who acquired laboratory-confirmed Omicron BA.1.15 ten weeks after a third dose of BNT162b2, and BA.2 thirteen weeks later. Responses were compared to 124 COVID-19-naive vaccinees. One month post-second and -third vaccine doses, the participantâs wild-type and BA.1-specific IgG, ACE2-displacement and virus neutralization activities were average for a COVID-19-naive triple-vaccinated individual. BA.1 infection boosted the participantâs responses to the cohort â„95th percentile, but even this strong âhybridâ immunity failed to protect against BA.2. Reinfection increased BA.1 and BA.2-specific responses only modestly. Though vaccines clearly protect against severe disease, results highlight the continued importance of maintaining additional protective measures to counteract the immune-evasive Omicron variant, particularly as vaccine-induced immune responses naturally decline over time. |
Link[42] Competing health risks associated with the COVID-19 pandemic and early response: A scoping review
Author: Stefan Baral, Amrita Rao, Jean Olivier Twahirwa Rwema, Carrie Lyons, Muge Cevik, Anna E. KĂ„gesten, Daouda Diouf, Annette H. Sohn, Refilwe Nancy Phaswana-Mafuya, Adeeba Kamarulzaman, Gregorio Millett, Julia L. Marcus, Sharmistha Mishra Publication date: 29 August 2022 Publication info: PLoS ONE 17(8): e0273389 Cited by: David Price 2:44 AM 9 December 2023 GMT Citerank: (2) 679880Sharmistha MishraSharmistha Mishra is an infectious disease physician and mathematical modeler and holds a Tier 2 Canadian Research Chair in Mathematical Modeling and Program Science.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0 URL: DOI: https://doi.org/10.1371/journal.pone.0273389
| Excerpt / Summary [PLoS ONE, 29 August 2022]
Background: COVID-19 has rapidly emerged as a global public health threat with infections recorded in nearly every country. Responses to COVID-19 have varied in intensity and breadth, but generally have included domestic and international travel limitations, closure of non-essential businesses, and repurposing of health services. While these interventions have focused on testing, treatment, and mitigation of COVID-19, there have been reports of interruptions to diagnostic, prevention, and treatment services for other public health threats.
Objectives: We conducted a scoping review to characterize the early impact of COVID-19 on HIV, tuberculosis, malaria, sexual and reproductive health, and malnutrition.
Methods: A scoping literature review was completed using searches of PubMed and preprint servers (medRxiv/bioRxiv) from November 1st, 2019 to October 31st, 2020, using Medical Subject Headings (MeSH) terms related to SARS-CoV-2 or COVID-19 and HIV, tuberculosis, malaria, sexual and reproductive health, and malnutrition. Empiric studies reporting original data collection or mathematical models were included, and available data synthesized by region. Studies were excluded if they were not written in English.
Results: A total of 1604 published papers and 205 preprints were retrieved in the search. Overall, 8.0% (129/1604) of published studies and 10.2% (21/205) of preprints met the inclusion criteria and were included in this review: 7.3% (68/931) on HIV, 7.1% (24/339) on tuberculosis, 11.6% (26/224) on malaria, 7.8% (19/183) on sexual and reproductive health, and 9.8% (13/132) on malnutrition. Thematic results were similar across competing health risks, with substantial indirect effects of the COVID-19 pandemic and response on diagnostic, prevention, and treatment services for HIV, tuberculosis, malaria, sexual and reproductive health, and malnutrition.
Discussion: COVID-19 emerged in the context of existing public health threats that result in millions of deaths every year. Thus, effectively responding to COVID-19 while minimizing the negative impacts of COVID-19 necessitates innovation and integration of existing programs that are often siloed across health systems. Inequities have been a consistent driver of existing health threats; COVID-19 has worsened disparities, reinforcing the need for programs that address structural risks. The data reviewed here suggest that effective strengthening of health systems should include investment and planning focused on ensuring the continuity of care for both rapidly emergent and existing public health threats. |
Link[43] Comparative Single-Dose mRNA and ChAdOx1 Vaccine Effectiveness Against Severe Acute Respiratory Syndrome Coronavirus 2, Including Variants of Concern: Test-Negative Design, British Columbia, Canada
Author: Danuta M Skowronski, Solmaz Setayeshgar, Macy Zou, Natalie Prystajecky, John R Tyson, Hind Sbihi, Chris D Fjell, Eleni Galanis, Monika Naus, David M Patrick, Shiraz El Adam, May A Ahmed, Shinhye Kim, Bonnie Henry, Linda M N Hoang, Manish Sadarangani, Agatha N Jassem, Mel Krajden Publication date: 27 January 2022 Publication info: The Journal of Infectious Diseases, Volume 226, Issue 3, 1 August 2022, Pages 485â496, Published: 27 January 2022 Cited by: David Price 2:44 AM 9 December 2023 GMT Citerank: (3) 679854Natalie Anne PrystajeckyNatalie Prystajecky is the program head for the Environmental Microbiology program at the BCCDC Public Health Laboratory. She is also a clinical associate professor in the Department of Pathology & Laboratory Medicine at UBC.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1093/infdis/jiac023
| Excerpt / Summary [The Journal of Infectious Diseases, 27 January 2022]
Background: In British Columbia, Canada, most adults 50â69 years old became eligible for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine in April 2021, with chimpanzee adenoviral vectored vaccine (ChAdOx1) restricted to â„55-year-olds and second doses deferred â„6 weeks to optimize single-dose coverage.
Methods: Among adults 50â69 years old, single-dose messenger RNA (mRNA) and ChAdOx1 vaccine effectiveness (VE) against SARS-CoV-2 infection and hospitalization, including variant-specific, was assessed by test-negative design between 4 April and 2 October 2021.
Results: Single-dose VE included 11â
861 cases and 99â
544 controls. Median of postvaccination follow-up was 32 days (interquartile range, 15â52 days). Alpha, Gamma, and Delta variants comprised 23%, 18%, and 56%, respectively, of genetically characterized viruses. At 21â55 days postvaccination, single-dose mRNA and ChAdOx1 VE (95% confidence interval [CI]) was 74% (71%â76%) and 59% (53%â65%) against any infection and 86% (80%â90%) and 94% (85%â97%) against hospitalization, respectively. VE (95% CI) was similar against Alpha and Gamma infections for mRNA (80% [76%â84%] and 80% [75%â84%], respectively) and ChAdOx1 (69% [60%â76%] and 66% [56%â73%], respectively). mRNA VE was lower at 63% (95% CI, 56%â69%) against Delta but 85% (95% CI, 71%â92%) against Delta-associated hospitalization (nonestimable for ChAdOx1).
Conclusions: A single mRNA or ChAdOx1 vaccine dose gave important protection against SARS-CoV-2, including early variants of concern. ChAdOx1 VE was lower against infection, but 1 dose of either vaccine reduced the hospitalization risk by >85% to at least 8 weeks postvaccination. Findings inform program options, including longer dosing intervals. |
Link[44] Single-Dose Messenger RNA Vaccine Effectiveness Against Severe Acute Respiratory Syndrome Coronavirus 2 in Healthcare Workers Extending 16 Weeks Postvaccination: A Test-Negative Design From Québec, Canada
Author: Sara Carazo, Denis Talbot, Nicole Boulianne, Marc Brisson, Rodica Gilca, GeneviĂšve Deceuninck, Nicholas Brousseau, MĂ©lanie Drolet, Manale Ouakki, Chantal Sauvageau, Sapha Barkati, Ălise Fortin, Alex Carignan, Philippe De Wals, Danuta M Skowronski, Gaston De Serres Publication date: 1 July 2022 Publication info: Clinical Infectious Diseases, Volume 75, Issue 1, 1 July 2022, Pages e805âe813, Cited by: David Price 2:45 AM 9 December 2023 GMT Citerank: (4) 679839Marc BrissonDr. Marc Brisson is full professor at Laval University where he leads the Research Group in Mathematical Modeling and Health Economics of Infectious Diseases.10019D3ABAB, 685420Hospitals16289D5D4, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1093/cid/ciab739
| Excerpt / Summary [Clinical Infectious Diseases, 1 July 2022]
Background: In Canada, first and second doses of messenger RNA (mRNA) vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were uniquely spaced 16 weeks apart. We estimated 1- and 2-dose mRNA vaccine effectiveness (VE) among healthcare workers (HCWs) in Québec, Canada, including protection against varying outcome severity, variants of concern (VOCs), and the stability of single-dose protection up to 16 weeks postvaccination.
Methods: A test-negative design compared vaccination among SARS-CoV-2 testâpositive and weekly matched (10:1), randomly sampled, test-negative HCWs using linked surveillance and immunization databases. Vaccine status was defined by 1 dose â„14 days or 2 doses â„7 days before illness onset or specimen collection. Adjusted VE was estimated by conditional logistic regression.
Results: Primary analysis included 5316 cases and 53 160 controls. Single-dose VE was 70% (95% confidence interval [CI], 68%â73%) against SARS-CoV-2 infection; 73% (95% CI, 71%â75%) against illness; and 97% (95% CI, 92%â99%) against hospitalization. Two-dose VE was 86% (95% CI, 81%â90%) and 93% (95% CI, 89%â95%), respectively, with no hospitalizations. VE was higher for non-VOCs than VOCs (73% Alpha) among single-dose recipients but not 2-dose recipients. Across 16 weeks, no decline in single-dose VE was observed, with appropriate stratification based upon prioritized vaccination determined by higher vs lower likelihood of direct patient contact.
Conclusions: One mRNA vaccine dose provided substantial and sustained protection to HCWs extending at least 4 months postvaccination. In circumstances of vaccine shortage, delaying the second dose may be a pertinent public health strategy. |
Link[45] Age-Specific Changes in Virulence Associated With Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Variants of Concern
Author: David N Fisman, Ashleigh R Tuite Publication date: 1 July 2022 Publication info: Clinical Infectious Diseases, Volume 75, Issue 1, 1 July 2022, Pages e69âe75, Cited by: David Price 2:46 AM 9 December 2023 GMT Citerank: (3) 679755Ashleigh TuiteAshleigh Tuite is an Assistant Professor in the Epidemiology Division at the Dalla Lana School of Public Health at the University of Toronto.10019D3ABAB, 679777David FismanI am a Professor in the Division of Epidemiology at Division of Epidemiology, Dalla Lana School of Public Health at the University of Toronto. I am a Full Member of the School of Graduate Studies. I also have cross-appointments at the Institute of Health Policy, Management and Evaluation and the Department of Medicine, Faculty of Medicine. I serve as a Consultant in Infectious Diseases at the University Health Network.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0 URL: DOI: https://doi.org/10.1093/cid/ciac174
| Excerpt / Summary [Clinical Infectious Diseases, 1 July 2022]
Background: Novel variants of concern (VOCs) have been associated with both increased infectivity and virulence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virulence of SARS-CoV-2 is closely linked to age. Whether relative increases in virulence of novel VOCs are similar across the age spectrum or are limited to some age groups is unknown.
Methods: We created a retrospective cohort of people in Ontario, Canada, who tested positive for SARS-CoV-2 and were screened for VOCs (nâ
=â
259 984) between 7 February 2021 and 31 October 2021. Cases were classified as N501Y-positive VOC, probable Delta VOC, or VOC undetected. We constructed age-specific logistic regression models to evaluate associations between N501Y-postive or Delta VOC infections and infection severity using hospitalization, intensive care unit (ICU) admission, and death as outcome variables. Models were adjusted for sex, comorbidity, vaccination status, and temporal trends.
Results: Infection with either N501Y-positive or Delta VOCs was associated with significant elevations in risk of hospitalization, ICU admission, and death across age groups compared with infections where a VOC was not detected. The Delta VOC increased hospitalization risk in children aged <10 years by a factor of 2.5 (adjusted odds ratio; 95% confidence interval, 1.3 to 5.0) compared with non-VOCs. There was a significant inverse relationship between age and relative increase in risk of death with the Delta VOC, with younger age groups showing a greater relative increase in risk of death than older individuals.
Conclusions: SARS-CoV-2 VOCs appear to be associated with increased relative virulence of infection in all age groups, though low absolute numbers of outcomes in younger individuals make estimates in these groups imprecise. |
Link[46] Seroprevalence and Risk Factors for Severe Acute Respiratory Syndrome Coronavirus 2 Among Incarcerated Adult Men in Quebec, Canada, 2021
Author: Nadine Kronfli, Camille Dussault, Mathieu Maheu-Giroux, Alexandros Halavrezos, Sylvie Chalifoux, Jessica Sherman, Hyejin Park, Lina Del Balso, Matthew P Cheng, SĂ©bastien Poulin, Joseph Cox Publication date: 1 July 2022 Publication info: Clinical Infectious Diseases, Volume 75, Issue 1, 1 July 2022, Pages e165âe173, Cited by: David Price 2:47 AM 9 December 2023 GMT Citerank: (6) 679755Ashleigh TuiteAshleigh Tuite is an Assistant Professor in the Epidemiology Division at the Dalla Lana School of Public Health at the University of Toronto.10019D3ABAB, 679777David FismanI am a Professor in the Division of Epidemiology at Division of Epidemiology, Dalla Lana School of Public Health at the University of Toronto. I am a Full Member of the School of Graduate Studies. I also have cross-appointments at the Institute of Health Policy, Management and Evaluation and the Department of Medicine, Faculty of Medicine. I serve as a Consultant in Infectious Diseases at the University Health Network.10019D3ABAB, 679844Mathieu Maheu-GirouxCanada Research Chair (Tier 2) in Population Health Modeling and Associate Professor, McGill University.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6, 715376Serosurveillance859FDEF6 URL: DOI: https://doi.org/10.1093/cid/ciac031
| Excerpt / Summary [Clinical Infectious Diseases, 1 July 2022]
Background: People in prison are at increased risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We examined the seroprevalence of SARS-CoV-2 and associated carceral risk factors among incarcerated adult men in Quebec, Canada.
Methods: We conducted a cross-sectional seroprevalence study in 2021 across 3 provincial prisons, representing 45% of Quebecâs incarcerated male provincial population. The primary outcome was SARS-CoV-2 antibody seropositivity (Roche Elecsys serology test). Participants completed self-administered questionnaires on sociodemographic, clinical, and carceral characteristics. The association of carceral variables with SARS-CoV-2 seropositivity was examined using Poisson regression models with robust standard errors. Crude and adjusted prevalence ratios (aPR) with 95% confidence intervals (95% CIs) were calculated.
Results: Between 19 January 2021 and 15 September 2021, 246 of 1100 (22%) recruited individuals tested positive across 3 prisons (range, 15%â27%). Seropositivity increased with time spent in prison since March 2020 (aPR, 2.17; 95% CI, 1.53â3.07 for âallâ vs âlittle timeâ), employment during incarceration (aPR, 1.64; 95% CI, 1.28â2.11 vs not), shared meal consumption during incarceration (âwith cellmatesâ: aPR, 1.46; 95% CI, 1.08â1.97 vs âaloneâ; âwith sectorâ: aPR, 1.34; 95% CI, 1.03â1.74 vs âaloneâ), and incarceration post-prison outbreak (aPR, 2.32; 95% CI, 1.69â3.18 vs âpre-outbreakâ).
Conclusions: The seroprevalence of SARS-CoV-2 among incarcerated individuals was high and varied among prisons. Several carceral factors were associated with seropositivity, underscoring the importance of decarceration and occupational safety measures, individual meal consumption, and enhanced infection prevention and control measures including vaccination during incarceration. |
Link[47] COVID-19 vaccine effectiveness by HIV status and history of injection drug use: a test-negative analysis
Author: Joseph H. Puyat, James Wilton, Adeleke Fowokan, Naveed Zafar Janjua, Jason Wong, Troy Grennan, Catharine Chambers, Abigail Kroch, Cecilia T. Costiniuk, Curtis L. Cooper, Darren Lauscher, Monte Strong, Ann N. Burchell, Aslam Anis, Hasina Samji, COVAXHIV Study Team Publication date: 26 October 2023 Publication info: Journal of the International AIDS Society, Volume 26, Issue 10 e26178 Cited by: David Price 5:23 PM 9 December 2023 GMT Citerank: (3) 679856Naveed Zafar JanjuaDr. Naveed Zafar Janjua is an epidemiologist and senior scientist at the BC Centre for Disease Control and Clinical Associate Professor at School of Population and Public Health, University of British Columbia. Dr. Janjua is a Medical Doctor (MBBS) with a Masters of Science (MSc) degree in Epidemiology & Biostatistics and Doctorate in Public Health (DrPH). 10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1002/jia2.26178
| Excerpt / Summary [Journal of the International AIDS Society, 26 October 2023]
Introduction: People living with HIV (PLWH) and/or who inject drugs may experience lower vaccine effectiveness (VE) against SARS-CoV-2 infection.
Methods: A validated algorithm was applied to population-based, linked administrative datasets in the British Columbia COVID-19 Cohort (BCC19C) to ascertain HIV status and create a population of PLWH and matched HIV-negative individuals. The study population was limited to individuals who received an RT-PCR laboratory test for SARS-CoV-2 between 15 December 2020 and 21 November 2021 in BC, Canada. Any history of injection drug use (IDU) was ascertained using a validated administrative algorithm. We used a test-negative study design (modified caseâcontrol analysis) and multivariable logistic regression to estimate adjusted VE by HIV status and history of IDU.
Results: Our analysis included 2700 PLWH and a matched population of 375,043 HIV-negative individuals, among whom there were 351 and 103,049 SARS-CoV-2 cases, respectively. The proportion of people with IDU history was much higher among PLWH compared to HIV-negative individuals (40.7% vs. 4.3%). Overall VE during the first 6 months after second dose was lower among PLWH with IDU history (65.8%, 95% CI = 43.5â79.3) than PLWH with no IDU history (80.3%, 95% CI = 62.7â89.6), and VE was particularly low at 4â6 months (42.4%, 95% CI = â17.8 to 71.8 with IDU history vs. 64.0%; 95% CI = 15.7â84.7 without), although confidence intervals were wide. In contrast, overall VE was 88.6% (95% CI = 88.2â89.0) in the matched HIV-negative population with no history of IDU and remained relatively high at 4â6 months after second dose (84.6%, 95% CI = 83.8â85.4). Despite different patterns of vaccine protection by HIV status and IDU history, peak estimates were similar (â„88%) across all populations.
Conclusions: PLWH with a history of IDU may experience lower VE against COVID-19 infection, although findings were limited by a small sample size. The lower VE at 4â6 months may have implications for booster dose prioritization for PLWH and people who inject drugs. The immunocompromising effect of HIV, substance use and/or co-occurring comorbidities may partly explain these findings. |
Link[48] Vaccine effectiveness against hospitalization among adolescent and pediatric SARS-CoV-2 cases between May 2021 and January 2022 in Ontario, Canada: A retrospective cohort study
Author: Alison E. Simmons, Afia Amoako, Alicia A. Grima, Kiera R. Murison, Sarah A. Buchan, David N. Fisman, Ashleigh R. Tuite Publication date: 31 March 2023 Publication info: PLoS ONE 18(3): e0283715. Cited by: David Price 1:48 AM 10 December 2023 GMT Citerank: (5) 679755Ashleigh TuiteAshleigh Tuite is an Assistant Professor in the Epidemiology Division at the Dalla Lana School of Public Health at the University of Toronto.10019D3ABAB, 679777David FismanI am a Professor in the Division of Epidemiology at Division of Epidemiology, Dalla Lana School of Public Health at the University of Toronto. I am a Full Member of the School of Graduate Studies. I also have cross-appointments at the Institute of Health Policy, Management and Evaluation and the Department of Medicine, Faculty of Medicine. I serve as a Consultant in Infectious Diseases at the University Health Network.10019D3ABAB, 685420Hospitals16289D5D4, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1371/journal.pone.0283715
| Excerpt / Summary [PLoS ONE, 31 March 2023]
Background: Vaccines against SARS-CoV-2 have been shown to reduce risk of infection as well as severe disease among those with breakthrough infection in adults. The latter effect is particularly important as immune evasion by Omicron variants appears to have made vaccines less effective at preventing infection. Therefore, we aimed to quantify the protection conferred by mRNA vaccination against hospitalization due to SARS-CoV-2 in adolescent and pediatric populations.
Methods: We retrospectively created a cohort of reported SARS-CoV-2 case records from Ontarioâs Public Health Case and Contact Management Solution among those aged 4 to 17 linked to vaccination records from the COVaxON database on January 19, 2022. We used multivariable logistic regression to estimate the association between vaccination and hospitalization among SARS-CoV-2 cases prior to and during the emergence of Omicron.
Results: We included 62 hospitalized and 27,674 non-hospitalized SARS-CoV-2 cases, with disease onset from May 28, 2021 to December 4, 2021 (Pre-Omicron) and from December 23, 2021 to January 9, 2022 (Omicron). Among adolescents, two mRNA vaccine doses were associated with an 85% (aOR = 0.15; 95% CI: [0.04, 0.53]; p<0.01) lower likelihood of hospitalization among SARS-CoV-2 cases caused by Omicron. Among children, one mRNA vaccine dose was associated with a 79% (aOR = 0.21; 95% CI: [0.03, 0.77]; p<0.05) lower likelihood of hospitalization among SARS-CoV-2 cases caused by Omicron. The calculation of E-values, which quantifies how strong an unmeasured confounder would need to be to nullify our findings, suggest that these effects are unlikely to be explained by unmeasured confounding.
Conclusions: Despite immune evasion by SARS-CoV-2 variants, vaccination continues to be associated with a lower likelihood of hospitalization among adolescent and pediatric Omicron (B.1.1.529) SARS-CoV-2 cases, even when the vaccines do not prevent infection. Continued efforts are needed to increase vaccine uptake among adolescent and pediatric populations. |
Link[49] Relative pandemic severity in Canada and four peer nations during the SARS-CoV-2 pandemic
Author: Amy Peng, Alison Simmons, Afia Amoako, Ashleigh Tuite, David Fisman Publication date: 31 May 2023 Publication info: CCDR: Volume 49-5, May 2023: Innovative Technologies in Public Health, 2023;49(5):197â205. Cited by: David Price 1:49 AM 10 December 2023 GMT Citerank: (4) 679755Ashleigh TuiteAshleigh Tuite is an Assistant Professor in the Epidemiology Division at the Dalla Lana School of Public Health at the University of Toronto.10019D3ABAB, 679777David FismanI am a Professor in the Division of Epidemiology at Division of Epidemiology, Dalla Lana School of Public Health at the University of Toronto. I am a Full Member of the School of Graduate Studies. I also have cross-appointments at the Institute of Health Policy, Management and Evaluation and the Department of Medicine, Faculty of Medicine. I serve as a Consultant in Infectious Diseases at the University Health Network.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 715390Mortality859FDEF6 URL: DOI: https://doi.org/10.14745/ccdr.v49i05a05
| Excerpt / Summary [Canada Communicable Disease Report, 31 May 2023]
Background: National responses to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic have been highly variable. We sought to explore the effectiveness of the Canadian pandemic response up to May 2022 relative to responses in four peer countries with similar political, economic and health systems, and with close historical and cultural ties to Canada.
Methods: We used reported age-specific mortality data to generate estimates of pandemic mortality standardized to the Canadian population. Age-specific case fatality, hospitalization, and intensive care admission probabilities for the Canadian province of Ontario were applied to estimated deaths, to calculate hospitalizations and intensive care admissions averted by the Canadian response. Health impacts were valued in both monetary terms, and in terms of lost quality-adjusted life years.
Results: We estimated that the Canadian pandemic response averted 94,492, 64,306 and 13,641 deaths relative to the responses of the United States, United Kingdom and France, respectively, and more than 480,000 hospitalizations relative to the United States. The United States pandemic response, if applied to Canada, would have resulted in more than $40 billion in economic losses due to healthcare expenditures and lost quality-adjusted life years. In contrast, an Australian pandemic response applied to Canada would have averted over 28,000 additional deaths and averted nearly $9 billion in costs.
Conclusion: Canada outperformed several peer countries that aimed for mitigation rather than elimination of SARS-CoV-2 in the first two years of the pandemic, with substantial numbers of lives saved and economic costs averted. However, a comparison with Australia demonstrated that an elimination focus would have saved Canada tens of thousands of lives as well as substantial economic costs. |
Link[50] Emergence of SARS-CoV-2 Delta Variant and Effect of Nonpharmaceutical Interventions, British Columbia, Canada
Author: Y.L. Elaine Chan, Michael A. Irvine, Natalie Prystajecky, Hind Sbihi, Marsha Taylor, Yayuk Joffres, Andrea Schertzer, Caren Rose, Louise Dyson, Edward M. Hill, Michael Tildesley, John R. Tyson, Linda M.N. Hoang, Eleni Galanis Publication date: 1 October 2023 Publication info: Emerging Infectious Diseases. 2023;29(10):1999-2007. Cited by: David Price 1:53 AM 10 December 2023 GMT Citerank: (3) 679854Natalie Anne PrystajeckyNatalie Prystajecky is the program head for the Environmental Microbiology program at the BCCDC Public Health Laboratory. She is also a clinical associate professor in the Department of Pathology & Laboratory Medicine at UBC.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6 URL: DOI: https://doi.org/10.3201/eid2910.230055.
| Excerpt / Summary [Emerging Infectious Diseases, October 2023]
In British Columbia, Canada, initial growth of the SARS-CoV-2 Delta variant was slower than that reported in other jurisdictions. Delta became the dominant variant (>50% prevalence) within â7â13 weeks of first detection in regions within the United Kingdom and United States. In British Columbia, it remained at <10% of weekly incident COVID-19 cases for 13 weeks after first detection on March 21, 2021, eventually reaching dominance after 17 weeks. We describe the growth of Delta variant cases in British Columbia during March 1âJune 30, 2021, and apply retrospective counterfactual modeling to examine factors for the initially low COVID-19 case rate after Delta introduction, such as vaccination coverage and nonpharmaceutical interventions. Growth of COVID-19 cases in the first 3 months after Delta emergence was likely limited in British Columbia because additional nonpharmaceutical interventions were implemented to reduce levels of contact at the end of March 2021, soon after variant emergence. |
Link[51] Protocol for a living evidence synthesis on variants of concern and COVID-19 vaccine effectiveness
Author: Nicole Shaver, Melanie Katz, Julian Little, et al. - Gideon Darko Asamoah, Lori-Ann Linkins, Wael Abdelkader, Andrew Beck, Alexandria Bennett, Sarah E Hughes, Maureen Smith, Mpho Begin, Doug Coyle, Thomas Piggott, Benjamin M. Kagina, Vivian Welch, Caroline Colijn, David J.D. Earn, Khaled El Emam, Jane Heffernan, Sheila F. O'Brien, Kumanan Wilson, Erin Collins, Tamara Navarro, Joseph Beyene, Isabelle Boutron, Dawn Bowdish, Curtis Cooper, Andrew Costa, Janet Curran, Lauren Griffith, Amy Hsu, Jeremy Grimshaw, Marc-AndrĂ© Langlois, Xiaoguang Li, Anne Pham-Huy, Parminder Raina, Michele Rubini, Lehana Thabane, Hui Wang, Lan Xu, Melissa Brouwers, Tanya Horsley, John Lavis, Alfonso Iorio Publication date: 16 September 2023 Publication info: Vaccine, Volume 41, Issue 43, 2023, Pages 6411-6418, ISSN 0264-410X. Cited by: David Price 1:53 AM 10 December 2023 GMT Citerank: (5) 679761Caroline ColijnDr. Caroline Colijn works at the interface of mathematics, evolution, infection and public health, and leads the MAGPIE research group. She joined SFU's Mathematics Department in 2018 as a Canada 150 Research Chair in Mathematics for Infection, Evolution and Public Health. She has broad interests in applications of mathematics to questions in evolution and public health, and was a founding member of Imperial College London's Centre for the Mathematics of Precision Healthcare.10019D3ABAB, 679776David EarnProfessor of Mathematics and Faculty of Science Research Chair in Mathematical Epidemiology at McMaster University.10019D3ABAB, 679806Jane HeffernanJane Heffernan is a professor of infectious disease modelling in the Mathematics & Statistics Department at York University. She is a co-director of the Canadian Centre for Disease Modelling, and she leads national and international networks in mathematical immunology and the modelling of waning and boosting immunity.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1016/j.vaccine.2023.09.012
| Excerpt / Summary [Vaccine, 16 September 2023]
Background: It is evident that COVID-19 will remain a public health concern in the coming years, largely driven by variants of concern (VOC). It is critical to continuously monitor vaccine effectiveness as new variants emerge and new vaccines and/or boosters are developed. Systematic surveillance of the scientific evidence base is necessary to inform public health action and identify key uncertainties. Evidence syntheses may also be used to populate models to fill in research gaps and help to prepare for future public health crises. This protocol outlines the rationale and methods for a living evidence synthesis of the effectiveness of COVID-19 vaccines in reducing the morbidity and mortality associated with, and transmission of, VOC of SARS-CoV-2.
Methods: Living evidence syntheses of vaccine effectiveness will be carried out over one year for (1) a range of potential outcomes in the index individual associated with VOC (pathogenesis); and (2) transmission of VOC. The literature search will be conducted up to May 2023. Observational and database-linkage primary studies will be included, as well as RCTs. Information sources include electronic databases (MEDLINE; Embase; Cochrane, L*OVE; the CNKI and Wangfang platforms), pre-print servers (medRxiv, BiorXiv), and online repositories of grey literature. Title and abstract and full-text screening will be performed by two reviewers using a liberal accelerated method. Data extraction and risk of bias assessment will be completed by one reviewer with verification of the assessment by a second reviewer. Results from included studies will be pooled via random effects meta-analysis when appropriate, or otherwise summarized narratively.
Discussion: Evidence generated from our living evidence synthesis will be used to inform policy making, modelling, and prioritization of future research on the effectiveness of COVID-19 vaccines against VOC. |
Link[52] COVID-19 Vaccineâs Speed to Market and Vaccine Hesitancy: A Cross-Sectional Survey Study
Author: Ally Memedovich, Brenlea Farkas, Aidan Hollis, Charleen Salmon, Jia Hu, Kate Zinszer, Tyler Williamson, Reed F. Beall Publication date: 1 August 2023 Publication info: Healthcare Policy 19(1) August 2023: 99-113. Cited by: David Price 1:54 AM 10 December 2023 GMT Citerank: (3) 679891Tyler WilliamsonTyler Williamson is the Director of the Centre for Health Informatics, formerly the Associate Director. In addition, he is an Associate Professor of Biostatistics in the Department of Community Health Sciences as well as the Director of the Health Data Science and Biostatistics Diploma Program at the University of Calgary.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.12927/hcpol.2023.27153
| Excerpt / Summary [Healthcare Policy, August 2023]
Background: This paper aims to assess the extent to which the COVID-19 vaccine's speed to market affected Canadian residents' decision to remain unvaccinated.
Method: A cross-sectional survey conducted in late 2021 asked participants whether they had received the vaccine and their reasons for abstaining.
Results: Of the 2,712 participants who completed the survey, 8.9% remained unvaccinated. Unvaccinated respondents who selected âThey made the vaccine too fastâ (59.8%), were significantly more likely to identify as white, believe that the COVID-19 pandemic was not serious and have an unvaccinated social circle.
Conclusion: Should the COVID-19 vaccine rapid regulatory process be expanded, more patients may refuse treatment than if traditional timelines are followed. |
Link[53] Background rates of adverse events of special interest for COVID-19 vaccines: A multinational Global Vaccine Data Network (GVDN) analysis
Author: A. Phillips, Y. Jiang, D. Walsh, N. Andrews, M. Artama, H. Clothier, L. Cullen, L. Deng, S. Escolano, A. Gentile, G. Gidding, N. Giglio, T. Junker, W. Huang, N. Janjua, J. Kwong, J. Li, S. Nasreen, M. Naus, Z. Naveed, A. Pillsbury, J. Stowe, T. Vo, J. Buttery, H. Petousis-Harris, S. Black, A. Hviid Publication date: 5 September 2023 Publication info: Vaccine, Volume 41, Issue 42, 2023, Pages 6227-6238, ISSN 0264-410X Cited by: David Price 1:55 AM 10 December 2023 GMT Citerank: (3) 679856Naveed Zafar JanjuaDr. Naveed Zafar Janjua is an epidemiologist and senior scientist at the BC Centre for Disease Control and Clinical Associate Professor at School of Population and Public Health, University of British Columbia. Dr. Janjua is a Medical Doctor (MBBS) with a Masters of Science (MSc) degree in Epidemiology & Biostatistics and Doctorate in Public Health (DrPH). 10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1016/j.vaccine.2023.08.079
| Excerpt / Summary [Vaccine, 5 September 2023]
Background: The Global COVID Vaccine Safety (GCoVS) project was established in 2021 under the multinational Global Vaccine Data Network (GVDN) consortium to facilitate the rapid assessment of the safety of newly introduced vaccines. This study analyzed data from GVDN member sites on the background incidence rates of conditions designated as adverse events of special interest (AESI) for COVID-19 vaccine safety monitoring.
Methods: Eleven GVDN global sites obtained data from national or regional healthcare databases using standardized methods. Incident events of 13 pre-defined AESI were included for a pre-pandemic period (2015â19) and the first pandemic year (2020). Background incidence rates (IR) and 95% confidence intervals (CI) were calculated for inpatient and emergency department encounters, stratified by age and sex, and compared between pre-pandemic and pandemic periods using incidence rate ratios.
Results: An estimated 197 million people contributed 1,189,652,926 person-years of follow-up time. Among inpatients in the pre-pandemic period (2015â19), generalized seizures were the most common neurological AESI (IR ranged from 22.15 [95% CI 19.01â25.65] to 278.82 [278.20â279.44] per 100,000 person-years); acute disseminated encephalomyelitis was the least common (<0.5 per 100,000 person-years at most sites). Pulmonary embolism was the most common thrombotic event (IR 45.34 [95% CI 44.85â45.84] to 93.77 [95% CI 93.46â94.08] per 100,000 person-years). The IR of myocarditis ranged from 1.60 [(95% CI 1.45â1.76) to 7.76 (95% CI 7.46â8.08) per 100,000 person-years. The IR of several AESI varied by site, healthcare setting, age and sex. The IR of some AESI were notably different in 2020 compared to 2015â19.
Conclusion: Background incidence of AESIs exhibited some variability across study sites and between pre-pandemic and pandemic periods. These findings will contribute to global vaccine safety surveillance and research. |
Link[54] Medium-term scenarios of COVID-19 as a function of immune uncertainties and chronic disease
Author: Chadi M. Saad-Roy, Sinead E. Morris, Rachel E. Baker, Jeremy Farrar, Andrea L. Graham, Simon A. Levin, Caroline E. Wagner, C. Jessica. E. Metcalf, Bryan T. Grenfell Publication date: 30 August 2023 Publication info: J. R. Soc. Interface.202023024720230247 Cited by: David Price 1:55 AM 10 December 2023 GMT Citerank: (4) 679762Caroline E WagnerCaroline Wagner is an Assistant Professor in the Department of the Bioengineering at McGill University.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704036Immunology859FDEF6, 728545Long COVIDPost-acute sequelae of COVID-19 (PASC).859FDEF6 URL: DOI: https://doi.org/10.1098/rsif.2023.0247
| Excerpt / Summary [Journal of the Royal Society of Interface, 30 August 2023]
As the SARS-CoV-2 trajectory continues, the longer-term immuno-epidemiology of COVID-19, the dynamics of Long COVID, and the impact of escape variants are important outstanding questions. We examine these remaining uncertainties with a simple modelling framework that accounts for multiple (antigenic) exposures via infection or vaccination. If immunity (to infection or Long COVID) accumulates rapidly with the valency of exposure, we find that infection levels and the burden of Long COVID are markedly reduced in the medium term. More pessimistic assumptions on host adaptive immune responses illustrate that the longer-term burden of COVID-19 may be elevated for years to come. However, we also find that these outcomes could be mitigated by the eventual introduction of a vaccine eliciting robust (i.e. durable, transmission-blocking and/or âevolution-proofâ) immunity. Overall, our work stresses the wide range of future scenarios that still remain, the importance of collecting real-world epidemiological data to identify likely outcomes, and the crucial need for the development of a highly effective transmission-blocking, durable and broadly protective vaccine. |
Link[55] Comparison of socio-economic determinants of COVID-19 testing and positivity in Canada: A multi-provincial analysis
Author: Lilia Antonova, Chandy Somayaji, Jillian Cameron, Monica Sirski, Maria E. Sundaram, James Ted McDonald, Sharmistha Mishra, Jeffrey C. Kwong, Alan Katz,Stefan Baral, Lisa Caulley, Andrew Calzavara, Martin Corsten, Stephanie Johnson-Obaseki Publication date: 23 August 2023 Publication info: PLoS ONE 18(8): e0289292. Cited by: David Price 1:57 AM 10 December 2023 GMT Citerank: (4) 679880Sharmistha MishraSharmistha Mishra is an infectious disease physician and mathematical modeler and holds a Tier 2 Canadian Research Chair in Mathematical Modeling and Program Science.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 703966Social determinants859FDEF6, 715831Diagnostic testing859FDEF6 URL: DOI: https://doi.org/10.1371/journal.pone.0289292
| Excerpt / Summary [PLoS ONE, 23 August 23, 2023]
Background: The effects of the COVID-19 pandemic have been more pronounced for socially disadvantaged populations. We sought to determine how access to SARS-CoV-2 testing and the likelihood of testing positive for COVID-19 were associated with demographic factors, socioeconomic status (SES) and social determinants of health (SDH) in three Canadian provinces.
Methods: An observational population-based cross-sectional study was conducted for the provinces of Ontario, Manitoba and New Brunswick between March 1, 2020 and April 27, 2021, using provincial health administrative data. After excluding residents of long-term care homes, those without current provincial health insurance and those who were tested for COVID-19 out of province, records from provincial healthcare administrative databases were reviewed for 16,900,661 healthcare users. Data was modelled separately for each province in accordance to a prespecified protocol and follow-up consultations among provincial statisticians and collaborators. We employed univariate and multivariate regression models to examine determinants of testing and test results.
Results: After adjustment for other variables, female sex and urban residency were positively associated with testing, while female sex was negatively associated with test positivity. In New Brunswick and Ontario, individuals living in higher income areas were more likely to be tested, whereas in Manitoba higher income was negatively associated with both testing and positivity. High ethnocultural composition was associated with lower testing rates. Both high ethnocultural composition and high situational vulnerability increased the odds of testing positive for SARS-CoV-2.
Discussion: We observed that multiple demographic, income and SDH factors were associated with SARS-CoV-2 testing and test positivity. Barriers to healthcare access identified in this study specifically relate to COVID-19 testing but may reflect broader inequities for certain at-risk groups. |
Link[56] The evolution of SARS-CoV-2 seroprevalence in Canada: a time-series study, 2020â2023
Author: Tanya J. Murphy, Hanna Swail, Jaspreet Jain, David L. Buckeridge, et al. - Maureen Anderson, Philip Awadalla, Lesley Behl, Patrick E. Brown, Carmen L. Charlton, Karen Colwill, Steven J. Drews, Anne-Claude Gingras, Deena Hinshaw, Prabhat Jha, Jamil N. Kanji, Victoria A. Kirsh, Amanda L.S. Lang, Marc-AndrĂ© Langlois, Stephen Lee, Antoine Lewin, Sheila F. OâBrien, Chantale Pambrun, Kimberly Skead, David A. Stephens, Derek R. Stein, Graham Tipples, Paul G. Van Caeseele, Timothy G. Evans, Olivia Oxlade, Bruce D. Mazer Publication date: 14 August 2023 Publication info: CMAJ August 14, 2023 195 (31) E1030-E1037 Cited by: David Price 1:58 AM 10 December 2023 GMT Citerank: (3) 679775David BuckeridgeDavid is a Professor in the School of Population and Global Health at McGill University, where he directs the Surveillance Lab, an interdisciplinary group that develops, implements, and evaluates novel computational methods for population health surveillance. He is also the Chief Digital Health Officer at the McGill University Health Center where he directs strategy on digital transformation and analytics and he is an Associate Member with the Montreal Institute for Learning Algorithms (Mila).10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 715376Serosurveillance859FDEF6 URL: DOI: https://doi.org/10.1503/cmaj.230249
| Excerpt / Summary [CMAJ, 14 August 2023]
Background: During the first year of the COVID-19 pandemic, the proportion of reported cases of COVID-19 among Canadians was under 6%. Although high vaccine coverage was achieved in Canada by fall 2021, the Omicron variant caused unprecedented numbers of infections, overwhelming testing capacity and making it difficult to quantify the trajectory of population immunity.
Methods: Using a time-series approach and data from more than 900 000 samples collected by 7 research studies collaborating with the COVID-19 Immunity Task Force (CITF), we estimated trends in SARS-CoV-2 seroprevalence owing to infection and vaccination for the Canadian population over 3 intervals: prevaccination (March to November 2020), vaccine roll-out (December 2020 to November 2021), and the arrival of the Omicron variant (December 2021 to March 2023). We also estimated seroprevalence by geographical region and age.
Results: By November 2021, 9.0% (95% credible interval [CrI] 7.3%â11%) of people in Canada had humoral immunity to SARS-CoV-2 from an infection. Seroprevalence increased rapidly after the arrival of the Omicron variant â by Mar. 15, 2023, 76% (95% CrI 74%â79%) of the population had detectable antibodies from infections. The rapid rise in infection-induced antibodies occurred across Canada and was most pronounced in younger age groups and in the Western provinces: Manitoba, Saskatchewan, Alberta and British Columbia.
Interpretation: Data up to March 2023 indicate that most people in Canada had acquired antibodies against SARS-CoV-2 through natural infection and vaccination. However, given variations in population seropositivity by age and geography, the potential for waning antibody levels, and new variants that may escape immunity, public health policy and clinical decisions should be tailored to local patterns of population immunity.
The COVID-19 pandemic defied expectations about immunity arising from infection and vaccination. During the first months of the pandemic, despite the burden on Canadian society and health systems, rates of symptomatic infection remained low, with 580 000 confirmed cases by December 2020, representing 1.6% of the Canadian population.1 Vaccines were widely distributed in Canada beginning in early 2021, with a rapid rise in vaccine coverage to 79% by fall of 2021,2 whereas cumulative reported cases of COVID-19 remained low, at 4.7% of the population.3 The arrival of Omicron variants and subvariants, however, caused an unprecedented increase in the number of infections. In short, the high vaccine coverage, combined with population immunity from infections in earlier waves of the pandemic, were insufficient to slow the spread of the Omicron variant.
Although the overall progression of confirmed cases and vaccination is clear, the underlying dynamics of population seropositivity are less obvious, yet critically important for policy and clinical decisions about vaccination and other preventive measures. A count of confirmed cases of COVID-19 is of limited use for understanding the evolution of population immunity because case ascertainment is biased by multiple factors. Most notably, access to laboratory-based polymerase chain reaction (PCR) testing varied across the country and, in many locations, was overwhelmed by demand after December 2021. In this context, serological surveillance provides an informative adjunct to monitoring confirmed cases, as seroprevalence offers a more direct measure of population humoral immunity.
We sought to describe the trajectory of SARS-CoV-2 seroprevalence in the Canadian population, as measured by anti-nucleocapsid (anti-N) and anti-spike protein (anti-S) antibody levels over 3 intervals: prevaccination (March to November 2020), vaccine roll-out (December 2020 to November 2021), and the Omicron variant waves (December 2021 to March 2023). We draw on seroprevalence estimates from multiple studies collaborating with the COVID-19 Immunity Task Force (CITF).4 In addition to describing the temporal evolution of population seropositivity in Canada, we highlight trends in infection-acquired and vaccine-induced seroprevalence by Canadian region and age.
|
Link[57] Coronavirus Disease 2019 Vaccination Is Associated With Reduced Outpatient Antibiotic Prescribing in Older Adults With Confirmed Severe Acute Respiratory Syndrome Coronavirus 2: A Population-Wide Cohort Study
Author: Derek R MacFadden, Colleen Maxwell, Dawn Bowdish, Susan Bronskill, James Brooks, Kevin Brown, Lori L Burrows, Anna Clarke, Bradley Langford, Elizabeth Leung, Valerie Leung, Doug Manuel, Allison McGeer, Sharmistha Mishra, Andrew M Morris, Caroline Nott, Sumit Raybardhan, Mia Sapin, Kevin L Schwartz, Miranda So, Jean-Paul R Soucy, Nick Daneman Publication date: 31 March 2023 Publication info: Clinical Infectious Diseases, Volume 77, Issue 3, 1 August 2023, Pages 362â370, Cited by: David Price 1:59 AM 10 December 2023 GMT Citerank: (4) 679856Naveed Zafar JanjuaDr. Naveed Zafar Janjua is an epidemiologist and senior scientist at the BC Centre for Disease Control and Clinical Associate Professor at School of Population and Public Health, University of British Columbia. Dr. Janjua is a Medical Doctor (MBBS) with a Masters of Science (MSc) degree in Epidemiology & Biostatistics and Doctorate in Public Health (DrPH). 10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704017Antimicrobial resistance859FDEF6, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1093/cid/ciad190
| Excerpt / Summary [Clinical Infectious Diseases, 1 August 2023]
Background: Antibiotics are frequently prescribed unnecessarily in outpatients with coronavirus disease 2019 (COVID-19). We sought to evaluate factors associated with antibiotic prescribing in outpatients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.
Methods: We performed a population-wide cohort study of outpatients aged â„66 years with polymerase chain reactionâconfirmed SARS-CoV-2 from 1 January 2020 to 31 December 2021 in Ontario, Canada. We determined rates of antibiotic prescribing within 1 week before (prediagnosis) and 1 week after (postdiagnosis) reporting of the positive SARS-CoV-2 result, compared to a self-controlled period (baseline). We evaluated predictors of prescribing, including a primary-series COVID-19 vaccination, in univariate and multivariable analyses.
Results: We identified 13 529 eligible nursing home residents and 50 885 eligible community-dwelling adults with SARS-CoV-2 infection. Of the nursing home and community residents, 3020 (22%) and 6372 (13%), respectively, received at least 1 antibiotic prescription within 1 week of a SARS-CoV-2 positive result. Antibiotic prescribing in nursing home and community residents occurred, respectively, at 15.0 and 10.5 prescriptions per 1000 person-days prediagnosis and 20.9 and 9.8 per 1000 person-days postdiagnosis, higher than the baseline rates of 4.3 and 2.5 prescriptions per 1000 person-days. COVID-19 vaccination was associated with reduced prescribing in nursing home and community residents, with adjusted postdiagnosis incidence rate ratios (95% confidence interval) of 0.7 (0.4â1) and 0.3 (0.3â0.4), respectively.
Conclusions: Antibiotic prescribing was high and with little or no decline following SARS-CoV-2 diagnosis but was reduced in COVID-19âvaccinated individuals, highlighting the importance of vaccination and antibiotic stewardship in older adults with COVID-19. |
Link[58] Genomic epidemiology offers high resolution estimates of serial intervals for COVID-19
Author: Jessica E. Stockdale, Kurnia Susvitasari, Paul Tupper, Benjamin Sobkowiak, Nicola Mulberry, Anders Gonçalves da Silva, Anne E. Watt, Norelle L. Sherry, Corinna Minko, Benjamin P. Howden, Courtney R. Lane, Caroline Colijn Publication date: 10 August 2023 Publication info: Nature Communications, Volume 14, Article number: 4830 (2023 Cited by: David Price 2:07 AM 10 December 2023 GMT Citerank: (3) 679761Caroline ColijnDr. Caroline Colijn works at the interface of mathematics, evolution, infection and public health, and leads the MAGPIE research group. She joined SFU's Mathematics Department in 2018 as a Canada 150 Research Chair in Mathematics for Infection, Evolution and Public Health. She has broad interests in applications of mathematics to questions in evolution and public health, and was a founding member of Imperial College London's Centre for the Mathematics of Precision Healthcare.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 708734Genomics859FDEF6 URL: DOI: https://doi.org/10.1038/s41467-023-40544-y
| Excerpt / Summary [Nature Communications, 10 August 2023]
Serial intervals â the time between symptom onset in infector and infectee â are a fundamental quantity in infectious disease control. However, their estimation requires knowledge of individualsâ exposures, typically obtained through resource-intensive contact tracing efforts. We introduce an alternate framework using virus sequences to inform who infected whom and thereby estimate serial intervals. We apply our technique to SARS-CoV-2 sequences from case clusters in the first two COVID-19 waves in Victoria, Australia. We find that our approach offers high resolution, cluster-specific serial interval estimates that are comparable with those obtained from contact data, despite requiring no knowledge of who infected whom and relying on incompletely-sampled data. Compared to a published serial interval, cluster-specific serial intervals can vary estimates of the effective reproduction number by a factor of 2â3. We find that serial interval estimates in settings such as schools and meat processing/packing plants are shorter than those in healthcare facilities. |
Link[59] A proportional incidence rate model for aggregated data to study the vaccine effectiveness against COVID-19 hospital and ICU admissions
Author: Ping Yan, Muhammad Abu Shadeque Mullah, Ashleigh Tuite Publication date: 10 August 2023 Publication info: Biometrics, 10 August 2023 Cited by: David Price 2:10 AM 10 December 2023 GMT Citerank: (4) 679755Ashleigh TuiteAshleigh Tuite is an Assistant Professor in the Epidemiology Division at the Dalla Lana School of Public Health at the University of Toronto.10019D3ABAB, 685420Hospitals16289D5D4, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1111/biom.13915
| Excerpt / Summary [Biometrics, 10 August 2023]
We develop a proportional incidence model that estimates vaccine effectiveness (VE) at the population level using conditional likelihood for aggregated data. Our model assumes that the population counts of clinical outcomes for an infectious disease arise from a superposition of Poisson processes with different vaccination statuses. The intensity function in the model is calculated as the product of per capita incidence rate and the at-risk population size, both of which are time-dependent. We formulate a log-linear regression model with respect to the relative risk, defined as the ratio between the per capita incidence rates of vaccinated and unvaccinated individuals. In the regression analysis, we treat the baseline incidence rate as a nuisance parameter, similar to the Cox proportional hazard model in survival analysis. We then apply the proposed models and methods to age-stratified weekly counts of COVID-19ârelated hospital and ICU admissions among adults in Ontario, Canada. The data spanned from 2021 to February 2022, encompassing the Omicron era and the rollout of booster vaccine doses. We also discuss the limitations and confounding effects while advocating for the necessity of more comprehensive and up-to-date individual-level data that document the clinical outcomes and measure potential confounders. |
Link[60] Equity issues rarely addressed in the development of COVID-19 formal recommendations and good practice statements: a cross-sectional study
Author: Omar Dewidar, Mostafa Bondok, Mark Loeb, Peter Tugwell, et al. - Leenah Abdelrazeq, Khadija Aliyeva, Karla Solo, Vivian Welch, Romina Brignardello-Petersen, Joseph L. Mathew, Glen Hazlewood, Kevin Pottie, Lisa Hartling, Dina Sami Khalifa, Stephanie Duda, Maicon Falavigna, Joanne Khabsa, Tamara Lotfi, Jennifer Petkovic, Sarah Elliot, Yuan Chi, Roses Parker, Elizabeth Kristjansson, Alison Riddle, Andrea J. Darzi, Olivia Magwood, Ammar Saad, Gabriel Rada, Ignacio Neumann, Ludovic Reveiz
Dominik Mertz, Thomas Piggott, Alexis F. Turgeon, Holger SchĂŒnemann Publication date: 8 August 2023 Publication info: Special Issue: Methodological Considerations Related To Equity, Diversity, And Inclusion In Clinical Epidemiology, Volume 161, P116-126, September 2023 Cited by: David Price 2:20 AM 10 December 2023 GMT Citerank: (3) 679843Mark LoebProfessor at Pathology and Molecular Medicine (primary), Clinical Epidemiology and Biostatistics in the Department of Pathology and Molecular Medicine at McMaster University. Associate Member, Medicine and Michael G. DeGroote Chair in Infectious Diseases.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 703965Equity859FDEF6 URL: DOI: https://doi.org/10.1016/j.jclinepi.2023.08.002
| Excerpt / Summary [Journal of Clinical Epidemiology, 8 August 2023]
Background and Objective: To identify COVID-19 actionable statements (e.g., recommendations) focused on specific disadvantaged populations in the living map of COVID-19 recommendations (eCOVIDRecMap) and describe how health equity was assessed in the development of the formal recommendations.
Methods: We employed the place of residence, race or ethnicity or culture, occupation, gender or sex, religion, education, socio-economic status, and social capital-Plus framework to identify statements focused on specific disadvantaged populations. We assessed health equity considerations in the evidence to decision frameworks (EtD) of formal recommendations for certainty of evidence and impact on health equity criteria according to the Grading of Recommendations, Assessment, Development, and Evaluations criteria.
Results: We identified 16% (124/758) formal recommendations and 24% (186/819) good practice statements (GPS) that were focused on specific disadvantaged populations. Formal recommendations (40%, 50/124) and GPS (25%, 47/186) most frequently focused on children. Seventy-six percent (94/124) of the recommendations were accompanied with EtDs. Over half (55%, 52/94) of those considered indirectness of the evidence for disadvantaged populations. Considerations in impact on health equity criterion most frequently involved implementation of the recommendation for disadvantaged populations (17%, 16/94).
Conclusion: Equity issues were rarely explicitly considered in the development COVID-19 formal recommendations focused on specific disadvantaged populations. Guidance is needed to support the consideration of health equity in guideline development during health emergencies. |
Link[61] Campus node-based wastewater surveillance enables COVID-19 case localization and confirms lower SARS-CoV-2 burden relative to the surrounding community
Author: Jangwoo Lee, Nicole Acosta, Barbara J. Waddell, Tyler Williamson, Michael D. Parkins, et al. - Kristine Du, Kevin Xiang, Jennifer Van Doorn, Kashtin Low, Maria A. Bautista, Janine McCalder, Xiaotian Dai, Xuewen Lu, Thierry Chekouo, Puja Pradhan, Navid Sedaghat, Chloe Papparis, Alexander Buchner Beaudet, Jianwei Chen, Leslie Chan, Laura Vivas, Paul Westlund, Srijak Bhatnagar, September Stefani, Gail Visser, Jason Cabaj, Stefania Bertazzon, Shahrzad Sarabi, Gopal Achari, Rhonda G. Clark, Steve E. Hrudey, Bonita E. Lee, Xiaoli Pang, Brendan Webster, William Amin Ghali, Andre Gerald Buret, Danielle A. Southern, Jon Meddings, Kevin Frankowski, Casey R.J. Hubert Publication date: 8 August 2023 Publication info: Water Research, Volume 244, 2023, 120469, ISSN 0043-1354, Cited by: David Price 2:26 AM 10 December 2023 GMT Citerank: (4) 679891Tyler WilliamsonTyler Williamson is the Director of the Centre for Health Informatics, formerly the Associate Director. In addition, he is an Associate Professor of Biostatistics in the Department of Community Health Sciences as well as the Director of the Health Data Science and Biostatistics Diploma Program at the University of Calgary.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704022Surveillance859FDEF6, 715617Schools859FDEF6 URL: DOI: https://doi.org/10.1016/j.watres.2023.120469
| Excerpt / Summary [Water Research, 8 August 2023]
Wastewater-based surveillance (WBS) has been established as a powerful tool that can guide health policy at multiple levels of government. However, this approach has not been well assessed at more granular scales, including large work sites such as University campuses. Between August 2021 and April 2022, we explored the occurrence of SARS-CoV-2 RNA in wastewater using qPCR assays from multiple complimentary sewer catchments and residential buildings spanning the University of Calgary's campus and how this compared to levels from the municipal wastewater treatment plant servicing the campus. Real-time contact tracing data was used to evaluate an association between wastewater SARS-CoV-2 burden and clinically confirmed cases and to assess the potential of WBS as a tool for disease monitoring across worksites. Concentrations of wastewater SARS-CoV-2 N1 and N2 RNA varied significantly across six sampling sites â regardless of several normalization strategies â with certain catchments consistently demonstrating values 1â2 orders higher than the others. Relative to clinical cases identified in specific sewersheds, WBS provided one-week leading indicator. Additionally, our comprehensive monitoring strategy enabled an estimation of the total burden of SARS-CoV-2 for the campus per capita, which was significantly lower than the surrounding community (pâ€0.001). Allele-specific qPCR assays confirmed that variants across campus were representative of the community at large, and at no time did emerging variants first debut on campus. This study demonstrates how WBS can be efficiently applied to locate hotspots of disease activity at a very granular scale, and predict disease burden across large, complex worksites. |
Link[62] Testing Whether Higher Contact Among the Vaccinated Can Be a Mechanism for Observed Negative Vaccine Effectiveness
Author: Korryn Bodner, Jesse Knight, Mackenzie A Hamilton, Sharmistha Mishra Publication date: 9 March 2023 Publication info: American Journal of Epidemiology, Volume 192, Issue 8, August 2023, Pages 1335â1340, Cited by: David Price 6:55 PM 10 December 2023 GMT Citerank: (3) 679880Sharmistha MishraSharmistha Mishra is an infectious disease physician and mathematical modeler and holds a Tier 2 Canadian Research Chair in Mathematical Modeling and Program Science.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1093/aje/kwad055
| Excerpt / Summary [American Journal of Epidemiology, 9 March 2023]
Evidence from early observational studies suggested negative vaccine effectiveness (â VEff) for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant. Since true VEff is unlikely to be negative, we explored how differences in contact among vaccinated persons (e.g., potentially from the implementation of vaccine mandates) could lead to observed negative VEff. Using a susceptible-exposed-infectious-recovered (SEIR) transmission model, we examined how vaccinated-contact heterogeneity, defined as an increase in the contact rate only between vaccinated individuals, interacted with 2 mechanisms of vaccine efficacy: vaccine efficacy against susceptibility (â VES) and vaccine efficacy against infectiousness (â VEI), to produce underestimated and in some cases, negative measurements of VEff. We found that vaccinated-contact heterogeneity led to negative estimates when VEI, and especially VESâ , were low. Moreover, we determined that when contact heterogeneity was very high, VEff could still be underestimated given relatively high vaccine efficacies (0.7), although its effect on VEff was strongly reduced. We also found that this contact heterogeneity mechanism generated a signature temporal pattern: The largest underestimates and negative measurements of VEff occurred during epidemic growth. Overall, our research illustrates how vaccinated-contact heterogeneity could have feasibly produced negative measurements during the Omicron period and highlights its general ability to bias observational studies of VEffâ . |
Link[63] The utility of SARS-CoV-2 genomic data for informative clustering under different epidemiological scenarios and sampling
Author: Benjamin Sobkowiak, Pouya Haghmaram, Natalie Prystajecky, James E.A. Zlosnik, John Tyson, Linda M.N. Hoang, Caroline Colijn Publication date: 2 August 2023 Publication info: Infection, Genetics and Evolution, Volume 113, 2023, 105484, ISSN 1567-1348, Cited by: David Price 7:05 PM 10 December 2023 GMT Citerank: (4) 679761Caroline ColijnDr. Caroline Colijn works at the interface of mathematics, evolution, infection and public health, and leads the MAGPIE research group. She joined SFU's Mathematics Department in 2018 as a Canada 150 Research Chair in Mathematics for Infection, Evolution and Public Health. She has broad interests in applications of mathematics to questions in evolution and public health, and was a founding member of Imperial College London's Centre for the Mathematics of Precision Healthcare.10019D3ABAB, 679854Natalie Anne PrystajeckyNatalie Prystajecky is the program head for the Environmental Microbiology program at the BCCDC Public Health Laboratory. She is also a clinical associate professor in the Department of Pathology & Laboratory Medicine at UBC.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 708734Genomics859FDEF6 URL: DOI: https://doi.org/10.1016/j.meegid.2023.105484
| Excerpt / Summary [Infection, Genetics and Evolution, 2 August 2023]
Objectives: Clustering pathogen sequence data is a common practice in epidemiology to gain insights into the genetic diversity and evolutionary relationships among pathogens. We can find groups of cases with a shared transmission history and common origin, as well as identifying transmission hotspots. Motivated by the experience of clustering SARS-CoV-2 cases using whole genome sequence data during the COVID-19 pandemic to aid with public health investigation, we investigated how differences in epidemiology and sampling can influence the composition of clusters that are identified.
Methods: We performed genomic clustering on simulated SARS-CoV-2 outbreaks produced with different transmission rates and levels of genomic diversity, along with varying the proportion of cases sampled.
Results: In single outbreaks with a low transmission rate, decreasing the sampling fraction resulted in multiple, separate clusters being identified where intermediate cases in transmission chains are missed. Outbreaks simulated with a high transmission rate were more robust to changes in the sampling fraction and largely resulted in a single cluster that included all sampled outbreak cases. When considering multiple outbreaks in a sampled jurisdiction seeded by different introductions, low genomic diversity between introduced cases caused outbreaks to be merged into large clusters. If the transmission and sampling fraction, and diversity between introductions was low, a combination of the spurious break-up of outbreaks and the linking of closely related cases in different outbreaks resulted in clusters that may appear informative, but these did not reflect the true underlying population structure. Conversely, genomic clusters matched the true population structure when there was relatively high diversity between introductions and a high transmission rate.
Conclusion: Differences in epidemiology and sampling can impact our ability to identify genomic clusters that describe the underlying population structure. These findings can help to guide recommendations for the use of pathogen clustering in public health investigations. |
Link[64] How Canadaâs decentralised covid-19 response affected public health data and decision making
Author: Tania Bubela, Colleen M Flood, Kimberlyn McGrail, Sharon E Straus, Sharmistha Mishra Publication date: 24 July 2023 Publication info: BMJ 2023;382:e075665 Cited by: David Price 7:07 PM 10 December 2023 GMT Citerank: (2) 679880Sharmistha MishraSharmistha Mishra is an infectious disease physician and mathematical modeler and holds a Tier 2 Canadian Research Chair in Mathematical Modeling and Program Science.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0 URL: DOI: https://doi.org/10.1136/bmj-2023-075665
| Excerpt / Summary [BMJ, 24 July 2023]
Canadaâs public health system was reformed after its 2003 severe acute respiratory syndrome (SARS) outbreak, which was the worst outside of Asia with 438 cases and 44 deaths. Ensuing national and provincial inquiries led to the creation of the national Public Health Agency of Canada (PHAC) to coordinate Canadaâs preparation for and response to public health threats. Subnational public health agencies were also created or strengthened to function as regional centres for disease control. These actions should have put Canada in a good position to respond to the covid-19 pandemic.
Despite these reforms, Canada experienced serious failures during the covid-19 pandemic. Memories faded rapidly after SARS, and if history is not to repeat itself, government and health system leaders must strengthen the countryâs public health and healthcare systems in preparation for the next threat. Health authorities as well as all Canadians need to reflect on the crises of the past three yearsâwhat went well and why; what caused pandemic response failures, and what were their consequences? Here, in the first of a series of articles examining Canadaâs response and setting out suggestions for a national inquiry, we examine the limitations of the countryâs decentralised structure for public health decision making and missed lessons from the 2003 SARS-CoV-1 outbreak, particularly with regard to data infrastructure.5 Other articles in the series examine how research and data failed to inform public health responses tailored to community and population needs, the predictable failures in long term care, and Canadaâs role in global vaccine inequity⊠|
Link[65] Use and misuse of research: Canadaâs response to covid-19 and its health inequalities
Author: Sharmistha Mishra, Jennifer D Walker, Linda Wilhelm, Vincent LariviĂšre, Tania Bubela, Sharon E Straus Publication date: 24 July 2023 Publication info: BMJ 2023;382:e075666 Cited by: David Price 7:10 PM 10 December 2023 GMT Citerank: (3) 679880Sharmistha MishraSharmistha Mishra is an infectious disease physician and mathematical modeler and holds a Tier 2 Canadian Research Chair in Mathematical Modeling and Program Science.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 703965Equity859FDEF6 URL: DOI: https://doi.org/10.1136/bmj-2023-075666
| Excerpt / Summary [BMJ, 24 July 2023]
Canada had one of the lowest rates of covid-19 cases and deaths per population than most in the G10 group of industrialised countries. But overall rates ignore underlying health inequalitiesâa consistent feature of the covid-19 pandemic across countries, within and outside the G10. Across every G10 country, for example, economic marginalisation was associated with twofold to fourfold higher rates of covid deaths.
Disproportionate risks of exposures and transmissions are shaped by physical and social networks: how, under what context, and with whom infectious disease contacts take place. The same context that governs these networks often defines what happens after infection occurs: access to and quality of care and treatment within a healthcare system that is built with the same tools as the social and economic system that failed to mitigate disproportionate risks. Yet early in the pandemic, Canada, like most countries, largely applied public health measures universally across its decentralised public health system with little focus on how measures and strategies would, or would not, reach and apply to those most at risk. |
Link[66] Effectiveness of previous infection-induced and vaccine-induced protection against hospitalisation due to omicron BA subvariants in older adults: a test-negative, case-control study in Quebec, Canada
Author: Sara Carazo, Danuta M Skowronski, Marc Brisson, Chantal Sauvageau, Nicholas Brousseau, Judith Fafard, Rodica Gilca, Denis Talbot, Manale Ouakki, Yossi Febriani, GeneviĂšve Deceuninck, Philippe De Wals, Gaston De Serres Publication date: 14 July 2023 Publication info: The Lancet Healthy Longevity, VOLUME 4, ISSUE 8, E409-E420, AUGUST 2023 Cited by: David Price 7:18 PM 10 December 2023 GMT Citerank: (4) 679839Marc BrissonDr. Marc Brisson is full professor at Laval University where he leads the Research Group in Mathematical Modeling and Health Economics of Infectious Diseases.10019D3ABAB, 685420Hospitals16289D5D4, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1016/S2666-7568(23)00099-5
| Excerpt / Summary [The Lancet Healthy Longevity, 14 July 2023]
Background: Older adults (aged â„60 years) were prioritised for COVID-19 booster vaccination due to severe outcome risk, but the risk for this group is also affected by previous SARS-CoV-2 infection and vaccination. We estimated vaccine effectiveness against omicron-associated hospitalisation in older adults by previously documented infection, time since last immunological event, and age group.
Methods: This was a population-based test-negative case-control study done in Quebec, Canada, during BA.1 dominant (December, 2021, to March, 2022), BA.2 dominant (April to June, 2022), and BA.4/5 dominant (July to November, 2022) periods using provincial laboratory, immunisation, hospitalisation, and chronic disease surveillance databases. We included older adults (aged â„60 years) with symptoms associated with COVID-19 who were tested for SARS-CoV-2 in acute-care hospitals. Cases were defined as patients who were hospitalised for COVID-19 within 14 days after testing positive; controls were patients who tested negative. Analyses spanned 3â14 months after last vaccine dose or previous infection. Logistic regression models compared COVID-19 hospitalisation risk by mRNA vaccine dose and previous infection versus unvaccinated and infection-naive participants.
Findings: Between Dec 26, 2021, and Nov 5, 2022, we included 174â819 specimens (82â870 [47·4%] from men and 91â949 [52·6%] from women; from 8455 cases and 166â364 controls), taken from 2951 cases and 48â724 controls in the BA.1 period; 1897 cases and 41â702 controls in the BA.2 period; and 3607 cases and 75â938 controls in the BA.4/5 period. In participants who were infection naive, vaccine effectiveness against hospitalisation improved with dose number, consistent with a shorter median time since last dose, but decreased with more recent omicron subvariants. Four-dose vaccine effectiveness was 96% (95% CI 93â98) during the BA.1 period, 84% (81â87) during the BA.2 period, and 68% (63â72) during the BA.4/5 period. Regardless of dose number (two to five doses) or timing since previous infection, hybrid protection was more than 90%, persisted for at least 6â8 months, and did not decline with age.
Interpretation: Older adults with both previous SARS-CoV-2 infection and two or more vaccine doses appear to be well protected for a prolonged period against hospitalisation due to omicron subvariants, including BA.4/5. Ensuring that older adults who are infection naive remain up to date with vaccination might reduce COVID-19 hospitalisations most efficiently. |
Link[67] Selection for immune evasion in SARS-CoV-2 revealed by high-resolution epitope mapping and sequence analysis
Author: Arnaud NâGuessan, Senthilkumar Kailasam, Fatima Mostefai, RaphaĂ«l Poujol, Jean-Christophe Grenier, Nailya Ismailova, Paola Contini, Raffaele De Palma, Carsten Haber, Volker Stadler, Guillaume Bourque, Julie G. Hussin, B. Jesse Shapiro, Jörg H. Fritz, Ciriaco A. Piccirillo Publication date: 13 July 2023 Publication info: iScience, VOLUME 26, ISSUE 8, 107394, AUGUST 18, 2023 Cited by: David Price 7:31 PM 10 December 2023 GMT Citerank: (3) 679756Jesse ShapiroJesse Shapiro is an Associate Professor in the Faculty of Medicine and Health Sciences at McGill University. Jesseâs research uses genomics to understand the ecology and evolution of microbes, ranging from freshwater bacterioplankton to the human gut microbiome. His work has helped elucidate the origins of bacterial species, leading to a more unified species concept across domains of life, and has developed genome-wide association study (GWAS) methods tailored for bacteria.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704036Immunology859FDEF6 URL: DOI: https://doi.org/10.1016/j.isci.2023.107394
| Excerpt / Summary [iScience, 13 July 2023]
Here, we exploit a deep serological profiling strategy coupled with an integrated, computational framework for the analysis of SARS-CoV-2 humoral immune responses. Applying a high-density peptide array (HDPA) spanning the entire proteomes of SARS-CoV-2 and endemic human coronaviruses allowed identification of B cell epitopes and relate them to their evolutionary and structural properties. We identify hotspots of pre-existing immunity and identify cross-reactive epitopes that contribute to increasing the overall humoral immune response to SARS-CoV-2. Using a public dataset of over 38,000 viral genomes from the early phase of the pandemic, capturing both inter- and within-host genetic viral diversity, we determined the evolutionary profile of epitopes and the differences across proteins, waves, and SARS-CoV-2 variants. Lastly, we show that mutations in spike and nucleocapsid epitopes are under stronger selection between than within patients, suggesting that most of the selective pressure for immune evasion occurs upon transmission between hosts. |
Link[68] Examining the effect of the COVID-19 pandemic on community virus prevalence and healthcare utilisation reveals that peaks in asthma, COPD and respiratory tract infection occur with the re-emergence of rhino/enterovirus
Author: Terence Ho, Abdullah Shahzad, Aaron Jones, Natya Raghavan, Mark Loeb, Neil Johnston Publication date: 9 July 2023 Publication info: Thorax, 9 July 2023 Cited by: David Price 7:33 PM 10 December 2023 GMT Citerank: (3) 679843Mark LoebProfessor at Pathology and Molecular Medicine (primary), Clinical Epidemiology and Biostatistics in the Department of Pathology and Molecular Medicine at McMaster University. Associate Member, Medicine and Michael G. DeGroote Chair in Infectious Diseases.10019D3ABAB, 685420Hospitals16289D5D4, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0 URL: DOI: https://doi.org/10.1136/thorax-2022-219957
| Excerpt / Summary [Thorax, 9 July 2023]
Introduction: Airway disease exacerbations are cyclical related to respiratory virus prevalence. The COVID-19 pandemic has been associated with reduced exacerbations possibly related to public health measures and their impact on non-COVID-19 respiratory viruses. We aimed to investigate the prevalence of non-COVID-19 respiratory viruses during the pandemic compared with prior in Ontario, Canada and healthcare utilisation related to asthma, chronic obstructive pulmonary disease (COPD) and respiratory tract infection.
Methods: This is a population-based retrospective analysis of respiratory virus tests, emergency department (ED) visits and hospitalisations between 2015 and 2021 in Ontario. Weekly virus testing data were used to estimate viral prevalence for all non-COVID-19 respiratory viruses. We plotted the %positivity and observed and expected counts of each virus to visualise the impact of the pandemic. We used Poisson and binomial logistic regression models to estimate the change in %positivity, count of positive viral cases and count of healthcare utilisation during the pandemic.
Results: The prevalence of all non-COVID-19 respiratory viruses decreased dramatically during the pandemic compared with prior. Comparing periods, the incidence rate ratio (IRR) for positive cases corresponded to a >90% reduction for non-COVID-19 respiratory viruses except adenovirus and rhino/enterovirus. Asthma-related ED visits and hospital admissions fell by 57% (IRR 0.43 (95% CI 0.37 to 0.48)) and 61% (IRR 0.39 (95% CI 0.33 to 0.46)). COPD-related ED visits and admissions fell by 63% (IRR 0.37 (95% CI 0.30 to 0.45)) and 45% (IRR 0.55 (95% CI 0.48 to 0.62)). Respiratory tract infection ED visits and admissions fell by 85% (IRR 0.15 (95% CI 0.10 to 0.22)), and 85% (IRR 0.15 (95% CI 0.09 to 0.24)). Rather than the usual peaks in disease condition, during the pandemic, healthcare utilisation peaked in October when rhino/enterovirus peaked.
Conclusions: The prevalence of nearly all non-COVID-19 respiratory viruses decreased during the pandemic and was associated with marked reductions in ED visits and hospitalisations. The re-emergence of rhino/enterovirus was associated with increased healthcare utilisation. |
Link[69] Risk of COVID-19 hospitalization in people living with HIV and HIV-negative individuals and the role of COVID-19 vaccination: A retrospective cohort study
Author: Joseph H. Puyat, Adeleke Fowokan, James Wilton, Naveed Z. Janjua, Jason Wong, Troy Grennan, Catharine Chambers, Abigail Kroch, Cecilia T. Costiniuk, Curtis L. Cooper, Darren Lauscher, Monte Strong, Ann N. Burchell, Aslam H. Anis, Hasina Samji, COVAXHIV Study Team Publication date: 5 July 2023 Publication info: International Journal of Infectious Diseases, VOLUME 135, P49-56, OCTOBER 2023 Cited by: David Price 7:38 PM 10 December 2023 GMT Citerank: (4) 679856Naveed Zafar JanjuaDr. Naveed Zafar Janjua is an epidemiologist and senior scientist at the BC Centre for Disease Control and Clinical Associate Professor at School of Population and Public Health, University of British Columbia. Dr. Janjua is a Medical Doctor (MBBS) with a Masters of Science (MSc) degree in Epidemiology & Biostatistics and Doctorate in Public Health (DrPH). 10019D3ABAB, 685420Hospitals16289D5D4, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 708761HIV859FDEF6 URL: DOI: https://doi.org/10.1016/j.ijid.2023.06.026
| Excerpt / Summary [International Journal of Infectious Diseases, 5 July 2023]
Objective: To examine the risk of hospitalization within 14 days of COVID-19 diagnosis among people living with HIV (PLWH) and HIV-negative individuals who had laboratory-confirmed SARS-CoV-2 infection.
Methods: We used Cox proportional hazard models to compare the relative risk of hospitalization in PLWH and HIV-negative individuals. Then, we used propensity score weighting to examine the influence of sociodemographic factors and comorbid conditions on risk of hospitalization. These models were further stratified by vaccination status and pandemic period (pre-Omicron: December 15, 2020, to November 21, 2021; Omicron: November 22, 2021, to October 31, 2022).
Results: The crude hazard ratio (HR) for risk of hospitalization in PLWH was 2.44 (95% confidence interval [CI]: 2.04-2.94). In propensity score-weighted models that included all covariates, the relative risk of hospitalization was substantially attenuated in the overall analyses (adjusted HR [aHR]: 1.03; 95% CI: 0.85-1.25), in vaccinated (aHR 1.00; 95% CI: 0.69-1.45), inadequately vaccinated (aHR: 1.04; 95% CI: 0.76-1.41) and unvaccinated individuals (aHR: 1.15; 95% CI: 0.84-1.56).
Conclusion: PLWH had about two times the risk of COVID-19 hospitalization than HIV-negative individuals in crude analyses which attenuated in propensity score-weighted models. This suggests that the risk differential can be explained by sociodemographic factors and history of comorbidity, underscoring the need to address social and comorbid vulnerabilities (e.g., injecting drugs) that were more prominent among PLWH. |
Link[70] Comparison of pretrained transformer-based models for influenza and COVID-19 detection using social media text data in Saskatchewan, Canada
Author: Yuan Tian, Wenjing Zhang, Lujie Duan, Wade McDonald, Nathaniel Osgood Publication date: 28 June 2023 Publication info: Front. Digit. Health, 28 June 2023, Volume 5 - 2023 Cited by: David Price 7:41 PM 10 December 2023 GMT Citerank: (6) 679855Nathaniel OsgoodNathaniel D. Osgood is a Professor in the Department of Computer Science and Associate Faculty in the Department of Community Health & Epidemiology at the University of Saskatchewan.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 701037MfPH â Publications144B5ACA0, 703953Machine learning859FDEF6, 703974Influenza859FDEF6, 715666Social networks859FDEF6 URL: DOI: https://doi.org/10.3389/fdgth.2023.1203874
| Excerpt / Summary [Frontiers in Digital Health, 28 June 2023]
Background: The use of social media data provides an opportunity to complement traditional influenza and COVID-19 surveillance methods for the detection and control of outbreaks and informing public health interventions.
Objective: The first aim of this study is to investigate the degree to which Twitter users disclose health experiences related to influenza and COVID-19 that could be indicative of recent plausible influenza cases or symptomatic COVID-19 infections. Second, we seek to use the Twitter datasets to train and evaluate the classification performance of Bidirectional Encoder Representations from Transformers (BERT) and variant language models in the context of influenza and COVID-19 infection detection.
Methods: We constructed two Twitter datasets using a keyword-based filtering approach on English-language tweets collected from December 2016 to December 2022 in Saskatchewan, Canada. The influenza-related dataset comprised tweets filtered with influenza-related keywords from December 13, 2016, to March 17, 2018, while the COVID-19 dataset comprised tweets filtered with COVID-19 symptom-related keywords from January 1, 2020, to June 22, 2021. The Twitter datasets were cleaned, and each tweet was annotated by at least two annotators as to whether it suggested recent plausible influenza cases or symptomatic COVID-19 cases. We then assessed the classification performance of pre-trained transformer-based language models, including BERT-base, BERT-large, RoBERTa-base, RoBERT-large, BERTweet-base, BERTweet-covid-base, BERTweet-large, and COVID-Twitter-BERT (CT-BERT) models, on each dataset. To address the notable class imbalance, we experimented with both oversampling and undersampling methods.
Results: The influenza dataset had 1129 out of 6444 (17.5%) tweets annotated as suggesting recent plausible influenza cases. The COVID-19 dataset had 924 out of 11939 (7.7%) tweets annotated as inferring recent plausible COVID-19 cases. When compared against other language models on the COVID-19 dataset, CT-BERT performed the best, supporting the highest scores for recall (94.8%), F1(94.4%), and accuracy (94.6%). For the influenza dataset, BERTweet models exhibited better performance. Our results also showed that applying data balancing techniques such as oversampling or undersampling method did not lead to improved model performance.
Conclusions: Utilizing domain-specific language models for monitoring usersâ health experiences related to influenza and COVID-19 on social media shows improved classification performance and has the potential to supplement real-time disease surveillance. |
Link[71] A comparison of sampling and testing approaches for the surveillance of SARS-CoV-2 in farmed American mink
Author: Chelsea G. Himsworth, Jessica M. Caleta, Michelle Coombe, Glenna McGregor, Antonia Dibernardo, Robbin Lindsay, Inna Sekirov, Natalie Prystajecky Publication date: 27 June 2023 Publication info: Journal of Veterinary Diagnostic Investigation, Volume 35, Issue 5, June 27, 2023 Cited by: David Price 7:55 PM 10 December 2023 GMT Citerank: (4) 679854Natalie Anne PrystajeckyNatalie Prystajecky is the program head for the Environmental Microbiology program at the BCCDC Public Health Laboratory. She is also a clinical associate professor in the Department of Pathology & Laboratory Medicine at UBC.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 703961Zoonosis859FDEF6, 704022Surveillance859FDEF6 URL: DOI: https://doi.org/10.1177/10406387231183685
| Excerpt / Summary [Journal of Veterinary Diagnostic Investigation, 27 June 2023]
Surveillance for SARS-CoV-2 in American mink (Neovison vison) is a global priority because outbreaks on mink farms have potential consequences for animal and public health. Surveillance programs often focus on screening natural mortalities; however, significant knowledge gaps remain regarding sampling and testing approaches. Using 76 mink from 3 naturally infected farms in British Columbia, Canada, we compared the performance of 2 reverse-transcription real-time PCR (RT-rtPCR) targets (the envelope [E] and RNA-dependent RNA polymerase [RdRp] genes) as well as serology. We also compared RT-rtPCR and sequencing results from nasopharyngeal, oropharyngeal, skin, and rectal swabs, as well as nasopharyngeal samples collected using swabs and interdental brushes. We found that infected mink were generally RT-rtPCRâpositive on all samples; however, Ct values differed significantly among sample types (nasopharyngealâ<âoropharyngealâ<âskinâ<ârectal). There was no difference in the results of nasopharyngeal samples collected using swabs or interdental brushes. For most mink (89.4%), qualitative (i.e., positive vs. negative) serology and RT-rtPCR results were concordant. However, mink were positive on RT-rtPCR and negative on serology and vice versa, and there was no significant correlation between Ct values on RT-rtPCR and percent inhibition on serology. Both the E and RdRp targets were detectable in all sample types, albeit with a small difference in Ct values. Although SARS-CoV-2 RNA can be detected in multiple sample types, passive surveillance programs in mink should focus on multiple target RT-rtPCR testing of nasopharyngeal samples in combination with serology. |
Link[72] COVID-19 lockdown revisionism
Author: Blake Murdoch, Timothy Caulfield Publication date: 17 April 2023 Publication info: CMAJ April 17, 2023 195 (15) E552-E554 Cited by: David Price 8:02 PM 10 December 2023 GMT Citerank: (3) 690184Timothy CaulfieldTimothy Caulfield is a Canada Research Chair in Health Law and Policy, a Professor in the Faculty of Law and the School of Public Health, and Research Director of the Health Law Institute at the University of Alberta.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6 URL: DOI: https://doi.org/10.1503/cmaj.221543
| Excerpt / Summary [CMAJ, 17 April 2023]
The term âlockdownâ has become a powerful and perverted word in the infodemic about democraciesâ responses to the COVID-19 pandemic. Lockdown, as used in public discourse, has expanded to include any public health measure, even if it places little to no restriction on social mobility or interaction. For example, a working literature review and meta-analysis on the effects of lockdowns on COVID-19 mortality misleadingly defined lockdowns as âthe imposition of at least 1 compulsory non-pharmaceutical intervention.â This working paper therefore conflated mandatory isolation for people with confirmed infections and masking policies with heavy-handed limitations on freedom of movement, and since it gained viral fame, it has helped fuel calls for âno more lockdowns.â This working paper has been highly critiqued and is less convincing than comparative assessments of health measures, like the Oxford Stringency Index.
Here, we discuss the spread of misinformation on lockdowns and other public health measures, which we refer to as âlock-down revisionism,â and how this phenomenon has damaged trust in public health initiatives designed to keep people safer⊠|
Link[73] Wastewater-based surveillance can be used to model COVID-19-associated workforce absenteeism
Author: Nicole Acosta, Xiaotian Dai, Tyler Williamson, Michael D. Parkins, et al. - Maria A. Bautista, Barbara J. Waddell, Jangwoo Lee, Kristine Du, Janine McCalder, Puja Pradhan, Chloe Papparis, Xuewen Lu, Thierry Chekouo, Alexander Krusina, Danielle Southern, Rhonda G. Clark, Raymond A. Patterson, Paul Westlund, Jon Meddings, Norma Ruecker, Christopher Lammiman, Coby Duerr, Gopal Achari, Steve E. Hrudey, Bonita E. Lee, Xiaoli Pang, Kevin Frankowski, Casey R.J. Hubert Publication date: 26 June 2023 Publication info: Science of The Total Environment, Volume 900, 2023, 165172, ISSN 0048-9697. Cited by: David Price 8:05 PM 10 December 2023 GMT Citerank: (5) 679891Tyler WilliamsonTyler Williamson is the Director of the Centre for Health Informatics, formerly the Associate Director. In addition, he is an Associate Professor of Biostatistics in the Department of Community Health Sciences as well as the Director of the Health Data Science and Biostatistics Diploma Program at the University of Calgary.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704022Surveillance859FDEF6, 708744Wastewater-based surveillance (WBS) 859FDEF6, 715454Workforce impact859FDEF6 URL: DOI: https://doi.org/10.1016/j.scitotenv.2023.165172
| Excerpt / Summary [Science of The Total Environment, 26 June 2023]
Wastewater-based surveillance (WBS) of infectious diseases is a powerful tool for understanding community COVID-19 disease burden and informing public health policy. The potential of WBS for understanding COVID-19's impact in non-healthcare settings has not been explored to the same degree. Here we examined how SARS-CoV-2 measured from municipal wastewater treatment plants (WWTPs) correlates with workforce absenteeism. SARS-CoV-2 RNA N1 and N2 were quantified three times per week by RT-qPCR in samples collected at three WWTPs servicing Calgary and surrounding areas, Canada (1.4 million residents) between June 2020 and March 2022. Wastewater trends were compared to workforce absenteeism using data from the largest employer in the city (>15,000 staff). Absences were classified as being COVID-19-related, COVID-19-confirmed, and unrelated to COVID-19. Poisson regression was performed to generate a prediction model for COVID-19 absenteeism based on wastewater data. SARS-CoV-2 RNA was detected in 95.5 % (85/89) of weeks assessed. During this period 6592 COVID-19-related absences (1896 confirmed) and 4524 unrelated absences COVID-19 cases were recorded. A generalized linear regression using a Poisson distribution was performed to predict COVID-19-confirmed absences out of the total number of absent employees using wastewater data as a leading indicator (P < 0.0001). The Poisson regression with wastewater as a one-week leading signal has an Akaike information criterion (AIC) of 858, compared to a null model (excluding wastewater predictor) with an AIC of 1895. The likelihood-ratio test comparing the model with wastewater signal with the null model shows statistical significance (P < 0.0001). We also assessed the variation of predictions when the regression model was applied to new data, with the predicted values and corresponding confidence intervals closely tracking actual absenteeism data. Wastewater-based surveillance has the potential to be used by employers to anticipate workforce requirements and optimize human resource allocation in response to trackable respiratory illnesses like COVID-19. |
Link[74] Adaptability of single-nucleotide polymorphism-polymerase chain reaction (SNP-PCR) for subtyping SARS-CoV-2 and a new SNP-PCR for XBB, XBB.1.5, and B.Q.1/B.Q.1.1
Author: Gordon Ritchie, Matthew Young, Natalie Prystajecky, Marc G. Romney, Christopher F. Lowe, Nancy Matic Publication date: 14 April 2023 Publication info: Clinical Microbiology and Infection, LETTER TO THE EDITOR, VOLUME 29, ISSUE 10, P1339-1341, OCTOBER 2023 Cited by: David Price 8:10 PM 10 December 2023 GMT Citerank: (2) 679854Natalie Anne PrystajeckyNatalie Prystajecky is the program head for the Environmental Microbiology program at the BCCDC Public Health Laboratory. She is also a clinical associate professor in the Department of Pathology & Laboratory Medicine at UBC.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0 URL: DOI: https://doi.org/10.1016/j.cmi.2023.06.014
| Excerpt / Summary [Clinical Microbiology and Infection, 14 June 2023]
Single-nucleotide polymorphism (SNP)-PCR has been proposed as a rapid, reliable and complementary method to whole-genome sequencing (WGS) for SARS-CoV-2 surveillance. The advantages of SNP-PCR include the ability of front-line clinical laboratories to easily implement the method without additional instrumentation or bioinformatics, flexibility to edit or update the SNP-PCR as new SARS-CoV-2 variants of concern (VOC) emerge, and rapid turnaround time when needed for clinical management (e.g. determining the effectiveness of monoclonal antibody therapy or investigating prolonged infection versus re-infection). From a laboratory workflow and utilization perspective, SNP-PCR also allows WGS resources to be best reserved for atypical or newly emerging strains, whereas the SNP-PCR rapidly identifies the most common and predominant VOC. Currently, public health experts are calling for prompt testing to identify new cases of the latest emerging variants: XBB, XBB.1.5 and B.Q.1/B.Q.1.1.
Our clinical virology laboratory has performed SNP-PCR for SARS-CoV-2 subtyping on positive clinical samples weekly since January 2021, using various combinations of the commercially available VirSNiP assays (TIB Molbiol, Germany) and end-point hydrolysis probe PCR, validated with WGS (Illumina MiSeq). For all samples undergoing SNP-PCR, 500 ΌL underwent nucleic acid extraction using MagNA Pure 96 (Roche Molecular Systems Inc., USA) into 50 ΌL of eluate, followed by PCR amplification according to the manufacturer's protocols using LightCycler 480 (Roche). Our SNP-PCR algorithm has evolved many times over the course of the pandemic to incorporate new mutations reflecting the latest VOC (Table 1). As readers may currently seek to implement testing protocols in their own laboratories, we describe our previous SNP-PCR algorithms for Omicron variants and the design of a new SNP-PCR to differentiate XBB, XBB.1.5 and B.Q.1/B.Q.1.1 in a single well, which has not been previously described⊠|
Link[75] SARS-CoV-2 cross-sectional seroprevalence study among public school staff in Metro Vancouver after the first Omicron wave in British Columbia, Canada
Author: Allison W Watts, Louise C MĂąsse, David M Goldfarb, Mike A Irvine, Sarah M Hutchison, Lauren Muttucomaroe, Bethany Poon, Vilte E Barakauskas, Collette OâReilly, Else Bosman, Frederic Reicherz, Daniel Coombs, Mark Pitblado, Sheila F OâBrien, Pascal M Lavoie Publication date: 12 June 2023 Publication info: BMJ Open 2023;13:e071228 Cited by: David Price 8:11 PM 10 December 2023 GMT Citerank: (4) 679773Daniel CoombsProfessor and Head of the Mathematics Department in the Institute of Applied Mathematics at the University of British Columbia.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 715376Serosurveillance859FDEF6, 715617Schools859FDEF6 URL: DOI: https://doi.org/10.1136/bmjopen-2022-071228
| Excerpt / Summary [BMJ Open, 12 June 2023]
Objective: To determine the SARS-CoV-2 seroprevalence among school workers within the Greater Vancouver area, British Columbia, Canada, after the first Omicron wave.
Design: Cross-sectional study by online questionnaire, with blood serology testing.
Setting: Three main school districts (Vancouver, Richmond and Delta) in the Vancouver metropolitan area.
Participants: Active school staff enrolled from January to April 2022, with serology testing between 27 January and 8 April 2022. Seroprevalence estimates were compared with data obtained from Canadian blood donors weighted over the same sampling period, age, sex and postal code distribution.
Primary and secondary outcomes: SARS-CoV-2 nucleocapsid antibody testing results adjusted for test sensitivity and specificity, and regional variation across school districts using Bayesian models.
Results: Of 1850 school staff enrolled, 65.8% (1214/1845) reported close contact with a COVID-19 case outside the household. Of those close contacts, 51.5% (625/1214) were a student and 54.9% (666/1214) were a coworker. Cumulative incidence of COVID-19 positive testing by self-reported nucleic acid or rapid antigen testing since the beginning of the pandemic was 15.8% (291/1845). In a representative sample of 1620 school staff who completed serology testing (87.6%), the adjusted seroprevalence was 26.5% (95% CrI 23.9% to 29.3%), compared with 32.4% (95% CrI 30.6% to 34.5%) among 7164 blood donors.
Conclusion: Despite frequent COVID-19 exposures reported, SARS-CoV-2 seroprevalence among school staff in this setting remained no greater than the community reference group. Results are consistent with the premise that many infections were acquired outside the school setting, even with Omicron. |
Link[76] The influence of sociodemographic factors on COVID-19 vaccine certificate acceptance: A cross-sectional study
Author: David Smith, David T. Zhu, Steven Hawken, A. Brianne Bota, Salima S. Mithani, Alessandro Marcon, Gordon Pennycook, Devon Greyson, Timothy Caulfield, Frank Graves, Jeff Smith, Kumanan Wilson Publication date: 8 June 2023 Publication info: Human Vaccines & Immunotherapeutics, Volume 19, 2023 - Issue 2 Cited by: David Price 8:14 PM 10 December 2023 GMT Citerank: (3) 690184Timothy CaulfieldTimothy Caulfield is a Canada Research Chair in Health Law and Policy, a Professor in the Faculty of Law and the School of Public Health, and Research Director of the Health Law Institute at the University of Alberta.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1080/21645515.2023.2220628
| Excerpt / Summary [Human Vaccines & Immunotherapeutics, 8 June 2023]
Vaccine certificates have been implemented worldwide, aiming to promote vaccination rates and to reduce the spread of COVID-19. However, their use during the COVID-19 pandemic was controversial and has been criticized for infringing upon medical autonomy and individual rights. We administered a national online survey exploring social and demographic factors predicting the degree of public approval of vaccine certificates in Canada. We conducted a multivariate linear regression which revealed which factors were predictive of vaccine certificate acceptance in Canada. Self-reported minority status (pâ<â.001), rurality (pâ<â.001), political ideology (pâ<â.001), age (pâ<â.001), having children under 18 in the household (pâ<â.001), education (pâ=â.014), and income status (pâ=â.034) were significant predictors of attitudes toward COVID-19 vaccine certificates. We observed the lowest vaccine-certificate approval among participants who: self-identify as a visible minority; live in rural areas; are politically conservative; are 18â34âyears of age; have children under age 18 living in the household; have completed an apprenticeship or trades education; and those with an annual income between $100,000â$159,999. The present findings are valuable for their ability to inform the implementation of vaccine certificates during future pandemic scenarios which may require targeted communication between public health agencies and under-vaccinated populations. |
Link[77] Diminished Neutralization Capacity of SARS-CoV-2 Omicron BA.1 in Donor Plasma Collected from January to March 2021
Author: Yi-Chan J. Lin, David H. Evans, Ninette F. Robbins, Guillermo Orjuela, Kento T. Abe, Bhavisha Rathod, Karen Colwill, Anne-Claude Gingras, Ashleigh Tuite, Qi-Long Yi, Sheila F. OâBrien, Steven J. Drews Publication date: 8 June 2023 Publication info: Clinical Microbiology, 8 June 2023 Cited by: David Price 4:17 PM 11 December 2023 GMT Citerank: (2) 679755Ashleigh TuiteAshleigh Tuite is an Assistant Professor in the Epidemiology Division at the Dalla Lana School of Public Health at the University of Toronto.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0 URL: DOI: https://doi.org/10.1128/spectrum.05256-22
| Excerpt / Summary [Clinical Microbiology, 8 June 2023]
The 50% plaque reduction neutralization assay (PRNT50) has been previously used to assess the neutralization capacity of donor plasma against wild-type and variant of concern (VOC) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Emerging data suggest that plasma with an anti-SARS-CoV-2 level of â„2âĂâ104 binding antibody units/mL (BAU/mL) protects against SARS-CoV-2 Omicron BA.1 infection. Specimens were collected using a cross-sectional random sampling approach. For PRNT50 studies, 63 previously analyzed specimens by PRNT50 versus SARS-CoV-2 wild-type, Alpha, Beta, Gamma, and Delta were analyzed by PRNT50 versus Omicron BA.1. The 63 specimens plus 4,390 specimens (randomly sampled regardless of serological evidence of infection) were also tested using the Abbott SARS-CoV-2 IgG II Quant assay (anti-spike [S]; Abbott, Chicago, IL, USA; Abbott Quant assay). In the vaccinated group, the percentages of specimens with any measurable PRNT50 versus wild-type or VOC were wild type (21/25 [84%]), Alpha (19/25 [76%]), Beta (18/25 [72%]), Gamma (13/25 [52%]), Delta (19/25 [76%]), and Omicron BA.1 (9/25 [36%]). In the unvaccinated group, the percentages of specimens with any measurable PRNT50 versus wild type or VOC were wild-type SARS-CoV-2 (16/39 [41%]), Alpha (16/39 [41%]), Beta (10/39 [26%]), Gamma (9/39 [23%]), Delta (16/39 [41%]), and Omicron BA.1 (0/39) (Fisher's exact tests, vaccinated versus unvaccinated for each variant, Pâ<â0.05). None of the 4,453 specimens tested by the Abbott Quant assay had a binding capacity of â„2âĂâ104 BAU/mL. Vaccinated donors were more likely than unvaccinated donors to neutralize Omicron when assessed by a PRNT50 assay. |
Link[78] An exploration of the relationship between wastewater viral signals and COVID-19 hospitalizations in Ottawa, Canada
Author: K. Ken Peng, Elizabeth M. Renouf, Charmaine B. Dean, X. Joan Hu, Robert Delatolla, Douglas G. Manuel Publication date: 7 June 2023 Publication info: Infectious Disease Modelling, Volume 8, Issue 3, 2023, Pages 617-631, ISSN 2468-0427, Cited by: David Price 4:18 PM 11 December 2023 GMT Citerank: (5) 679764Charmaine DeanCharmaine Dean is Vice-President, Research and Professor in the Department of Statistics and Actuarial Science at the University of Waterloo.10019D3ABAB, 685230Doug ManuelDr. Manuel is a Medical Doctor with a Masters in Epidemiology and Royal College specialization in Public Health and Preventive Medicine. He is a Senior Scientist in the Clinical Epidemiology Program at Ottawa Hospital Research Institute, and a Professor in the Departments of Family Medicine and Epidemiology and Community Medicine.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704022Surveillance859FDEF6, 708744Wastewater-based surveillance (WBS) 859FDEF6 URL: DOI: https://doi.org/10.1016/j.idm.2023.05.011
| Excerpt / Summary [Infectious Disease Modelling, 7 June 2023]
Monitoring of viral signal in wastewater is considered a useful tool for monitoring the burden of COVID-19, especially during times of limited availability in testing. Studies have shown that COVID-19 hospitalizations are highly correlated with wastewater viral signals and the increases in wastewater viral signals can provide an early warning for increasing hospital admissions. The association is likely nonlinear and time-varying. This project employs a distributed lag nonlinear model (DLNM) (Gasparrini et al., 2010) to study the nonlinear exposure-response delayed association of the COVID-19 hospitalizations and SARS-CoV-2 wastewater viral signals using relevant data from Ottawa, Canada. We consider up to a 15-day time lag from the average of SARS-CoV N1 and N2 gene concentrations to COVID-19 hospitalizations. The expected reduction in hospitalization is adjusted for vaccination efforts. A correlation analysis of the data verifies that COVID-19 hospitalizations are highly correlated with wastewater viral signals with a time-varying relationship. Our DLNM based analysis yields a reasonable estimate of COVID-19 hospitalizations and enhances our understanding of the association of COVID-19 hospitalizations with wastewater viral signals. |
Link[79] Use of latent class analysis and patient reported outcome measures to identify distinct long COVID phenotypes: A longitudinal cohort study
Author: Alyson W Wong, Karen C Tran, Mawuena Binka, Naveed Z Janjua, Hind Sbihi, James A Russell, Christopher Carlsten, Adeera Levin, Christopher J Ryerson Publication date: 2 June 2023 Publication info: PLoS One. 2023 Jun 2;18(6):e0286588. Cited by: David Price 4:18 PM 11 December 2023 GMT Citerank: (3) 679856Naveed Zafar JanjuaDr. Naveed Zafar Janjua is an epidemiologist and senior scientist at the BC Centre for Disease Control and Clinical Associate Professor at School of Population and Public Health, University of British Columbia. Dr. Janjua is a Medical Doctor (MBBS) with a Masters of Science (MSc) degree in Epidemiology & Biostatistics and Doctorate in Public Health (DrPH). 10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 728545Long COVIDPost-acute sequelae of COVID-19 (PASC).859FDEF6 URL: DOI: https://doi.org/10.1371/journal.pone.0286588
| Excerpt / Summary [PLoS One, 2 June 2023]
Objectives: We sought to 1) identify long COVID phenotypes based on patient reported outcome measures (PROMs) and 2) determine whether the phenotypes were associated with quality of life (QoL) and/or lung function.
Methods: This was a longitudinal cohort study of hospitalized and non-hospitalized patients from March 2020 to January 2022 that was conducted across 4 Post-COVID Recovery Clinics in British Columbia, Canada. Latent class analysis was used to identify long COVID phenotypes using baseline PROMs (fatigue, dyspnea, cough, anxiety, depression, and post-traumatic stress disorder). We then explored the association between the phenotypes and QoL (using the EuroQoL 5 dimensions visual analogue scale [EQ5D VAS]) and lung function (using the diffusing capacity of the lung for carbon monoxide [DLCO]).
Results: There were 1,344 patients enrolled in the study (mean age 51 ±15 years; 780 [58%] were females; 769 (57%) were of a non-White race). Three distinct long COVID phenotypes were identified: Class 1) fatigue and dyspnea, Class 2) anxiety and depression, and Class 3) fatigue, dyspnea, anxiety, and depression. Class 3 had a significantly lower EQ5D VAS at 3 (50±19) and 6 months (54 ± 22) compared to Classes 1 and 2 (p<0.001). The EQ5D VAS significantly improved between 3 and 6 months for Class 1 (median difference of 6.0 [95% CI, 4.0 to 8.0]) and Class 3 (median difference of 5.0 [95% CI, 0 to 8.5]). There were no differences in DLCO between the classes.
Conclusions: There were 3 distinct long COVID phenotypes with different outcomes in QoL between 3 and 6 months after symptom onset. These phenotypes suggest that long COVID is a heterogeneous condition with distinct subpopulations who may have different outcomes and warrant tailored therapeutic approaches. |
Link[80] Association of COVID-19 Infection With Incident Diabetes
Author: Zaeema Naveed, HĂ©ctor A. VelĂĄsquez GarcĂa, Stanley Wong, James Wilton, Geoffrey McKee, Bushra Mahmood, Mawuena Binka, Drona Rasali, Naveed Z. Janjua Publication date: 18 April 2023 Publication info: JAMA Netw Open. 2023;6(4):e238866. Cited by: David Price 4:31 PM 11 December 2023 GMT Citerank: (5) 679856Naveed Zafar JanjuaDr. Naveed Zafar Janjua is an epidemiologist and senior scientist at the BC Centre for Disease Control and Clinical Associate Professor at School of Population and Public Health, University of British Columbia. Dr. Janjua is a Medical Doctor (MBBS) with a Masters of Science (MSc) degree in Epidemiology & Biostatistics and Doctorate in Public Health (DrPH). 10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704045Covid-19859FDEF6, 715953Diabetes859FDEF6, 728545Long COVIDPost-acute sequelae of COVID-19 (PASC).859FDEF6 URL: DOI: https://doi.org/10.1001/jamanetworkopen.2023.8866
| Excerpt / Summary [JAMA Network Open, 18 April 2023]
Importance: SARS-CoV-2 infection may lead to acute and chronic sequelae. Emerging evidence suggests a higher risk of diabetes after infection, but population-based evidence is still sparse.
Objective: To evaluate the association between COVID-19 infection, including severity of infection, and risk of diabetes.
Design, Setting, and Participants: This population-based cohort study was conducted in British Columbia, Canada, from January 1, 2020, to December 31, 2021, using the British Columbia COVID-19 Cohort, a surveillance platform that integrates COVID-19 data with population-based registries and administrative data sets. Individuals tested for SARS-CoV-2 by real-time reverse transcription-polymerase chain reaction (RT-PCR) were included. Those who tested positive for SARS-CoV-2 (ie, those who were exposed) were matched on sex, age, and collection date of RT-PCR test at a 1:4 ratio to those who tested negative (ie, those who were unexposed). Analysis was conducted January 14, 2022, to January 19, 2023.
Exposure: SARS-CoV-2 infection.
Main Outcomes and Measures: The primary outcome was incident diabetes (insulin dependent or not insulin dependent) identified more than 30 days after the specimen collection date for the SARS-CoV-2 test with a validated algorithm based on medical visits, hospitalization records, chronic disease registry, and prescription drugs for diabetes management. Multivariable Cox proportional hazard modeling was performed to evaluate the association between SARS-CoV-2 infection and diabetes risk. Stratified analyses were performed to assess the interaction of SARS-CoV-2 infection with diabetes risk by sex, age, and vaccination status.
Results: Among 629âŻ935 individuals (median [IQR] age, 32 [25.0-42.0] years; 322âŻ565 females [51.2%]) tested for SARS-CoV-2 in the analytic sample, 125âŻ987 individuals were exposed and 503âŻ948 individuals were unexposed. During the median (IQR) follow-up of 257 (102-356) days, events of incident diabetes were observed among 608 individuals who were exposed (0.5%) and 1864 individuals who were not exposed (0.4%). The incident diabetes rate per 100âŻ000 person-years was significantly higher in the exposed vs nonexposed group (672.2 incidents; 95% CI, 618.7-725.6 incidents vs 508.7 incidents; 95% CI, 485.6-531.8 incidents; Pâ<â.001). The risk of incident diabetes was also higher in the exposed group (hazard ratio [HR], 1.17; 95% CI, 1.06-1.28) and among males (adjusted HR, 1.22; 95% CI, 1.06-1.40). The risk of diabetes was higher among people with severe disease vs those without COVID-19, including individuals admitted to the intensive care unit (HR, 3.29; 95% CI, 1.98-5.48) or hospital (HR, 2.42; 95% CI, 1.87-3.15). The fraction of incident diabetes cases attributable to SARS-CoV-2 infection was 3.41% (95% CI, 1.20%-5.61%) overall and 4.75% (95% CI, 1.30%-8.20%) among males.
Conclusions and Relevance: In this cohort study, SARS-CoV-2 infection was associated with a higher risk of diabetes and may have contributed to a 3% to 5% excess burden of diabetes at a population level. |
Link[83] Predicting the daily counts of COVID-19 infection using temporal convolutional networks
Author: Michael Li, Fatemeh Esfahani, Li Xing, Xuekui Zhang Publication date: 26 May 2023 Publication info: JoGH, 26 May 2023 Cited by: David Price 4:41 PM 11 December 2023 GMT Citerank: (2) 685355Xuekui ZhangDr. Xuekui Zhang (PhD) is an Assistant Professor at University of Victoria, a Canada Research Chair (Tier II) in Bioinformatics and Biostatistics (2017-2027), and a Michael Smith Health Research BC Scholar (2022-2027).10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0 URL: DOI: https://doi.org/10.7189/jogh.13.03029
| Excerpt / Summary [JoGH, 26 May 2023]
The coronavirus 2019 (COVID-19) pandemic has significantly impacted the global economy and society. One of the key challenges in combating it was predicting its spread to take appropriate measures, such as lockdowns and social distancing. These measures have now been lifted, and many countries are entering the final stages of the COVID-19 pandemic.
It is essential to continue studying the data collected during the COVID-19 pandemic, even as the focus shifts to recovery and rebuilding, to improve our ability to respond to future pandemics and protect public health. The COVID-19 pandemic has provided a wealth of data that can be used to enhance our understanding of the virus and how it spreads. We used data from 3112 counties in the USA obtained from multiple sources, including the daily infection rates from the COVID-19 Data Repository of the Center for Systems Science and Engineering (CSSE) at the John Hopkins University [1], interventions used to control the spread of the virus [2], and demographics from the US Census [3], to train monitoring systems that detect and track future outbreaks or pandemics, allowing us to better prepare or even mitigate them in advance.
Artificial intelligence (AI) models have been used to forecast the cumulative daily number of COVID-19 cases. These models can analyse large amounts of data and make predictions quickly, which is critical in fast-moving pandemics. We built a forecasting model based on the temporal convolutional network (TCN) [4] and implemented a web application [5] that displays 28-day forecasts for every county in the United States. In our evaluation study, we found that our TCN-based model outperformed its extension (an ensemble model) and other state-of-art forecasting models⊠|
Link[84] Inferring the differences in incubation-period and generation-interval distributions of the Delta and Omicron variants of SARS-CoV-2
Author: Sang Woo Park, Kaiyuan Sun, Sam Abbott, Ron Sender, Yinon M. Bar-on, Joshua S. Weitz, Sebastian Funk, Bryan T. Grenfell, Jantien A. Backer, Jacco Wallinga, Cecile Viboud, Jonathan Dushoff Publication date: 22 May 2023 Publication info: PNAS, 22 May 2023, 120 (22) e2221887120 Cited by: David Price 4:44 PM 11 December 2023 GMT Citerank: (2) 679814Jonathan DushoffProfessor in the Department Of Biology at McMaster University.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0 URL: DOI: https://doi.org/10.1073/pnas.2221887120
| Excerpt / Summary [PNAS, 22 May 2023]
Estimating the differences in the incubation-period, serial-interval, and generation-interval distributions of SARS-CoV-2 variants is critical to understanding their transmission. However, the impact of epidemic dynamics is often neglected in estimating the timing of infectionâfor example, when an epidemic is growing exponentially, a cohort of infected individuals who developed symptoms at the same time are more likely to have been infected recently. Here, we reanalyze incubation-period and serial-interval data describing transmissions of the Delta and Omicron variants from the Netherlands at the end of December 2021. Previous analysis of the same dataset reported shorter mean observed incubation period (3.2 d vs. 4.4 d) and serial interval (3.5 d vs. 4.1 d) for the Omicron variant, but the number of infections caused by the Delta variant decreased during this period as the number of Omicron infections increased. When we account for growth-rate differences of two variants during the study period, we estimate similar mean incubation periods (3.8 to 4.5 d) for both variants but a shorter mean generation interval for the Omicron variant (3.0 d; 95% CI: 2.7 to 3.2 d) than for the Delta variant (3.8 d; 95% CI: 3.7 to 4.0 d). The differences in estimated generation intervals may be driven by the ânetwork effectââhigher effective transmissibility of the Omicron variant can cause faster susceptible depletion among contact networks, which in turn prevents late transmission (therefore shortening realized generation intervals). Using up-to-date generation-interval distributions is critical to accurately estimating the reproduction advantage of the Omicron variant. |
Link[85] A queuing model for ventilator capacity management during the COVID-19 pandemic
Author: Samantha L. Zimmerman, Alexander R. Rutherford, Alexa van der Waall, Monica Norena, Peter Dodek Publication date: 23 May 2023 Publication info: Health Care Management Science, 22 May 2023, Volume 26, pages 200â216 (2023) Cited by: David Price 4:47 PM 11 December 2023 GMT Citerank: (3) 679748Alexander RutherfordDr. Rutherford is the Director for the CSMG. Prior to joining the CSMG, he was the Scientific Executive Officer at the Pacific Institute for the Mathematical Sciences (PIMS). 10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 714608Charting a FutureCharting a Future for Emerging Infectious Disease Modelling in Canada â April 2023 [1] 2794CAE1 URL: DOI: https://doi.org/10.1007/s10729-023-09632-9
| Excerpt / Summary [Health Care Management Science, 22 May 2023]
We applied a queuing model to inform ventilator capacity planning during the first wave of the COVID-19 epidemic in the province of British Columbia (BC), Canada. The core of our framework is a multi-class Erlang loss model that represents ventilator use by both COVID-19 and non-COVID-19 patients. Input for the model includes COVID-19 case projections, and our analysis incorporates projections with different levels of transmission due to public health measures and social distancing. We incorporated data from the BC Intensive Care Unit Database to calibrate and validate the model. Using discrete event simulation, we projected ventilator access, including when capacity would be reached and how many patients would be unable to access a ventilator. Simulation results were compared with three numerical approximation methods, namely pointwise stationary approximation, modified offered load, and fixed point approximation. Using this comparison, we developed a hybrid optimization approach to efficiently identify required ventilator capacity to meet access targets. Model projections demonstrate that public health measures and social distancing potentially averted up to 50 deaths per day in BC, by ensuring that ventilator capacity was not reached during the first wave of COVID-19. Without these measures, an additional 173 ventilators would have been required to ensure that at least 95% of patients can access a ventilator immediately. Our model enables policy makers to estimate critical care utilization based on epidemic projections with different transmission levels, thereby providing a tool to quantify the interplay between public health measures, necessary critical care resources, and patient access indicators. |
Link[86] A fast and scalable method for inferring phylogenetic networks from trees by aligning lineage taxon strings
Author: Louxin Zhang, Niloufar Abhari, Caroline Colijn, Yufeng Wu Publication date: 22 May 2023 Publication info: Genome Res. 2023. 33: 1053-1060 Cited by: David Price 4:49 PM 11 December 2023 GMT Citerank: (3) 679761Caroline ColijnDr. Caroline Colijn works at the interface of mathematics, evolution, infection and public health, and leads the MAGPIE research group. She joined SFU's Mathematics Department in 2018 as a Canada 150 Research Chair in Mathematics for Infection, Evolution and Public Health. She has broad interests in applications of mathematics to questions in evolution and public health, and was a founding member of Imperial College London's Centre for the Mathematics of Precision Healthcare.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 708734Genomics859FDEF6 URL: DOI: 0.1101/gr.277669.123
| Excerpt / Summary [Genome Research, 22 May 2023]
The reconstruction of phylogenetic networks is an important but challenging problem in phylogenetics and genome evolution, as the space of phylogenetic networks is vast and cannot be sampled well. One approach to the problem is to solve the minimum phylogenetic network problem, in which phylogenetic trees are first inferred, and then the smallest phylogenetic network that displays all the trees is computed. The approach takes advantage of the fact that the theory of phylogenetic trees is mature, and there are excellent tools available for inferring phylogenetic trees from a large number of biomolecular sequences. A treeâchild network is a phylogenetic network satisfying the condition that every nonleaf node has at least one child that is of indegree one. Here, we develop a new method that infers the minimum treeâchild network by aligning lineage taxon strings in the phylogenetic trees. This algorithmic innovation enables us to get around the limitations of the existing programs for phylogenetic network inference. Our new program, named ALTS, is fast enough to infer a treeâchild network with a large number of reticulations for a set of up to 50 phylogenetic trees with 50 taxa that have only trivial common clusters in about a quarter of an hour on average. |
Link[87] Estimating the Under-ascertainment of COVID-19 cases in Toronto, Ontario, March to May 2020
Author: Binyam N Desta, Sylvia Ota, Effie Gournis, Sara M Pires, Amy L Greer, Warren Dodd, Shannon E Majowicz Publication date: 12 May 2023 Publication info: Journal of Public Health ResearchVolume 12, Issue 2, April-June 2023 Cited by: David Price 4:52 PM 11 December 2023 GMT Citerank: (3) 679751Amy GreerCanada Research Chair in Population Disease Modelling and an associate professor in the Department of Population Medicine, Ontario Veterinary College at the University of Guelph.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 715831Diagnostic testing859FDEF6 URL: DOI: https://doi.org/10.1177/227990362311741
| Excerpt / Summary [Journal of Public Health Research, 12 May 2023]
Background: Public health surveillance data do not always capture all cases, due in part to test availability and health care seeking behaviour. Our study aimed to estimate under-ascertainment multipliers for each step in the reporting chain for COVID-19 in Toronto, Canada.
Design and methods: We applied stochastic modeling to estimate these proportions for the period from March 2020 (the beginning of the pandemic) through to May 23, 2020, and for three distinct windows with different laboratory testing criteria within this period.
Results: For each laboratory-confirmed symptomatic case reported to Toronto Public Health during the entire period, the estimated number of COVID-19 infections in the community was 18 (5th and 95th percentile: 12, 29). The factor most associated with under-reporting was the proportion of those who sought care that received a test.
Conclusions: Public health officials should use improved estimates to better understand the burden of COVID-19 and other similar infections. |
Link[88] Understanding the impact of mobility on COVID-19 spread: A hybrid gravity-metapopulation model of COVID-19
Author: Sarafa A Iyaniwura, Notice Ringa, Prince A Adu, Sunny Mak, Naveed Z Janjua, Michael A Irvine, Michael Otterstatter Publication date: 12 May 2023 Publication info: PLoS Comput Biol. 2023 May 12;19(5):e1011123, PMID: 37172027 PMCID: PMC10208486 Cited by: David Price 4:55 PM 11 December 2023 GMT Citerank: (3) 679856Naveed Zafar JanjuaDr. Naveed Zafar Janjua is an epidemiologist and senior scientist at the BC Centre for Disease Control and Clinical Associate Professor at School of Population and Public Health, University of British Columbia. Dr. Janjua is a Medical Doctor (MBBS) with a Masters of Science (MSc) degree in Epidemiology & Biostatistics and Doctorate in Public Health (DrPH). 10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 703963Mobility859FDEF6 URL: DOI: https://doi.org/10.1371/journal.pcbi.1011123
| Excerpt / Summary [PLoS Computational Biology, 12 May 2023]
The outbreak of the severe acute respiratory syndrome coronavirus 2 started in Wuhan, China, towards the end of 2019 and spread worldwide. The rapid spread of the disease can be attributed to many factors including its high infectiousness and the high rate of human mobility around the world. Although travel/movement restrictions and other non-pharmaceutical interventions aimed at controlling the disease spread were put in place during the early stages of the pandemic, these interventions did not stop COVID-19 spread. To better understand the impact of human mobility on the spread of COVID-19 between regions, we propose a hybrid gravity-metapopulation model of COVID-19. Our modeling framework has the flexibility of determining mobility between regions based on the distances between the regions or using data from mobile devices. In addition, our model explicitly incorporates time-dependent human mobility into the disease transmission rate, and has the potential to incorporate other factors that affect disease transmission such as facemasks, physical distancing, contact rates, etc. An important feature of this modeling framework is its ability to independently assess the contribution of each factor to disease transmission. Using a Bayesian hierarchical modeling framework, we calibrate our model to the weekly reported cases of COVID-19 in thirteen local health areas in Metro Vancouver, British Columbia (BC), Canada, from July 2020 to January 2021. We consider two main scenarios in our model calibration: using a fixed distance matrix and time-dependent weekly mobility matrices. We found that the distance matrix provides a better fit to the data, whilst the mobility matrices have the ability to explain the variance in transmission between regions. This result shows that the mobility data provides more information in terms of disease transmission than the distances between the regions. |
Link[89] Association of COVID-19 Infection With Incident Diabetes
Author: Zaeema Naveed, HĂ©ctor A. VelĂĄsquez GarcĂa, Stanley Wong, James Wilton, Geoffrey McKee, Bushra Mahmood, Mawuena Binka, Drona Rasali, Naveed Z. Janjua Publication date: 18 April 2023 Publication info: JAMA Netw Open. 2023;6(4):e238866. Cited by: David Price 6:26 PM 11 December 2023 GMT Citerank: (5) 679856Naveed Zafar JanjuaDr. Naveed Zafar Janjua is an epidemiologist and senior scientist at the BC Centre for Disease Control and Clinical Associate Professor at School of Population and Public Health, University of British Columbia. Dr. Janjua is a Medical Doctor (MBBS) with a Masters of Science (MSc) degree in Epidemiology & Biostatistics and Doctorate in Public Health (DrPH). 10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704045Covid-19859FDEF6, 715953Diabetes859FDEF6, 728545Long COVIDPost-acute sequelae of COVID-19 (PASC).859FDEF6 URL: DOI: https://doi.org/10.1001/jamanetworkopen.2023.8866
| Excerpt / Summary [JAMA Network Open, 18 April 2023]
Importance: SARS-CoV-2 infection may lead to acute and chronic sequelae. Emerging evidence suggests a higher risk of diabetes after infection, but population-based evidence is still sparse.
Objective: To evaluate the association between COVID-19 infection, including severity of infection, and risk of diabetes.
Design, Setting, and Participants: This population-based cohort study was conducted in British Columbia, Canada, from January 1, 2020, to December 31, 2021, using the British Columbia COVID-19 Cohort, a surveillance platform that integrates COVID-19 data with population-based registries and administrative data sets. Individuals tested for SARS-CoV-2 by real-time reverse transcription-polymerase chain reaction (RT-PCR) were included. Those who tested positive for SARS-CoV-2 (ie, those who were exposed) were matched on sex, age, and collection date of RT-PCR test at a 1:4 ratio to those who tested negative (ie, those who were unexposed). Analysis was conducted January 14, 2022, to January 19, 2023.
Exposure: SARS-CoV-2 infection.
Main Outcomes and Measures: The primary outcome was incident diabetes (insulin dependent or not insulin dependent) identified more than 30 days after the specimen collection date for the SARS-CoV-2 test with a validated algorithm based on medical visits, hospitalization records, chronic disease registry, and prescription drugs for diabetes management. Multivariable Cox proportional hazard modeling was performed to evaluate the association between SARS-CoV-2 infection and diabetes risk. Stratified analyses were performed to assess the interaction of SARS-CoV-2 infection with diabetes risk by sex, age, and vaccination status.
Results: Among 629âŻ935 individuals (median [IQR] age, 32 [25.0-42.0] years; 322âŻ565 females [51.2%]) tested for SARS-CoV-2 in the analytic sample, 125âŻ987 individuals were exposed and 503âŻ948 individuals were unexposed. During the median (IQR) follow-up of 257 (102-356) days, events of incident diabetes were observed among 608 individuals who were exposed (0.5%) and 1864 individuals who were not exposed (0.4%). The incident diabetes rate per 100âŻ000 person-years was significantly higher in the exposed vs nonexposed group (672.2 incidents; 95% CI, 618.7-725.6 incidents vs 508.7 incidents; 95% CI, 485.6-531.8 incidents; Pâ<â.001). The risk of incident diabetes was also higher in the exposed group (hazard ratio [HR], 1.17; 95% CI, 1.06-1.28) and among males (adjusted HR, 1.22; 95% CI, 1.06-1.40). The risk of diabetes was higher among people with severe disease vs those without COVID-19, including individuals admitted to the intensive care unit (HR, 3.29; 95% CI, 1.98-5.48) or hospital (HR, 2.42; 95% CI, 1.87-3.15). The fraction of incident diabetes cases attributable to SARS-CoV-2 infection was 3.41% (95% CI, 1.20%-5.61%) overall and 4.75% (95% CI, 1.30%-8.20%) among males.
Conclusions and Relevance: In this cohort study, SARS-CoV-2 infection was associated with a higher risk of diabetes and may have contributed to a 3% to 5% excess burden of diabetes at a population level. |
Link[90] People With Human Immunodeficiency Virus Receiving Suppressive Antiretroviral Therapy Show Typical Antibody Durability After Dual Coronavirus Disease 2019 Vaccination and Strong Third Dose Responses
Author: Hope R Lapointe, Francis Mwimanzi, Natalie Prystajecky, Zabrina L Brumme, et al. - Hope R Lapointe, Francis Mwimanzi, Peter K Cheung, Yurou Sang, Fatima Yaseen, Gisele Umviligihozo, Rebecca Kalikawe, Sarah Speckmaier, Nadia Moran-Garcia, Sneha Datwani, Maggie C Duncan, Olga Agafitei, Siobhan Ennis, Landon Young, Hesham Ali, Bruce Ganase, F Harrison Omondi, Winnie Dong, Junine Toy, Paul Sereda, Laura Burns, Cecilia T Costiniuk, Curtis Cooper, Aslam H Anis, Victor Leung, Daniel T Holmes, Mari L DeMarco, Janet Simons, Malcolm Hedgcock, Christopher F Lowe, Ralph Pantophlet, Marc G Romney, Rolando Barrios, Silvia Guillemi, Chanson J Brumme, Julio S G Montaner, Mark Hull, Marianne Harris, Masahiro Niikura, Mark A Brockman Publication date: 7 June 2023 Publication info: The Journal of Infectious Diseases, Volume 227, Issue 7, 1 April 2023, Pages 838â849 Cited by: David Price 6:30 PM 11 December 2023 GMT Citerank: (4) 679854Natalie Anne PrystajeckyNatalie Prystajecky is the program head for the Environmental Microbiology program at the BCCDC Public Health Laboratory. She is also a clinical associate professor in the Department of Pathology & Laboratory Medicine at UBC.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6, 708761HIV859FDEF6 URL: DOI: https://doi.org/10.1093/infdis/jiac229
| Excerpt / Summary [The Journal of Infectious Diseases, 7 June 2022]
Background: Longer-term humoral responses to 2-dose coronavirus disease 2019 (COVID-19) vaccines remain incompletely characterized in people living with human immunodeficiency virus (HIV) (PLWH), as do initial responses to a third dose.
Methods: We measured antibodies against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein receptor-binding domain, angiotensin-converting enzyme 2 (ACE2) displacement, and viral neutralization against wild-type and Omicron strains up to 6 months after 2-dose vaccination, and 1 month after the third dose, in 99 PLWH receiving suppressive antiretroviral therapy and 152 controls.
Results: Although humoral responses naturally decline after 2-dose vaccination, we found no evidence of lower antibody concentrations or faster rates of antibody decline in PLWH compared with controls after accounting for sociodemographic, health, and vaccine-related factors. We also found no evidence of poorer viral neutralization in PLWH after 2 doses, nor evidence that a low nadir CD4+ T-cell count compromised responses. Postâthird-dose humoral responses substantially exceeded postâsecond-dose levels, though Omicron-specific responses were consistently weaker than responses against wild-type virus. Nevertheless, postâthird-dose responses in PLWH were comparable to or higher than controls. An mRNA-1273 third dose was the strongest consistent correlate of higher postâthird-dose responses.
Conclusion: PLWH receiving suppressive antiretroviral therapy mount strong antibody responses after 2- and 3-dose COVID-19 vaccination. Results underscore the immune benefits of third doses in light of Omicron |
Link[91] Public health interventions, priority populations, and the impact of COVID-19 disruptions on hepatitis C elimination among people who have injected drugs in Montreal (Canada): A modeling study
Author: Charlotte LaniĂšce Delaunay, Marina B. Klein, Arnaud Godin, Joseph Cox, Nadine Kronfli, Bertrand LebouchĂ©, Carla Doyle, Mathieu Maheu-Giroux Publication date: 17 April 2023 Publication info: International Journal of Drug Policy, Volume 116, 2023, 104026, ISSN 0955-3959 Cited by: David Price 6:34 PM 11 December 2023 GMT Citerank: (5) 679844Mathieu Maheu-GirouxCanada Research Chair (Tier 2) in Population Health Modeling and Associate Professor, McGill University.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 703973Hepatitis859FDEF6, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6, 715390Mortality859FDEF6 URL: DOI: https://doi.org/10.1016/j.drugpo.2023.104026
| Excerpt / Summary [International Journal of Drug Policy, 17 April 2023]
Background: In Montreal (Canada), high hepatitis C virus (HCV) seroincidence (21 per 100 person-years in 2017) persists among people who have injected drugs (PWID) despite relatively high testing rates and coverage of needle and syringe programs (NSP) and opioid agonist therapy (OAT). We assessed the potential of interventions to achieve HCV elimination (80% incidence reduction and 65% reduction in HCV-related mortality between 2015 and 2030) in the context of COVID-19 disruptions among all PWID and PWID living with HIV.
Methods: Using a dynamic model of HCV-HIV co-transmission, we simulated increases in NSP (from 82% to 95%) and OAT (from 33% to 40%) coverage, HCV testing (every 6 months), or treatment rate (100 per 100 person-years) starting in 2022 among all PWID and PWID living with HIV. We also modeled treatment scale-up among active PWID only (i.e., people who report injecting in the past six months). We reduced intervention levels in 2020â2021 due to COVID-19-related disruptions. Outcomes included HCV incidence, prevalence, and mortality, and proportions of averted chronic HCV infections and deaths.
Results: COVID-19-related disruptions could have caused temporary rebounds in HCV transmission. Further increasing NSP/OAT or HCV testing had little impact on incidence. Scaling-up treatment among all PWID achieved incidence and mortality targets among all PWID and PWID living with HIV. Focusing treatment on active PWID could achieve elimination, yet fewer projected deaths were averted (36% versus 48%).
Conclusions: HCV treatment scale-up among all PWID will be required to eliminate HCV in high-incidence and prevalence settings. Achieving elimination by 2030 will entail concerted efforts to restore and enhance pre-pandemic levels of HCV prevention and care. |
Link[92] Zooanthroponotic transmission of SARS-CoV-2 and host-specific viral mutations revealed by genome-wide phylogenetic analysis
Author: Sana Naderi, Peter E Chen, Carmen Lia Murall, Raphael Poujol, Susanne Kraemer, Bradley S Pickering, Selena M Sagan, B Jesse Shapiro Publication date: 4 April 2023 Publication info: eLife, 4 April 2023 Cited by: David Price 6:39 PM 11 December 2023 GMT Citerank: (5) 679756Jesse ShapiroJesse Shapiro is an Associate Professor in the Faculty of Medicine and Health Sciences at McGill University. Jesseâs research uses genomics to understand the ecology and evolution of microbes, ranging from freshwater bacterioplankton to the human gut microbiome. His work has helped elucidate the origins of bacterial species, leading to a more unified species concept across domains of life, and has developed genome-wide association study (GWAS) methods tailored for bacteria.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 703961Zoonosis859FDEF6, 708734Genomics859FDEF6, 715351Sana NaderiSana is a PhD student in the Shapiro Lab in the McGill Genome Center and the Department of Microbiology and Immunology at McGill University.10019D3ABAB URL: DOI: https://doi.org/10.7554/eLife.83685
| Excerpt / Summary [eLife, 4 April 2023]
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a generalist virus, infecting and evolving in numerous mammals, including captive and companion animals, free-ranging wildlife, and humans. Transmission among non-human species poses a risk for the establishment of SARS-CoV-2 reservoirs, makes eradication difficult, and provides the virus with opportunities for new evolutionary trajectories, including the selection of adaptive mutations and the emergence of new variant lineages. Here, we use publicly available viral genome sequences and phylogenetic analysis to systematically investigate the transmission of SARS-CoV-2 between human and non-human species and to identify mutations associated with each species. We found the highest frequency of animal-to-human transmission from mink, compared with lower transmission from other sampled species (cat, dog, and deer). Although inferred transmission events could be limited by sampling biases, our results provide a useful baseline for further studies. Using genome-wide association studies, no single nucleotide variants (SNVs) were significantly associated with cats and dogs, potentially due to small sample sizes. However, we identified three SNVs statistically associated with mink and 26 with deer. Of these SNVs, approx â
were plausibly introduced into these animal species from local human populations, while the remaining approx â
were more likely derived in animal populations and are thus top candidates for experimental studies of species-specific adaptation. Together, our results highlight the importance of studying animal-associated SARS-CoV-2 mutations to assess their potential impact on human and animal health. |
Link[93] COVID-19 Vaccine Effectiveness Against Omicron Infection and Hospitalization
Author: Pierre-Philippe PichĂ©-Renaud, Sarah Swayze, Sarah A. Buchan, Sarah E. Wilson, Peter C. Austin, Shaun K. Morris, Sharifa Nasreen, Kevin L. Schwartz, Mina Tadrous, Nisha Thampi, Kumanan Wilson, Jeffrey C. Kwong Publication date: 3 March 2023 Publication info: Pediatrics (2023) 151 (4): e2022059513. Cited by: David Price 6:43 PM 11 December 2023 GMT Citerank: (4) 679856Naveed Zafar JanjuaDr. Naveed Zafar Janjua is an epidemiologist and senior scientist at the BC Centre for Disease Control and Clinical Associate Professor at School of Population and Public Health, University of British Columbia. Dr. Janjua is a Medical Doctor (MBBS) with a Masters of Science (MSc) degree in Epidemiology & Biostatistics and Doctorate in Public Health (DrPH). 10019D3ABAB, 685420Hospitals16289D5D4, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1542/peds.2022-059513
| Excerpt / Summary [Pediatrics, 3 March 2023]
OBJECTIVES: This study aimed to provide real-world evidence on coronavirus disease 2019 vaccine effectiveness (VE) against symptomatic infection and severe outcomes caused by Omicron in children aged 5 to 11 years.
METHODS: We used the test-negative study design and linked provincial databases to estimate BNT162b2 vaccine effectiveness against symptomatic infection and severe outcomes caused by Omicron in children aged 5 to 11 years between January 2 and August 27, 2022 in Ontario. We used multivariable logistic regression to estimate VE by time since the latest dose, compared with unvaccinated children, and we evaluated VE by dosing interval.
RESULTS: We included 6284 test-positive cases and 8389 test-negative controls. VE against symptomatic infection declined from 24% (95% confidence interval [CI], 8% to 36%) 14 to 29 days after a first dose and 66% (95% CI, 60% to 71%) 7 to 29 days after 2 doses. VE was higher for children with dosing intervals of â„56 days (57% [95% CI, 51% to 62%]) than 15 to 27 days (12% [95% CI, â11% to 30%]) and 28 to 41 days (38% [95% CI, 28% to 47%]), but appeared to wane over time for all dosing interval groups. VE against severe outcomes was 94% (95% CI, 57% to 99%) 7 to 29 days after 2 doses and declined to 57% (95%CI, â20% to 85%) after â„120 days.
CONCLUSIONS: In children aged 5 to 11 years, 2 doses of BNT162b2 provide moderate protection against symptomatic Omicron infection within 4 months of vaccination and good protection against severe outcomes. Protection wanes more rapidly for infection than severe outcomes. Overall, longer dosing intervals confer higher protection against symptomatic infection, however protection decreases and becomes similar to shorter dosing interval starting 90 days after vaccination. |
Link[94] Observed negative vaccine effectiveness could be the canary in the coal mine for biases in observational COVID-19 studies
Author: Korryn Bodner, Michael A. Irvine, Jeffrey C. Kwong, Sharmistha Mishra Publication date: 26 March 2023 Publication info: International Journal of Infectious Diseases, VOLUME 131, P111-114, JUNE 2023 Cited by: David Price 6:56 PM 11 December 2023 GMT Citerank: (3) 679880Sharmistha MishraSharmistha Mishra is an infectious disease physician and mathematical modeler and holds a Tier 2 Canadian Research Chair in Mathematical Modeling and Program Science.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1016/j.ijid.2023.03.022
| Excerpt / Summary [International Journal of Infectious Diseases, 26 March 2023]
Since the emergence of the SARS-CoV-2 Omicron variant, multiple observational studies have reported negative vaccine effectiveness (VE) against infection, symptomatic infection, and even severity (hospitalization), potentially leading to an interpretation that vaccines were facilitating infection and disease. However, current observations of negative VE likely stem from the presence of various biases (e.g., exposure differences, testing differences). Although negative VE is more likely to arise when true biological efficacy is generally low and biases are large, positive VE measurements can also be subject to the same mechanisms of bias. In this perspective, we first outline the different mechanisms of bias that could lead to false-negative VE measurements and then discuss their ability to potentially influence other protection measurements. We conclude by discussing the use of suspected false-negative VE measurements as a signal to interrogate the estimates (quantitative bias analysis) and to discuss potential biases when communicating real-world immunity research. |
Link[95] A population-based assessment of myocarditis after messenger RNA COVID-19 booster vaccination among adult recipients
Author: Zaeema Naveed, Julia Li, Monika Naus, HĂ©ctor Alexander VelĂĄsquez GarcĂa, James Wilton, Naveed Z. Janjua, Canadian Immunization Research Network Provincial Collaborative Network investigators Publication date: 24 March 2023 Publication info: International Journal of Infectious Diseases, VOLUME 131, P75-78, JUNE 2023 Cited by: David Price 7:04 PM 11 December 2023 GMT Citerank: (3) 679856Naveed Zafar JanjuaDr. Naveed Zafar Janjua is an epidemiologist and senior scientist at the BC Centre for Disease Control and Clinical Associate Professor at School of Population and Public Health, University of British Columbia. Dr. Janjua is a Medical Doctor (MBBS) with a Masters of Science (MSc) degree in Epidemiology & Biostatistics and Doctorate in Public Health (DrPH). 10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1016/j.ijid.2023.03.027
| Excerpt / Summary [International Journal of Infectious Diseases, 24 March 2023]
Objectives: We aimed to estimate the rate of myocarditis after the messenger RNA (mRNA) COVID-19 booster vaccination by vaccine type, age, and sex.
Methods: We used data from the British Columbia COVID-19 Cohort, a population-based cohort surveillance platform. The exposure was a booster dose of an mRNA vaccine. The outcome was diagnosis of myocarditis during hospitalization or an emergency department visit within 7-21 days of booster vaccination.
Results: The overall rate of myocarditis was lower for the booster dose (6.41, 95% confidence interval [CI]: 3.50-10.75) than the second dose (17.97, 95% CI: 13.78-23.04); (Rate ratiobooster vs dose-2 = 0.34, 95% CI: 0.17-0.61). This difference was more apparent for the mRNA-1273 vaccine type. After the second dose, the myocarditis rate in males was significantly lower for BNT162b2 than mRNA-1273 overall and among those aged 18-39 years. In contrast, after the booster dose, no significant differences between myocarditis and vaccine type was observed overall or within the specific age groups among males or females.
Conclusion: Myocarditis after mRNA COVID-19 vaccines is a rare event. A lower absolute risk of myocarditis was observed after a booster dose of mRNA vaccine than the primary series second dose. |
Link[96] Differential Patterns by Area-Level Social Determinants of Health in Coronavirus Disease 2019 (COVID-19)âRelated Mortality and NonâCOVID-19 Mortality: A Population-Based Study of 11.8 Million People in Ontario, Canada
Author: Linwei Wang, Andrew Calzavara, Stefan Baral, Janet Smylie, Adrienne K Chan, Beate Sander, Peter C Austin, Jeffrey C Kwong, Sharmistha Mishra Publication date: 28 October 2022 Publication info: Clinical Infectious Diseases, Volume 76, Issue 6, 15 March 2023, Pages 1110â1120, Cited by: David Price 7:05 PM 11 December 2023 GMT Citerank: (5) 679757Beate SanderCanada Research Chair in Economics of Infectious Diseases and Director, Health Modeling & Health Economics and Population Health Economics Research at THETA (Toronto Health Economics and Technology Assessment Collaborative).10019D3ABAB, 679880Sharmistha MishraSharmistha Mishra is an infectious disease physician and mathematical modeler and holds a Tier 2 Canadian Research Chair in Mathematical Modeling and Program Science.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 703966Social determinants859FDEF6, 715390Mortality859FDEF6 URL: DOI: https://doi.org/10.1093/cid/ciac850
| Excerpt / Summary [Clinical Infectious Diseases, 28 October 2022]
Background: Social determinants of health (SDOH) have been associated with coronavirus disease 2019 (COVID-19) outcomes. We examined patterns in COVID-19ârelated mortality by SDOH and compared these patterns to those for nonâCOVID-19 mortality.
Methods: Residents of Ontario, Canada, aged â„20 years were followed from 1 March 2020 to 2 March 2021. COVID-19ârelated death was defined as death within 30 days following or 7 days prior to a positive COVID-19 test. Area-level SDOH from the 2016 census included median household income; proportion with diploma or higher educational attainment; proportion essential workers, racially minoritized groups, recent immigrants, apartment buildings, and high-density housing; and average household size. We examined associations between SDOH and COVID-19ârelated mortality, and non-COVID-19 mortality using cause-specific hazard models.
Results: Of 11 810 255 individuals, we observed 3880 COVID-19ârelated deaths and 88 107 nonâCOVID-19 deaths. After accounting for demographics, baseline health, and other area-level SDOH, the following were associated with increased hazards of COVID-19ârelated death (hazard ratio [95% confidence interval]: lower income (1.30 [1.04â1.62]), lower educational attainment (1.27 [1.07â1.52]), higher proportions essential workers (1.28 [1.05â1.57]), racially minoritized groups (1.42 [1.08â1.87]), apartment buildings (1.25 [1.07â1.46]), and large vs medium household size (1.30 [1.12â1.50]). Areas with higher proportion racially minoritized groups were associated with a lower hazard of nonâCOVID-19 mortality (0.88 [0.84â0.92]).
Conclusions: Area-level SDOH are associated with COVID-19ârelated mortality after accounting for demographic and clinical factors. COVID-19 has reversed patterns of lower nonâCOVID-19 mortality among racially minoritized groups. Pandemic responses should include strategies to address disproportionate risks and inequitable coverage of preventive interventions associated with SDOH. |
Link[97] Using a hybrid simulation model to assess the impacts of combined COVID-19 containment measures in a high-speed train station
Author: Hongli Zhu, Shiyong Liu, Xiaoyan Li, Weiwei Zhang, Nathaniel Osgood, Peng Jia Publication date: 20 March 2023 Publication info: Journal of Simulation, 20 March 2023 Cited by: David Price 7:10 PM 11 December 2023 GMT Citerank: (5) 679855Nathaniel OsgoodNathaniel D. Osgood is a Professor in the Department of Computer Science and Associate Faculty in the Department of Community Health & Epidemiology at the University of Saskatchewan.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 701037MfPH â Publications144B5ACA0, 703963Mobility859FDEF6, 708812Simulation859FDEF6 URL: DOI: https://doi.org/10.1080/17477778.2023.2189027
| Excerpt / Summary [Journal of Simulation, 20 March 2023]
In this study, we present a hybrid agent-based model (ABM) and discrete event simulation (DES) framework where ABM captures the spread dynamics of COVID-19 via asymptomatic passengers and DES captures the impacts of environmental variables, such as service process capacity, on the results of different containment measures in a typical high-speed train station in China. The containment and control measures simulated include as-is (nothing changed) passenger flow control, enforcing social distancing, adherence level in face mask-wearing, and adding capacity to current service stations. These measures are evaluated individually and then jointly under a different initial number of asymptomatic passengers. The results show how some measures can consolidate the outcomes for each other, while combinations of certain measures could compromise the outcomes for one or the other due to unbalanced service process configurations. The hybrid ABM and DES models offer a useful multi-function simulation tool to help inform decision/policy makers of intervention designs and implementations for addressing issues like public health emergencies and emergency evacuations. Challenges still exist for the hybrid model due to the limited availability of simulation platforms, extensive consumption of computing resources, and difficulties in validation and optimisation. |
Link[98] Food insecurity in Yukon communities during COVID-19: A qualitative study
Author: Sara McPhee-Knowles, David Gatensby Publication date: 27 November 2023 Publication info: Journal of Agriculture, Food Systems, and Community Development, 13(1), 1â14. Cited by: David Price 8:05 PM 12 December 2023 GMT Citerank: (3) 679874Sara McPhee-KnowlesPhD in Public Policy and Instructor in Business Administration at Yukon University.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 708768Food security859FDEF6 URL: DOI: https://doi.org/10.5304/jafscd.2023.131.015
| Excerpt / Summary [Journal of Agriculture, Food Systems, and Community Development, 27 November 2023]
Food insecurity increased in Canada during the COVID-19 pandemic; in the Yukon Territory, the Whitehorse Food Bank saw its scope increase sigÂnificantly as smaller Yukon communities were requesting deliveries of food while travel restrictions were in place. In this qualitative study, the researchers conducted semi-structured interÂviews with food bank clients in Whitehorse and two smaller Yukon communities, as well as repreÂsentatives of other organizations that were involved in community food security initiatives. The results revealed five main themes emerging from shared client experiences and impacts from the pandemic: emphasis on the hamper as core food on an ongoing basis, the importance of tradiÂtional foods, food insecurity and access, the role of the Whitehorse Food Bank in supporting informal networks in communities, and ideal food situations that focused on an abundance of fresh and land-based foods. The results show some contrast between needs in Whitehorse and needs in smaller, more remote Yukon communities. Because of limÂited access to fresh foods in communities outside of Whitehorse, merely increasing income supports would not completely alleviate food insecurity for these participants, who they lack physical access as well as economic access to fresh, preferred foods. |
Link[99] Psychology Meets Biology in COVID-19: What We Know and Why It Matters for Public Health
Author: Emily J. Jones, Kieran Ayling, Cameron R. Wiley, Adam W.A. Geraghty, Amy L. Greer, Julianne Holt-Lunstad, Aric A. Prather, Hannah M.C. Schreier, Roxane Cohen Silver, Rodlescia S. Sneed, Anna L. Marsland, Sarah D. Pressman, Kavita Vedhara Publication date: 15 March 2023 Publication info: Policy Insights from the Behavioral and Brain Sciences, 10(1), 33-40. Volume 10, Issue 1, March 15, 2023 Cited by: David Price 0:39 AM 13 December 2023 GMT Citerank: (2) 679751Amy GreerCanada Research Chair in Population Disease Modelling and an associate professor in the Department of Population Medicine, Ontario Veterinary College at the University of Guelph.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0 URL: DOI: https://doi.org/10.1177/23727322221145308
| Excerpt / Summary [Policy Insights from the Behavioral and Brain Sciences, 15 March 2023]
Psychosocial factors are related to immune, viral, and vaccination outcomes. Yet, this knowledge has been poorly represented in public health initiatives during the COVID-19 pandemic. This review provides an overview of biopsychosocial links relevant to COVID-19 outcomes by describing seminal evidence about these associations known prepandemic as well as contemporary research conducted during the pandemic. This focuses on the negative impact of the pandemic on psychosocial health and how this in turn has likely consequences for critically relevant viral and vaccination outcomes. We end by looking forward, highlighting the potential of psychosocial interventions that could be leveraged to support all people in navigating a postpandemic world and how a biopsychosocial approach to health could be incorporated into public health responses to future pandemics. |
Link[100] Impact of Age and Severe Acute Respiratory Syndrome Coronavirus 2 Breakthrough Infection on Humoral Immune Responses After Three Doses of Coronavirus Disease 2019 mRNA Vaccine
Author: Francis Mwimanzi, Hope R Lapointe, Peter K Cheung, Yurou Sang, Fatima Yaseen, Rebecca Kalikawe, Sneha Datwani, Laura Burns, Landon Young, Victor Leung, Siobhan Ennis, Chanson J Brumme, Julio S G Montaner, Winnie Dong, Natalie Prystajecky, Christopher F Lowe, Mari L DeMarco, Daniel T Holmes, Janet Simons, Masahiro Niikura, Marc G Romney, Zabrina L Brumme, Mark A Brockman Publication date: 9 February 2023 Publication info: Open Forum Infectious Diseases, Volume 10, Issue 3, March 2023, ofad073 Cited by: David Price 0:42 AM 13 December 2023 GMT Citerank: (4) 679854Natalie Anne PrystajeckyNatalie Prystajecky is the program head for the Environmental Microbiology program at the BCCDC Public Health Laboratory. She is also a clinical associate professor in the Department of Pathology & Laboratory Medicine at UBC.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704036Immunology859FDEF6, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1093/ofid/ofad073
| Excerpt / Summary [Open Forum Infectious Diseases, 9 February 2023]
Background: Longer-term immune response data after 3 doses of coronavirus disease 2019 (COVID-19) mRNA vaccine remain limited, particularly among older adults and after Omicron breakthrough infection.
Methods: We quantified wild-type- and Omicron-specific serum immunoglobulin (Ig)G levels, angiotensin-converting enzyme 2 displacement activities, and live virus neutralization up to 6 months after third dose in 116 adults aged 24â98â
years who remained COVID-19 naive or experienced their first severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection during this time.
Results: Among the 78 participants who remained COVID-19 naive throughout follow up, wild-type- and Omicron-BA.1-specific IgG concentrations were comparable between younger and older adults, although BA.1-specific responses were consistently significantly lower than wild-type-specific responses in both groups. Wild-type- and BA.1-specific IgG concentrations declined at similar rates in COVID-19-naive younger and older adults, with median half-lives ranging from 69 to 78â
days. Antiviral antibody functions declined substantially over time in COVID-19-naive individuals, particularly in older adults: by 6 months, BA.1-specific neutralization was undetectable in 96% of older adults, versus 56% of younger adults. Severe acute respiratory syndrome coronavirus 2 infection, experienced by 38 participants, boosted IgG levels and neutralization above those induced by vaccination alone. Nevertheless, BA.1-specific neutralization remained significantly lower than wild-type, with BA.5-specific neutralization lower still. Higher Omicron BA.1-specific neutralization 1 month after third dose was an independent correlate of lower SARS-CoV-2 infection risk.
Conclusions: Results underscore the immune benefits of the third COVID-19 mRNA vaccine dose in adults of all ages and identify vaccine-induced Omicron-specific neutralization as a correlate of protective immunity. Systemic antibody responses and functions however, particularly Omicron-specific neutralization, decline rapidly in COVID-19-naive individuals, particularly in older adults, supporting the need for additional booster doses. |
Link[101] The Role of Vaccine Status Homophily in the COVID-19 Pandemic: A Cross-Sectional Survey with Modeling
Author: Elisha B. Are, Kiffer G. Card, Caroline Colijn Publication date: 10 June 2023 Publication info: medRxiv 2023.06.06.23291056 Cited by: David Price 1:07 AM 13 December 2023 GMT Citerank: (4) 679761Caroline ColijnDr. Caroline Colijn works at the interface of mathematics, evolution, infection and public health, and leads the MAGPIE research group. She joined SFU's Mathematics Department in 2018 as a Canada 150 Research Chair in Mathematics for Infection, Evolution and Public Health. She has broad interests in applications of mathematics to questions in evolution and public health, and was a founding member of Imperial College London's Centre for the Mathematics of Precision Healthcare.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 701758Pacific Institute on Pathogens, Pandemics and Society (PIPPS)The Pacific Institute on Pathogens, Pandemics and Society is a new provincial research institute based at Simon Fraser University's (SFU) Burnaby campus. The Institute focuses on understanding the emergence and spread of new pathogens and responding to infectious disease events with pandemic potential that pose potentially severe risks to the health and well-being of populations.10015D3D3AB, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1101/2023.06.06.23291056
| Excerpt / Summary [medRxiv, 10 June 2023]
Background: Vaccine homophily describes non-heterogeneous vaccine uptake within contact networks. This study was performed to determine observable patterns of vaccine homophily, associations between vaccine homophily, self-reported vaccination, COVID-19 prevention behaviours, contact network size, and self-reported COVID-19, as well as the impact of vaccine homophily on disease transmission within and between vaccination groups under conditions of high and low vaccine efficacy.
Methods: Residents of British Columbia, Canada, aged â„16 years, were recruited via online advertisements between February and March 2022, and provided information about vaccination status, perceived vaccination status of household and non-household contacts, compliance with COVID-19 prevention guidelines, and history of COVID-19. A deterministic mathematical model was used to assess transmission dynamics between vaccine status groups under conditions of high and low vaccine efficacy.
Results: Vaccine homophily was observed among the 1304 respondents, but was lower among those with fewer doses (p<0.0001). Unvaccinated individuals had larger contact networks (p<0.0001), were more likely to report prior COVID-19 (p<0.0001), and reported lower compliance with COVID-19 prevention guidelines (p<0.0001). Mathematical modelling showed that vaccine homophily plays a considerable role in epidemic growth under conditions of high and low vaccine efficacy. Further, vaccine homophily contributes to a high force of infection among unvaccinated individuals under conditions of high vaccine efficacy, as well as elevated force of infection from unvaccinated to vaccinated individuals under conditions of low vaccine efficacy.
Interpretation: The uneven uptake of COVID-19 vaccines and the nature of the contact network in the population play important roles in shaping COVID-19 transmission dynamics. |
Link[102] The need for linked genomic surveillance of SARS-CoV-2
Author: Caroline Colijn, David JD Earn, Jonathan Dushoff, Nicholas H Ogden, Michael Li, Natalie Knox, Gary Van Domselaar, Kristyn Franklin, Gordon Jolly, Sarah P Otto Publication date: 6 April 2022 Publication info: Can Commun Dis Rep. 2022 Apr 6; 48(4): 131â139, PMCID: PMC9017802PMID: 35480703 Cited by: David Price 1:18 AM 13 December 2023 GMT
Citerank: (11) 679761Caroline ColijnDr. Caroline Colijn works at the interface of mathematics, evolution, infection and public health, and leads the MAGPIE research group. She joined SFU's Mathematics Department in 2018 as a Canada 150 Research Chair in Mathematics for Infection, Evolution and Public Health. She has broad interests in applications of mathematics to questions in evolution and public health, and was a founding member of Imperial College London's Centre for the Mathematics of Precision Healthcare.10019D3ABAB, 679814Jonathan DushoffProfessor in the Department Of Biology at McMaster University.10019D3ABAB, 679875Sarah OttoProfessor in Zoology. Theoretical biologist, Canada Research Chair in Theoretical and Experimental Evolution, and Killam Professor at the University of British Columbia.10019D3ABAB, 685445Michael WZ LiMichael Li is Senior Scientist in the Public Health Risk Science Division (PHRS) of the Public Health Agency of Canada (PHAC) and a Research Associate at the South African Centre for Epidemiological Modelling and Analysis (SACEMA).10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 701023GenomicsWhile virus genomes can describe the global context of introductions and origins of local clusters of cases, CANMOD will focus on building methods for characterizing and modelling local transmission once it is established, and for surveillance for viral determinants of increased fitness and of enhanced risk of spillover, virulence and transmission.859FDEF6, 701037MfPH â Publications144B5ACA0, 707634Gary Van DomselaarDr. Gary Van Domselaar, PhD (University of Alberta, 2003) is the Chief of the Bioinformatics Laboratory at the National Microbiology Laboratory in Winnipeg Canada, and Adjunct Professor in the Department of Medical Microbiology at the University of Manitoba.10019D3ABAB, 708734Genomics859FDEF6, 715277Covid-19Covid-19 » Relevance » Genomics10000FFFACD, 715329Nick OgdenNicholas Ogden is a senior research scientist and Director of the Public Health Risk Sciences Division within the National Microbiology Laboratory at the Public Health Agency of Canada.10019D3ABAB URL: DOI: https://doi.org/10.14745/ccdr.v48i04a03
| Excerpt / Summary [Canada Communicable Disease Report, 6 April 2022]
Genomic surveillance during the coronavirus disease 2019 (COVID-19) pandemic has been key to the timely identification of virus variants with important public health consequences, such as variants that can transmit among and cause severe disease in both vaccinated or recovered individuals. The rapid emergence of the Omicron variant highlighted the speed with which the extent of a threat must be assessed. Rapid sequencing and public health institutionsâ openness to sharing sequence data internationally give an unprecedented opportunity to do this; however, assessing the epidemiological and clinical properties of any new variant remains challenging. Here we highlight a âband of fourâ key data sources that can help to detect viral variants that threaten COVID-19 management: 1) genetic (virus sequence) data; 2) epidemiological and geographic data; 3) clinical and demographic data; and 4) immunization data. We emphasize the benefits that can be achieved by linking data from these sources and by combining data from these sources with virus sequence data. The considerable challenges of making genomic data available and linked with virus and patient attributes must be balanced against major consequences of not doing so, especially if new variants of concern emerge and spread without timely detection and action. |
Link[103] Transient prophylaxis and multiple epidemic waves
Author: Rebecca C. Tyson, Noah D. Marshall, Bert O. Baumgaertner Publication date: 10 January 2022 Publication info: AIMS Mathematics, 2022, Volume 7, Issue 4: 5616-5633. Cited by: David Price 1:20 AM 13 December 2023 GMT Citerank: (6) 679842Mark LewisProfessor Mark Lewis, Kennedy Chair in Mathematical Biology at the University of Victoria and Emeritus Professor at the University of Alberta.10019D3ABAB, 679867Rebecca TysonDr. Rebecca C. Tyson is an Associate Professor in Mathematical Biology at the University of British Columbia Okanagan.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 701146Mathematical modelling of human response behaviour during pandemicsMathematical modelling of human response behaviour, opinion dynamics, and social influence during pandemics. COVID-19 showed that understanding human response to intervention is essential in mitigating disease spread and forming policy. We are particularly interested in understanding how opinion influence affects vaccine and NPI hesitancy. This project aims to incorporate a broader understanding of intervention and control, which embodies the entire theme.859FDEF6, 701222OMNI â Publications144B5ACA0, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6 URL: DOI: https://doi.org/10.3934/math.2022311
| Excerpt / Summary [AIMS Mathematics, 10 January 2022]
Public opinion and opinion dynamics can have a strong effect on the transmission rate of an infectious disease for which there is no vaccine. The coupling of disease and opinion dynamics however, creates a dynamical system that is complex and poorly understood. We present a simple model in which susceptible groups adopt or give up prophylactic behaviour in accordance with the influence related to pro- and con-prophylactic communication. This influence varies with disease prevalence. We observe how the speed of the opinion dynamics affects the total size and peak size of the epidemic. We find that more reactive populations will experience a lower peak epidemic size, but possibly a larger final size and more epidemic waves, and that an increase in polarization results in a larger epidemic. |
Link[104] Antigenic evolution of SARS-CoV-2 in immunocompromised hosts
Author: Cameron A Smith, Ben Ashby Publication date: 11 November 2022 Publication info: Evol Med Public Health. 2023; 11(1): 90â100, PMCID: PMC10061940, PMID: 37007166 Cited by: David Price 1:25 AM 13 December 2023 GMT Citerank: (4) 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704036Immunology859FDEF6, 715368Ben AshbyBen is an Associate Professor in the Department of Mathematics at Simon Fraser University.10019D3ABAB, 71537023/11/16 Ben AshbyAntigenic evolution of SARS-CoV-2 in immunocompromised hosts.144B5ACA0 URL: DOI: https://doi.org/10.1093/emph/eoac037
| Excerpt / Summary [Evolution, Medicine, and Public Health, 11 November 2022]
Objectives/aims: Prolonged infections of immunocompromised individuals have been proposed as a crucial source of new variants of SARS-CoV-2 during the COVID-19 pandemic. In principle, sustained within-host antigenic evolution in immunocompromised hosts could allow novel immune escape variants to emerge more rapidly, but little is known about how and when immunocompromised hosts play a critical role in pathogen evolution.
Materials and methods: Here, we use a simple mathematical model to understand the effects of immunocompromised hosts on the emergence of immune escape variants in the presence and absence of epistasis.
Conclusions: We show that when the pathogen does not have to cross a fitness valley for immune escape to occur (no epistasis), immunocompromised individuals have no qualitative effect on antigenic evolution (although they may accelerate immune escape if within-host evolutionary dynamics are faster in immunocompromised individuals). But if a fitness valley exists between immune escape variants at the between-host level (epistasis), then persistent infections of immunocompromised individuals allow mutations to accumulate, therefore, facilitating rather than simply speeding up antigenic evolution. Our results suggest that better genomic surveillance of infected immunocompromised individuals and better global health equality, including improving access to vaccines and treatments for individuals who are immunocompromised (especially in lower- and middle-income countries), may be crucial to preventing the emergence of future immune escape variants of SARS-CoV-2. |
Link[105] HostSeq: a Canadian whole genome sequencing and clinical data resource
Author: S Yoo, E Garg, LT Elliott, LJ Strug, et al. - RJ Hung, AR Halevy, JD Brooks, SB Bull, F Gagnon, CMT Greenwood, JF Lawless, AD Paterson, L Sun, MH Zawati, J Lerner-Ellis, RJS Abraham, I Birol, G Bourque, J-M Garant, C Gosselin, J Li, J Whitney, B Thiruvahindrapuram, J-A Herbrick, M Lorenti, MS Reuter, OO Adeoye, S Liu, U Allen, FP Bernier, CM Biggs, AM Cheung, J Cowan, M Herridge, DM Maslove, BP Modi, V Mooser, SK Morris, M Ostrowski, RS Parekh, G Pfeffer, O Suchowersky, J Taher, J Upton, RL Warren, RSM Yeung, N Aziz, SE Turvey, BM Knoppers, M Lathrop, SJM Jones, SW Scherer Publication date: 2 May 2023 Publication info: BMC Genomic Data volume 24, Article number: 26 (2023) Cited by: David Price 1:29 AM 13 December 2023 GMT Citerank: (3) 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 708734Genomics859FDEF6, 715254Lloyd T. ElliottAssistant Professor, Statistics and Actuarial Science at Simon Fraser University.10019D3ABAB URL: DOI: https://doi.org/10.1186/s12863-023-01128-3
| Excerpt / Summary [BMC Genomic Data, 2 May 2023]
HostSeq was launched in April 2020 as a national initiative to integrate whole genome sequencing data from 10,000 Canadians infected with SARS-CoV-2 with clinical information related to their disease experience. The mandate of HostSeq is to support the Canadian and international research communities in their efforts to understand the risk factors for disease and associated health outcomes and support the development of interventions such as vaccines and therapeutics. HostSeq is a collaboration among 13 independent epidemiological studies of SARS-CoV-2 across five provinces in Canada. Aggregated data collected by HostSeq are made available to the public through two data portals: a phenotype portal showing summaries of major variables and their distributions, and a variant search portal enabling queries in a genomic region. Individual-level data is available to the global research community for health research through a Data Access Agreement and Data Access Compliance Office approval. Here we provide an overview of the collective project design along with summary level information for HostSeq. We highlight several statistical considerations for researchers using the HostSeq platform regarding data aggregation, sampling mechanism, covariate adjustment, and X chromosome analysis. In addition to serving as a rich data source, the diversity of study designs, sample sizes, and research objectives among the participating studies provides unique opportunities for the research community. |
Link[106] Charting a future for emerging infectious disease modelling in Canada
Author: Mark A. Lewis, Patrick Brown, Caroline Colijn, Laura Cowen, Christopher Cotton, Troy Day, Rob Deardon, David Earn, Deirdre Haskell, Jane Heffernan, Patrick Leighton, Kumar Murty, Sarah Otto, Ellen Rafferty, Carolyn Hughes Tuohy, Jianhong Wu, Huaiping Zhu Publication date: 26 April 2023 Cited by: David Price 1:33 AM 13 December 2023 GMT
Citerank: (22) 679703EIDM?The Emerging Infectious Diseases Modelling Initiative (EIDM) â by the Public Health Agency of Canada and NSERC â aims to establish multi-disciplinary network(s) of specialists across the country in modelling infectious diseases to be applied to public needs associated with emerging infectious diseases and pandemics such as COVID-19. [1]7F1CEB7, 679761Caroline ColijnDr. Caroline Colijn works at the interface of mathematics, evolution, infection and public health, and leads the MAGPIE research group. She joined SFU's Mathematics Department in 2018 as a Canada 150 Research Chair in Mathematics for Infection, Evolution and Public Health. She has broad interests in applications of mathematics to questions in evolution and public health, and was a founding member of Imperial College London's Centre for the Mathematics of Precision Healthcare.10019D3ABAB, 679769Christopher CottonChristopher Cotton is a Professor of Economics at Queenâs University where he holds the Jarislowsky-Deutsch Chair in Economic & Financial Policy.10019D3ABAB, 679776David EarnProfessor of Mathematics and Faculty of Science Research Chair in Mathematical Epidemiology at McMaster University.10019D3ABAB, 679797Huaiping ZhuProfessor of mathematics at the Department of Mathematics and Statistics at York University, a York Research Chair (YRC Tier I) in Applied Mathematics, the Director of the Laboratory of Mathematical Parallel Systems at the York University (LAMPS), the Director of the Canadian Centre for Diseases Modelling (CCDM) and the Director of the One Health Modelling Network for Emerging Infections (OMNI-RĂUNIS). 10019D3ABAB, 679806Jane HeffernanJane Heffernan is a professor of infectious disease modelling in the Mathematics & Statistics Department at York University. She is a co-director of the Canadian Centre for Disease Modelling, and she leads national and international networks in mathematical immunology and the modelling of waning and boosting immunity.10019D3ABAB, 679812Jianhong WuProfessor Jianhong Wu is a University Distinguished Research Professor and Senior Canada Research Chair in industrial and applied mathematics at York University. He is also the NSERC Industrial Research Chair in vaccine mathematics, modelling, and manufacturing. 10019D3ABAB, 679826Laura CowenAssociate Professor in the Department of Mathematics and Statistics at the University of Victoria.10019D3ABAB, 679842Mark LewisProfessor Mark Lewis, Kennedy Chair in Mathematical Biology at the University of Victoria and Emeritus Professor at the University of Alberta.10019D3ABAB, 679858Patrick BrownAssociate Professor in the Centre for Global Health Research at St. Michaelâs Hospital, and in the Department of Statistical Sciences at the University of Toronto.10019D3ABAB, 679859Patrick LeightonPatrick Leighton is a Professor of Epidemiology and Public Health at the Faculty of Veterinary Medicine, University of Montreal, and an active member of the Epidemiology of Zoonoses and Public Health Research Group (GREZOSP) and the Centre for Public Health Research (CReSP). 10019D3ABAB, 679869Rob DeardonAssociate Professor in the Department of Production Animal Health in the Faculty of Veterinary Medicine and the Department of Mathematics and Statistics in the Faculty of Science at the University of Calgary.10019D3ABAB, 679875Sarah OttoProfessor in Zoology. Theoretical biologist, Canada Research Chair in Theoretical and Experimental Evolution, and Killam Professor at the University of British Columbia.10019D3ABAB, 679890Troy DayTroy Day is a Professor and the Associate Head of the Department of Mathematics and Statistics at Queenâs University. He is an applied mathematician whose research focuses on dynamical systems, optimization, and game theory, applied to models of infectious disease dynamics and evolutionary biology.10019D3ABAB, 679893Kumar MurtyProfessor Kumar Murty is in the Department of Mathematics at the University of Toronto. His research fields are Analytic Number Theory, Algebraic Number Theory, Arithmetic Algebraic Geometry and Information Security. He is the founder of the GANITA lab, co-founder of Prata Technologies and PerfectCloud. His interest in mathematics ranges from the pure study of the subject to its applications in data and information security.10019D3ABAB, 686724Ellen RaffertyDr. Ellen Rafferty has a Master of Public Health and a PhD in epidemiology and health economics from the University of Saskatchewan. Dr. Raffertyâs research focuses on the epidemiologic and economic impact of public health policies, such as estimating the cost-effectiveness of immunization programs. She is interested in the incorporation of economics into immunization decision-making, and to that aim has worked with a variety of provincial and national organizations.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 701037MfPH â Publications144B5ACA0, 701071OSN â Publications144B5ACA0, 701222OMNI â Publications144B5ACA0, 714608Charting a FutureCharting a Future for Emerging Infectious Disease Modelling in Canada â April 2023 [1] 2794CAE1, 715387SMMEID â Publications144B5ACA0 URL:
| Excerpt / Summary We propose an independent institute of emerging infectious disease modellers and policy experts, with an academic core, capable of renewing itself as needed. This institute will combine science and knowledge translation to inform decision-makers at all levels of government and ensure the highest level of preparedness (and readiness) for the next public health emergency. The Public Health Modelling Institute will provide cost-effective, science-based modelling for public policymakers in an easily visualizable, integrated framework, which can respond in an agile manner to changing needs, questions, and data. To be effective, the Institute must link to modelling groups within government, who are best able to pose questions and convey results for use by public policymakers. |
Link[107] COVID-19 hospitalizations and deaths averted under an accelerated vaccination program in northeastern and southern regions of the USA
Author: Thomas N. Vilches, Pratha Sah, Seyed M. Moghadas, Affan Shoukat, Meagan C. Fitzpatrick, Peter J. Hotez, Eric C. Schneider, Alison P. Galvani Publication date: 28 December 2021 Publication info: The Lancet Regional Health, Americas 6: 100147, Volume 6, 100147, February 2022 Cited by: David Price 1:38 AM 13 December 2023 GMT Citerank: (5) 679878Seyed MoghadasSeyed Moghadas is an infectious disease modeller whose research includes mathematical and computational modelling in epidemiology and immunology. In particular, he is interested in the theoretical and computational aspects of mathematical models describing the underlying dynamics of infectious diseases, with a particular emphasis on establishing strong links between micro (individual) and macro (population) levels.10019D3ABAB, 685420Hospitals16289D5D4, 701037MfPH â Publications144B5ACA0, 704041Vaccination859FDEF6, 715390Mortality859FDEF6 URL: DOI: https://doi.org/10.1016/j.lana.2021.100147
| Excerpt / Summary [The Lancet Regional Health, 28 December 2022]
Background: The fourth wave of COVID-19 pandemic peaked in the US at 160,000 daily cases, concentrated primarily in southern states. As the Delta variant has continued to spread, we evaluated the impact of accelerated vaccination on reducing hospitalization and deaths across northeastern and southern regions of the US census divisions.
Methods: We used an age-stratified agent-based model of COVID-19 to simulate outbreaks in all states within two U.S. regions. The model was calibrated using reported incidence in each state from October 1, 2020 to August 31, 2021, and parameterized with characteristics of the circulating SARS-CoV-2 variants and state-specific daily vaccination rate. We then projected the number of infections, hospitalizations, and deaths that would be averted between September 2021 and the end of March 2022 if the states increased their daily vaccination rate by 20 or 50% compared to maintaining the status quo pace observed during August 2021.
Findings: A 50% increase in daily vaccine doses administered to previously unvaccinated individuals is projected to prevent a total of 30,727 hospitalizations and 11,937 deaths in the two regions between September 2021 and the end of March 2022. Southern states were projected to have a higher weighted average number of hospitalizations averted (18.8) and lives saved (8.3) per 100,000 population, compared to the weighted average of hospitalizations (12.4) and deaths (2.7) averted in northeastern states. On a per capita basis, a 50% increase in daily vaccinations is expected to avert the most hospitalizations in Kentucky (56.7 hospitalizations per 100,000 averted with 95% CrI: 45.56 - 69.9) and prevent the most deaths in Mississippi, (22.1 deaths per 100,000 population prevented with 95% CrI: 18.0 - 26.9).
Interpretation: Accelerating progress to population-level immunity by raising the daily pace of vaccination would prevent substantial hospitalizations and deaths in the US, even in those states that have passed a Delta-driven peak in infections. |
Link[108] Pandemic modelling for regions implementing an elimination strategy
Author: Amy Hurford, Maria M. Martignoni, J.C. Loredo-Osti, Franics Anokye, Julien Arino, Bilal Saleh Husain, Brian Gaas, James Watmough Publication date: 18 July 2022 Publication info: medRxiv 2022.07.18.22277695; doi: Cited by: David Price 1:41 AM 13 December 2023 GMT
Citerank: (8) 679752Amy HurfordAmy Hurford is an Associate Professor jointly appointed in the Department of Biology and the Department of Mathematics and Statistics at Memorial University of Newfoundland and Labrador. 10019D3ABAB, 679805James WatmoughProfessor in the Department of Mathematics and Statistics at the University of New Brunswick.10019D3ABAB, 679817Julien ArinoProfessor and Faculty of Science Research Chair in Fundamental Science with the Department of Mathematics at the University of Manitoba.10019D3ABAB, 690185Brian GaasModeler in the Population and Public Health Evidence and Evaluation branch of the Department of Health and Social Services, Yukon government.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 701037MfPH â Publications144B5ACA0, 703963Mobility859FDEF6, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6 URL: DOI: https://doi.org/10.1101/2022.07.18.22277695
| Excerpt / Summary During the COVID-19 pandemic, some countries, such as Australia, China, Iceland, New Zealand, Thailand and Vietnam, successfully implemented an elimination strategy. Until June 2021, Atlantic Canada and Canadaâs territories had also experienced prolonged periods with few SARS-CoV-2 community cases. Such regions had a need for epidemiological models that could assess the risk of SARS-CoV-2 outbreaks, but most existing frameworks are applicable to regions where SARS-CoV-2 is spreading in the community, and so it was necessary to adapt existing frameworks to meet this need. We distinguish between infections that are travel-related and those that occur in the community, and find that in Newfoundland and Labrador (NL), Nova Scotia, and Prince Edward Island the mean percentage of daily cases that were travel-related was 80% or greater (July 1, 2020 â May 31, 2021). We show that by December 24, 2021, the daily probability of an Omicron variant community outbreak establishing in NL was near one, and nearly twice as high as the previous high, which occurred in September 2021 when the Delta variant was dominant. We evaluate how vaccination and new variants might affect hypothetical future outbreaks in Mt. Pearl, NL. Our modelling framework can be used to evaluate alternative plans to relax public health restrictions when high levels of vaccination are achieved in regions that have implemented an elimination strategy. |
Link[109] Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty
Author: Emily Howerton, Lucie Contamin, Luke C. Mullany, et al. - Michelle Qin, Nicholas G. Reich, Samantha Bents, Rebecca K. Borchering, Sung-mok Jung, Sara L. Loo, Claire P. Smith, John Levander, Jessica Kerr, J. Espino, Willem G. van Panhuis, Harry Hochheiser, Marta Galanti, Teresa Yamana, Sen Pei, Jeffrey Shaman, Kaitlin Rainwater-Lovett, Matt Kinsey, Kate Tallaksen, Shelby Wilson, Lauren Shin, Joseph C. Lemaitre, Joshua Kaminsky, Juan Dent Hulse, Elizabeth C. Lee, Clifton D. McKee, Alison Hill, Dean Karlen, Matteo Chinazzi, Jessica T. Davis, Kunpeng Mu, Xinyue Xiong, Ana Pastore y Piontti, Alessandro Vespignani, Erik T. Rosenstrom, Julie S. Ivy, Maria E. Mayorga, Julie L. Swann, Guido España, Sean Cavany, Sean Moore, Alex Perkins, Thomas Hladish, Alexander Pillai, Kok Ben Toh, Ira Longini Jr., Shi Chen, Rajib Paul, Daniel Janies, Jean-Claude Thill, Anass Bouchnita, Kaiming Bi, Michael Lachmann, Spencer J. Fox, Lauren Ancel Meyers, Ajitesh Srivastava, Przemyslaw Porebski, Srini Venkatramanan, Aniruddha Adiga, Bryan Lewis, Brian Klahn, Joseph Outt Publication date: 20 November 2023 Publication info: Nature Communications, 14, Article number: 7260 (2023) Cited by: David Price 1:43 AM 13 December 2023 GMT Citerank: (2) 685229Dean KarlenR.M. Pearce Professor of Physics, University of Victoria and TRIUMF10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0 URL: DOI: https://doi.org/10.1038/s41467-023-42680-x
| Excerpt / Summary [Nature Communications, 20 November 2023]
Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections. |
Link[110] Effective population size in simple infectious disease models
Author: Madi Yerlanov, Piyush Agarwal, Caroline Colijn, Jessica E. Stockdale Publication date: 6 November 2023 Publication info: Journal of Mathematical Biology, 6 November 2023, Volume 87, Article number: 80 (2023) Cited by: David Price 1:50 AM 13 December 2023 GMT Citerank: (2) 679761Caroline ColijnDr. Caroline Colijn works at the interface of mathematics, evolution, infection and public health, and leads the MAGPIE research group. She joined SFU's Mathematics Department in 2018 as a Canada 150 Research Chair in Mathematics for Infection, Evolution and Public Health. She has broad interests in applications of mathematics to questions in evolution and public health, and was a founding member of Imperial College London's Centre for the Mathematics of Precision Healthcare.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0 URL: DOI: https://doi.org/10.1007/s00285-023-02016-1
| Excerpt / Summary [Journal of Mathematical Biology, 6 November 2023]
Almost all models used in analysis of infectious disease outbreaks contain some notion of population size, usually taken as the census population size of the community in question. In many settings, however, the census population is not equivalent to the population likely to be exposed, for example if there are population structures, outbreak controls or other heterogeneities. Although these factors may be taken into account in the model: adding compartments to a compartmental model, variable mixing rates and so on, this makes fitting more challenging, especially if the population complexities are not fully known. In this work we consider the concept of effective population size in outbreak modelling, which we define as the size of the population involved in an outbreak, as an alternative to use of more complex models. Effective population size is an important quantity in genetics for estimation of genetic diversity loss in populations, but it has not been widely applied in epidemiology. Through simulation studies and application to data from outbreaks of COVID-19 in China, we find that simple SIR models with effective population size can provide a good fit to data which are not themselves simple or SIR. |
Link[111] Mathematical modelling of vaccination rollout and NPIs lifting on COVID-19 transmission with VOC: a case study in Toronto, Canada
Author: Elena Aruffo, Pei Yuan, Yi Tan, Evgenia Gatov, Iain Moyles, Jacques BĂ©lair, James Watmough, Sarah Collier, Julien Arino, Huaiping Zhu Publication date: 15 July 2022 Publication info: BMC Public Health, Volume 22, Article number: 1349 (2022) Cited by: David Price 11:54 PM 13 December 2023 GMT
Citerank: (10) 679797Huaiping ZhuProfessor of mathematics at the Department of Mathematics and Statistics at York University, a York Research Chair (YRC Tier I) in Applied Mathematics, the Director of the Laboratory of Mathematical Parallel Systems at the York University (LAMPS), the Director of the Canadian Centre for Diseases Modelling (CCDM) and the Director of the One Health Modelling Network for Emerging Infections (OMNI-RĂUNIS). 10019D3ABAB, 679799Iain MoylesAssistant Professor in the Department of Mathematics and Statistics at York University. 10019D3ABAB, 679803Jacques BĂ©lairProfessor, Department of Mathematics and Statistics, UniversitĂ© de MontrĂ©al10019D3ABAB, 679805James WatmoughProfessor in the Department of Mathematics and Statistics at the University of New Brunswick.10019D3ABAB, 679817Julien ArinoProfessor and Faculty of Science Research Chair in Fundamental Science with the Department of Mathematics at the University of Manitoba.10019D3ABAB, 701037MfPH â Publications144B5ACA0, 701222OMNI â Publications144B5ACA0, 704041Vaccination859FDEF6, 714608Charting a FutureCharting a Future for Emerging Infectious Disease Modelling in Canada â April 2023 [1] 2794CAE1, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6 URL: DOI: https://doi.org/10.1186/s12889-022-13597-9
| Excerpt / Summary [BMC Public Health, 15 July 2022]
Background: Since December 2020, public health agencies have implemented a variety of vaccination strategies to curb the spread of SARS-CoV-2, along with pre-existing Nonpharmaceutical Interventions (NPIs). Initial strategies focused on vaccinating the elderly to prevent hospitalizations and deaths, but with vaccines becoming available to the broader population, it became important to determine the optimal strategy to enable the safe lifting of NPIs while avoiding virus resurgence.
Methods: We extended the classic deterministic SIR compartmental disease-transmission model to simulate the lifting of NPIs under different vaccine rollout scenarios. Using case and vaccination data from Toronto, Canada between December 28, 2020, and May 19, 2021, we estimated transmission throughout past stages of NPI escalation/relaxation to compare the impact of lifting NPIs on different dates on cases, hospitalizations, and deaths, given varying degrees of vaccine coverages by 20-year age groups, accounting for waning immunity.
Results: We found that, once coverage among the elderly is high enough (80% with at least one dose), the main age groups to target are 20â39 and 40â59 years, wherein first-dose coverage of at least 70% by mid-June 2021 is needed to minimize the possibility of resurgence if NPIs are to be lifted in the summer. While a resurgence was observed for every scenario of NPI lifting, we also found that under an optimistic vaccination coverage (70% coverage by mid-June, along with postponing reopening from August 2021 to September 2021) can reduce case counts and severe outcomes by roughly 57% by December 31, 2021.
Conclusions: Our results suggest that focusing the vaccination strategy on the working-age population can curb the spread of SARS-CoV-2. However, even with high vaccination coverage in adults, increasing contacts and easing protective personal behaviours is not advisable since a resurgence is expected to occur, especially with an earlier reopening. |
Link[112] Quarantine and serial testing for variants of SARS-CoV-2 with benefits of vaccination and boosting on consequent control of COVID-19
Author: Chad R Wells, Abhishek Pandey, Senay Gokcebel, Gary Krieger, A Michael Donoghue, Burton H Singer, Seyed M Moghadas, Alison P Galvani, Jeffrey P Townsend Publication date: 27 July 2022 Publication info: PNAS Nexus, Volume 1, Issue 3, July 2022, pgac100, 27 July 2022 Cited by: David Price 6:21 PM 14 December 2023 GMT
Citerank: (7) 679878Seyed MoghadasSeyed Moghadas is an infectious disease modeller whose research includes mathematical and computational modelling in epidemiology and immunology. In particular, he is interested in the theoretical and computational aspects of mathematical models describing the underlying dynamics of infectious diseases, with a particular emphasis on establishing strong links between micro (individual) and macro (population) levels.10019D3ABAB, 701037MfPH â Publications144B5ACA0, 703963Mobility859FDEF6, 704041Vaccination859FDEF6, 704045Covid-19859FDEF6, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6, 715831Diagnostic testing859FDEF6 URL: DOI: https://doi.org/10.1093/pnasnexus/pgac100
| Excerpt / Summary [PNAS Nexus, 27 July 2022]
Quarantine and serial testing strategies for a disease depend principally on its incubation period and infectiousness profile. In the context of COVID-19, these primary public health tools must be modulated with successive SARS CoV-2 variants of concern that dominate transmission. Our analysis shows that (1) vaccination status of an individual makes little difference to the determination of the appropriate quarantine duration of an infected case, whereas vaccination coverage of the population can have a substantial effect on this duration, (2) successive variants can challenge disease control efforts by their earlier and increased transmission in the disease time course relative to prior variants, and (3) sufficient vaccine boosting of a population substantially aids the suppression of local transmission through frequent serial testing. For instance, with Omicron, increasing immunity through vaccination and boostersâfor instance with 100% of the population is fully immunized and at least 24% having received a third doseâcan reduce quarantine durations by up to 2 d, as well as substantially aid in the repression of outbreaks through serial testing. Our analysis highlights the paramount importance of maintaining high population immunity, preferably by booster uptake, and the role of quarantine and testing to control the spread of SARS CoV-2. |
Link[113] Dataset of non-pharmaceutical interventions and community support measures across Canadian universities and colleges during COVID-19 in 2020
Author: Haleema Ahmed, Taylor Cargill, Nicola Luigi Bragazzi, Jude Dzevela Kong Publication date: 17 November 2022 Publication info: Frontiers in Public Health, 10 Cited by: David Price 6:25 PM 14 December 2023 GMT Citerank: (3) 679815Jude KongDr. Jude Dzevela Kong is an Assistant Professor in the Department of Mathematics and Statistics at York University and the founding Director of the Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC). 10019D3ABAB, 701037MfPH â Publications144B5ACA0, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6 URL: DOI: https://doi.org/10.3389/fpubh.2022.1066654
| Excerpt / Summary [Frontiers in Public Health, 17 November 2022]
In Canada, the first confirmed case of âCoronavirus Disease 2019â (COVID-19), the disease caused by the virus known as âSevere Acute Respiratory Syndrome-related Coronavirus type 2â (SARS-CoV-2), was reported on January 25, 2020. COVID-19 was declared a Public Health Emergency of International Concern (PHEIC) by the World Health Organization (WHO) only 5 days later, on January 30th, and later a global pandemic on March 11, 2020 (1). Without widespread availability of effective COVID-19 vaccines or treatments in Canada, the government relied on non-pharmaceutical intervention (NPI) measures as the primary mitigation strategy for slowing the spread of COVID-19 (2). Canadian post-secondary institutions were faced with the challenge of interpreting the NPI guidance and announcements issued from federal, provincial and local public health authorities as well as decision and policy makers. However, guidance in some regions was regularly revised and/or updated rapidly to reflect the constantly evolving nature of the COVID-19 situation and the gradual accumulation of information on COVID-19 virulence and transmission. Thus, schools were, to some degree, called upon to take an individualized, proactive approach in deciding which NPI decisions to implement and when to implement them (3). In order to address the unique situation of their campus and community, institutions layered multiple COVID-19 mitigation strategies based on what each school deemed necessary for a robust institution-wide response. This process was typically directed by committees composed of university/college leadership, and it involved careful balancing of economic concerns, recommendations by public health authorities, and the needs of students, faculty and staff.
The majority of institutions communicated NPI decisions regularly to their internal student-staff community as well as the wider public through institution websites and social media channels. However, information on the reasoning and context behind these decisions is less typically made public. While studies have been conducted on factors affecting NPI adoption timing for universities in the United States of America, similar research has not been conducted in the context of Canada. Compiling the first dataset on the status and timing of NPI decisions and community support measures made by post-secondary institutions in response to the COVID-19 pandemic is valuable in illuminating for future study, why institutions made certain decisions, how effective these decisions were in containing viral spread, whether these decisions were data-driven and locally-informed, and how these choices intersected with the broader Canadian political and socio-economic landscape of COVID-19. With this aim, this study provides a dataset on the timing of 17 NPI decisions and support measures made by 122 post-secondary institutions throughout the year 2020. |
Link[114] Management of hospital beds and ventilators in the Gauteng province, South Africa, during the COVID-19 pandemic
Author: Mahnaz Alavinejad, Bruce Mellado, Ali Asgary, Mduduzi Mbada,Thuso Mathaha, Benjamin Lieberman, Finn Stevenson, Nidhi Tripathi, Abhaya Kumar Swain, James Orbinski, Jianhong Wu, Jude Dzevela Kong Publication date: 2 November 2022 Publication info: PLOS Global Public Health, 2(11), e0001113 Cited by: David Price 6:26 PM 14 December 2023 GMT Citerank: (5) 679750Ali AsgaryAssociate Professor and Associate Director, Advanced Disaster, Emergency and Rapid Response Simulation (ADERSIM) in the School of Administrative Studies, and Adjunct Professor in the School of Information Technology, at York University.10019D3ABAB, 679812Jianhong WuProfessor Jianhong Wu is a University Distinguished Research Professor and Senior Canada Research Chair in industrial and applied mathematics at York University. He is also the NSERC Industrial Research Chair in vaccine mathematics, modelling, and manufacturing. 10019D3ABAB, 679815Jude KongDr. Jude Dzevela Kong is an Assistant Professor in the Department of Mathematics and Statistics at York University and the founding Director of the Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC). 10019D3ABAB, 685420Hospitals16289D5D4, 701037MfPH â Publications144B5ACA0 URL: DOI: https://doi.org/10.1371/journal.pgph.0001113
| Excerpt / Summary [PLOS Global Public Health, 2 November 2022]
We conducted an observational retrospective study on patients hospitalized with COVID-19, during March 05, 2020, to October 28, 2021, and developed an agent-based model to evaluate effectiveness of recommended healthcare resources (hospital beds and ventilators) management strategies during the COVID-19 pandemic in Gauteng, South Africa. We measured the effectiveness of these strategies by calculating the number of deaths prevented by implementing them. We observed differ ences between the epidemic waves. The length of hospital stay (LOS) during the third wave was lower than the first two waves. The median of the LOS was 6.73 days, 6.63 days and 6.78 days for the first, second and third wave, respectively. A combination of public and private sector provided hospital care to COVID-19 patients requiring ward and Intensive Care Units (ICU) beds. The private sector provided 88.4% of High care (HC)/ICU beds and 49.4% of ward beds, 73.9% and 51.4%, 71.8% and 58.3% during the first, second and third wave, respectively. Our simulation results showed that with a high maximum capacity, i.e., 10,000 general and isolation ward beds, 4,000 high care and ICU beds and 1,200 ventilators, increasing the resource capacity allocated to COVID- 19 patients by 25% was enough to maintain bed availability throughout the epidemic waves. With a medium resource capacity (8,500 general and isolation ward beds, 3,000 high care and ICU beds and 1,000 ventilators) a combination of resource management strategies and their timing and criteria were very effective in maintaining bed availability and therefore preventing excess deaths. With a low number of maximum available resources (7,000 general and isolation ward beds, 2,000 high care and ICU beds and 800 ventilators) and a severe epidemic wave, these strategies were effective in maintaining the bed availability and minimizing the number of excess deaths throughout the epidemic wave. |
Link[115] Management of Healthcare Resources in the Gauteng Province, South Africa, During the COVID-19 Pandemic
Author: Mahnaz Alavinejad, Bruce Mellado, Ali Asgary, Mduduzi Mbada, Thuso Mathaha, Benjamin Lieberman, Finn Stevenson, Nidhi Tripathi, Abhaya Kumar Swain, James Orbinski, Jianhong Wu, Jude Dzevela Kong Publication date: 15 March 2022 Publication info: SSRN Electronic Journal. Cited by: David Price 6:28 PM 14 December 2023 GMT Citerank: (5) 679750Ali AsgaryAssociate Professor and Associate Director, Advanced Disaster, Emergency and Rapid Response Simulation (ADERSIM) in the School of Administrative Studies, and Adjunct Professor in the School of Information Technology, at York University.10019D3ABAB, 679812Jianhong WuProfessor Jianhong Wu is a University Distinguished Research Professor and Senior Canada Research Chair in industrial and applied mathematics at York University. He is also the NSERC Industrial Research Chair in vaccine mathematics, modelling, and manufacturing. 10019D3ABAB, 679815Jude KongDr. Jude Dzevela Kong is an Assistant Professor in the Department of Mathematics and Statistics at York University and the founding Director of the Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC). 10019D3ABAB, 685420Hospitals16289D5D4, 701037MfPH â Publications144B5ACA0 URL: DOI: https://dx.doi.org/10.2139/ssrn.4049177
| Excerpt / Summary [SSRN, 15 March 2022]
We conducted an observational retrospective study on patients hospitalized with COVID-19, during March 05, 2020, to October 28, 2021, and developed an agent-based model to evaluate effectiveness of recommended healthcare resource management strategies during the COVID-19 pandemic in Gauteng, South Africa. We measured the effectiveness of these strategies by calculating the number of deaths prevented by implementing them. We observed differences between the epidemic waves. The length of hospital stay (LOS) during the third wave was lower than the first two waves. The median of the LOS, was 6.73 days for the first wave, 6.63 days for the second wave and 6.78 days for the third wave. A combination of public and private sector provided hospital care to COVID-19 patients requiring ward and Intensive Care Units (ICU) beds. The private sector provided 88.4% of High care (HC)/ICU beds and 49.4% of ward beds during the first wave, 73.9% and 51.4% during the second wave, 71.8% and 58.3% during the third wave. Our simulation results showed that with a high maximum capacity, i.e., 10,000 general and isolation ward beds, 4,000 high care and ICU beds and 1,200 ventilators, increasing the resource capacity allocated to COVID-19 patients by 25% was enough to maintain bed availability throughout the epidemic waves. With a medium resource capacity (8,500 general and isolation ward beds, 3,000 high care and ICU beds and 1,000 ventilators) a combination of resource management strategies and their timing and criteria were very effective in maintaining bed availability and therefore preventing excess deaths. With a low number of maximum available resources (7,000 general and isolation ward beds, 2,000 high care and ICU beds and 800 ventilators) and a severe epidemic wave, these strategies were effective in maintaining the bed availability and minimizing the number of excess deaths for the entire province and throughout the epidemic wave. |
Link[117] Community structured model for vaccine strategies to control COVID19 spread: A mathematical study
Author: Elena Aruffo, Pei Yuan, Yi Tan, Evgenia Gatov, Effie Gournis, Sarah Collier, Nick Ogden, Jacques BĂ©lair, Huaiping Zhu Publication date: 27 October 2022 Publication info: PLoS ONE 17(10): e0258648 Cited by: David Price 6:31 PM 14 December 2023 GMT Citerank: (6) 679797Huaiping ZhuProfessor of mathematics at the Department of Mathematics and Statistics at York University, a York Research Chair (YRC Tier I) in Applied Mathematics, the Director of the Laboratory of Mathematical Parallel Systems at the York University (LAMPS), the Director of the Canadian Centre for Diseases Modelling (CCDM) and the Director of the One Health Modelling Network for Emerging Infections (OMNI-RĂUNIS). 10019D3ABAB, 679803Jacques BĂ©lairProfessor, Department of Mathematics and Statistics, UniversitĂ© de MontrĂ©al10019D3ABAB, 701037MfPH â Publications144B5ACA0, 704041Vaccination859FDEF6, 714608Charting a FutureCharting a Future for Emerging Infectious Disease Modelling in Canada â April 2023 [1] 2794CAE1, 715329Nick OgdenNicholas Ogden is a senior research scientist and Director of the Public Health Risk Sciences Division within the National Microbiology Laboratory at the Public Health Agency of Canada.10019D3ABAB URL: DOI: https://doi.org/10.1371/journal.pone.0258648
| Excerpt / Summary [PLoS ONE, 27 October 2022]
Initial efforts to mitigate the COVID-19 pandemic have relied heavily on non-pharmaceutical interventions (NPIs), including physical distancing, hand hygiene, and mask-wearing. However, an effective vaccine is essential to containing the spread of the virus. We developed a compartmental model to examine different vaccine strategies for controlling the spread of COVID-19. Our framework accounts for testing rates, test-turnaround times, and vaccination waning immunity. Using reported case data from the city of Toronto, Canada between Mar-Dec, 2020 we defined epidemic phases of infection using contact rates as well as the probability of transmission upon contact. We investigated the impact of vaccine distribution by comparing different permutations of waning immunity, vaccine coverage and efficacy throughout various stages of NPIâs relaxation in terms of cases and deaths. The basic reproduction number is also studied. We observed that widespread vaccine coverage substantially reduced the number of cases and deaths. Under phases with high transmission, an early or late reopening will result in new resurgence of the infection, even with the highest coverage. On the other hand, under phases with lower transmission, 60% of coverage is enough to prevent new infections. Our analysis of R0 showed that the basic reproduction number is reduced by decreasing the tests turnaround time and transmission in the household. While we found that household transmission can decrease following the introduction of a vaccine, public health efforts to reduce test turnaround times remain important for virus containment. |
Link[118] Simulating a Hockey Hub COVID-19 Mass Vaccination Facility
Author: Ali Asgary, Hudson Blue, Felippe Cronemberger, Matthew Ni Publication date: 4 May 2022 Publication info: Healthcare 2022, 10(5), 843; Cited by: David Price 6:32 PM 14 December 2023 GMT Citerank: (4) 679750Ali AsgaryAssociate Professor and Associate Director, Advanced Disaster, Emergency and Rapid Response Simulation (ADERSIM) in the School of Administrative Studies, and Adjunct Professor in the School of Information Technology, at York University.10019D3ABAB, 701037MfPH â Publications144B5ACA0, 704041Vaccination859FDEF6, 708812Simulation859FDEF6 URL: DOI: https://doi.org/10.3390/healthcare10050843
| Excerpt / Summary [Healthcare, 4 May 2023]
Mass vaccination is proving to be the most effective method of disease control, and several methods have been developed for the operation of mass vaccination clinics to administer vaccines safely and quickly. One such method is known as the hockey hub model, a relatively new method that involves isolating vaccine recipients in individual cubicles for the entire duration of the vaccination process. Healthcare staff move between the cubicles and administer vaccines. This allows for faster vaccine delivery and less recipient contact. In this paper we present a simulation tool which has been created to model the operation of a hockey hub clinic. This tool was developed using AnyLogic and simulates the process of individuals moving through a hockey hub vaccination clinic. To demonstrate this model, we simulate six scenarios comprising three different arrival rates with and without physical distancing. Findings demonstrate that the hockey hub method of vaccination clinic can function at a large capacity with minimal impact on wait times. |
Link[119] Modeling COVID-19 Outbreaks in Long-Term Care Facilities Using an Agent-Based Modeling and Simulation Approach
Author: Ali Asgary, Hudson Blue, Adriano O. Solis, Zachary McCarthy, Mahdi Najafabadi, Mohammad Ali Tofighi, Jianhong Wu Publication date: 24 February 2022 Publication info: International Journal of Environmental Research and Public Health, 19(5), 2635. Cited by: David Price 6:33 PM 14 December 2023 GMT Citerank: (4) 679750Ali AsgaryAssociate Professor and Associate Director, Advanced Disaster, Emergency and Rapid Response Simulation (ADERSIM) in the School of Administrative Studies, and Adjunct Professor in the School of Information Technology, at York University.10019D3ABAB, 679812Jianhong WuProfessor Jianhong Wu is a University Distinguished Research Professor and Senior Canada Research Chair in industrial and applied mathematics at York University. He is also the NSERC Industrial Research Chair in vaccine mathematics, modelling, and manufacturing. 10019D3ABAB, 701037MfPH â Publications144B5ACA0, 708813Agent-based models859FDEF6 URL: DOI: https://doi.org/10.3390/ijerph19052635
| Excerpt / Summary [International Journal of Environmental Research and Public Health, 24 February 2022]
The elderly, especially those individuals with pre-existing health problems, have been disproportionally at a higher risk during the COVID-19 pandemic. Residents of long-term care facilities have been gravely affected by the pandemic and resident death numbers have been far above those of the general population. To better understand how infectious diseases such as COVID-19 can spread through long-term care facilities, we developed an agent-based simulation tool that uses a contact matrix adapted from previous infection control research in these types of facilities. This matrix accounts for the average distinct daily contacts between seven different agent types that represent the roles of individuals in long-term care facilities. The simulation results were compared to actual COVID-19 outbreaks in some of the long-term care facilities in Ontario, Canada. Our analysis shows that this simulation tool is capable of predicting the number of resident deaths after 50 days with a less than 0.1 variation in death rate. We modeled and predicted the effectiveness of infection control measures by utilizing this simulation tool. We found that to reduce the number of resident deaths, the effectiveness of personal protective equipment must be above 50%. We also found that daily random COVID-19 tests for as low as less than 10% of a long-term care facilityâs population will reduce the number of resident deaths by over 75%. The results further show that combining several infection control measures will lead to more effective outcomes. |
Link[120] Spatiotemporal Analysis of Emergency Calls during the COVID-19 Pandemic: Case of the City of Vaughan
Author: Ali Asgary, Adriano O. Solis, Nawar Khan, Janithra Wimaladasa, Maryam Shafiei Sabet Publication date: 12 June 2023 Publication info: Urban Sci. 2023, 7(2), 62 Cited by: David Price 6:34 PM 14 December 2023 GMT Citerank: (3) 679750Ali AsgaryAssociate Professor and Associate Director, Advanced Disaster, Emergency and Rapid Response Simulation (ADERSIM) in the School of Administrative Studies, and Adjunct Professor in the School of Information Technology, at York University.10019D3ABAB, 701037MfPH â Publications144B5ACA0, 703960Spatio-temporal analysis859FDEF6 URL: DOI: https://doi.org/10.3390/urbansci7020062
| Excerpt / Summary [Urban Science, 12 June 2023]
Cities have experienced different realities during the COVID-19 pandemic due to its impacts and public health measures undertaken to respond to and manage the pandemic. These measures revealed significant implications for municipal functions, particularly emergency services. The aim of this study is to examine the spatiotemporal distribution of emergency calls during different stages/periods of the pandemic in the City of Vaughan, Canada, using spatial density and the emerging hotspot analysis. The Vaughan Fire and Rescue Service (VFRS) provided the dataset of all emergency calls responded to within the City of Vaughan for the period of 1 January 2017 to 15 July 2021. The dataset was divided according to 11 periods during the pandemic, each period associated with certain levels of public health restrictions. A spatial analysis was carried out by converting the data into shapefiles using geographic coordinates of each call. Study findings show significant spatiotemporal changes in patterns of emergency calls during the pandemic, particularly during more stringent public health measures such as lockdowns and closures of nonessential businesses. The results could provide useful information for both resource management in emergency services as well as understanding the underlying causes of such patterns. |
Link[121] Workplace absenteeism due to COVID-19 and influenza across Canada: A mathematical model
Author: W.S. Avusuglo, Rahele Mosleh, Tedi Ramaj, Ao Li, Sileshi Sintayehu Sharbayta, Abdoul Aziz Fall, Srijana Ghimire, Fenglin Shi, Jason K.H. Lee, Edward Thommes, Thomas Shin, Jianhong Wu Publication date: 7 September 2023 Publication info: Journal of Theoretical Biology, 111559â111559, Volume 572, 7 September 2023, Cited by: David Price 6:34 PM 14 December 2023 GMT Citerank: (4) 679812Jianhong WuProfessor Jianhong Wu is a University Distinguished Research Professor and Senior Canada Research Chair in industrial and applied mathematics at York University. He is also the NSERC Industrial Research Chair in vaccine mathematics, modelling, and manufacturing. 10019D3ABAB, 701037MfPH â Publications144B5ACA0, 715419Edward Thommes Edward W. Thommes is an Adjunct Professor of Mathematics at the University of Guelph and at York University. He is a Global Modeling Lead in the Modeling, Epidemiology and Data Science (MEDS) team of Sanofi Vaccines, an Affiliate Researcher in the Waterloo Institute for Complexity and Innovation (WICI), and a member of the Strategic Advisory Committee for the Mathematics for Public Health program at the Fields Institute.10019D3ABAB, 715454Workforce impact859FDEF6 URL: DOI: https://doi.org/10.1016/j.jtbi.2023.111559
| Excerpt / Summary [Journal of Theoretical Biology, 7 September 2023]
The continual distress of COVID-19 cannot be overemphasized. The pandemic economic and social costs are alarming, with recent attributed economic loss amounting to billions of dollars globally. This economic loss is partly driven by workplace absenteeism due to the disease. Influenza is believed to be a culprit in reinforcing this phenomenon as it may exist in the population concurrently with COVID-19 during the influenza season. Furthermore, their joint infection may increase workplace absenteeism leading to additional economic loss. The objective of this project will aim to quantify the collective impact of COVID-19 and influenza on workplace absenteeism via a mathematical compartmental disease model incorporating population screening and vaccination. Our results indicate that appropriate PCR testing and vaccination of both COVID-19 and seasonal influenza may significantly alleviate workplace absenteeism. However, with COVID-19 PCR testing, there may be a critical threshold where additional tests may result in diminishing returns. Regardless, we recommend on-going PCR testing as a public health intervention accompanying concurrent COVID-19 and influenza vaccination with the added caveat that sensitivity analyses will be necessary to determine the optimal thresholds for both testing and vaccine coverage. Overall, our results suggest that rates of COVID-19 vaccination and PCR testing capacity are important factors for reducing absenteeism, while the influenza vaccination rate and the transmission rates for both COVID-19 and influenza have lower and almost equal affect on absenteeism. We also use the model to estimate and quantify the (indirect) benefit that influenza immunization confers against COVID-19 transmission. |
Link[122] COVID-19 and Malaria Co-Infection: Do Stigmatization and Self-Medication Matter? A Mathematical Modelling Study for Nigeria
Author: Wisdom Avusuglo, Qing Han, Woldegebriel Assefa Woldegerima, Nicola Luigi Bragazzi, Ali Ahmadi, Ali Asgary, Jianhong Wu, James Orbinski, Jude Dzevela Kong Publication date: 11 May 2022 Publication info: SSRN Cited by: David Price 6:35 PM 14 December 2023 GMT Citerank: (6) 679750Ali AsgaryAssociate Professor and Associate Director, Advanced Disaster, Emergency and Rapid Response Simulation (ADERSIM) in the School of Administrative Studies, and Adjunct Professor in the School of Information Technology, at York University.10019D3ABAB, 679812Jianhong WuProfessor Jianhong Wu is a University Distinguished Research Professor and Senior Canada Research Chair in industrial and applied mathematics at York University. He is also the NSERC Industrial Research Chair in vaccine mathematics, modelling, and manufacturing. 10019D3ABAB, 679815Jude KongDr. Jude Dzevela Kong is an Assistant Professor in the Department of Mathematics and Statistics at York University and the founding Director of the Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC). 10019D3ABAB, 701037MfPH â Publications144B5ACA0, 704042Malaria859FDEF6, 715767Woldegebriel Assefa WoldegerimaDr. Woldegerima, knows as "Assefa", is an Assistant Professor at the Department of Mathematics and Statistics at York University.10019D3ABAB URL: DOI: http://dx.doi.org/10.2139/ssrn.4090040
| Excerpt / Summary [SSRN, 11 May 2022]
Self-medication and the use of complementary medicine are common among people in the Global South for social, economic, and psychological reasons. Governments in these countries are generally faced with several challenges, including limited resources and poor infrastructure, and patient health literacy. For COVID-19, this is fueled by the rapid spread of rumors in favour of these modalities on social media. Also common in the Global South is the stigmatization of people with COVID-19. Because of the stigma attached to having COVID-19, most COVID-19 patients prefer to test instead for malaria, since malaria (which is very common in the Global South) and COVID-19 share several symptoms leading to misdiagnosis. Thus, to efficiently predict the dynamics of COVID-19 in the Global South, the role of the self-medicated population, the dynamics of malaria, and the impact of stigmatization need to be taken into account. In this paper, we formulate and analyze a mathematical model for the co-dynamics of COVID-19 and malaria in Nigeria. The model is represented by a system of compartmental ODEs that take into account the self-medicated population and the impact of COVID-19 stigmatization. Our findings reveal that COVID-19 stigmatization and misdiagnosis contribute to self-medication, which, in turn, increases the prevalence of COVID-19. The basic and invasion reproduction numbers for these diseases and quantification of model parameters uncertainties and sensitivities are presented. |
Link[123] The effect of COVID-19 on public hospital revenues in Iran: An interrupted time-series analysis
Author: Masoud Behzadifar, Afshin Aalipour, Mohammad Kehsvari, Banafsheh Darvishi Teli, Mahboubeh Khaton Ghanbari, Hasan Abolghasem Gorji, Alaeddin Sheikhi, Samad Azari, Mohammad Heydarian, Seyed Jafar Ehsanzadeh, Jude Dzevela Kong, Maryam Ahadi, Nicola Luigi Bragazzi Publication date: 31 March 2022 Publication info: PLOS ONE, 17(3), e0266343. Cited by: David Price 6:37 PM 14 December 2023 GMT Citerank: (3) 679815Jude KongDr. Jude Dzevela Kong is an Assistant Professor in the Department of Mathematics and Statistics at York University and the founding Director of the Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC). 10019D3ABAB, 685420Hospitals16289D5D4, 701037MfPH â Publications144B5ACA0 URL: DOI: https://doi.org/10.1371/journal.pone.0266343
| Excerpt / Summary [PLOS ONE, 31 March 2022]
Background: The âCoronavirus Disease 2019â (COVID-19) pandemic has become a major challenge for all healthcare systems worldwide, and besides generating a high toll of deaths, it has caused economic losses. Hospitals have played a key role in providing services to patients and the volume of hospital activities has been refocused on COVID-19 patients. Other activities have been limited/repurposed or even suspended and hospitals have been operating with reduced capacity. With the decrease in non-COVID-19 activities, their financial system and sustainability have been threatened, with hospitals facing shortage of financial resources. The aim of this study was to investigate the effects of COVID-19 on the revenues of public hospitals in Lorestan province in western Iran, as a case study.
Method: In this quasi-experimental study, we conducted the interrupted time series analysis to evaluate COVID-19 induced changes in monthly revenues of 18 public hospitals, from April 2018 to August 2021, in Lorestan, Iran. In doing so, public hospitals report their earnings to the University of Medical Sciences monthly; then, we collected this data through the finance office.
Results: Due to COVID-19, the revenues of public hospitals experienced an average monthly decrease of $172,636 thousand (P-value = 0.01232). For about 13 months, the trend of declining hospital revenues continued. However, after February 2021, a relatively stable increase could be observed, with patient admission and elective surgeries restrictions being lifted. The average monthly income of hospitals increased by $83,574 thousand.
Conclusion: COVID-19 has reduced the revenues of public hospitals, which have faced many problems due to the high costs they have incurred. During the crisis, lack of adequate fundings can damage healthcare service delivery, and policymakers should allocate resources to prevent potential shocks. |
Link[124] Non-pharmaceutical intervention levels to reduce the COVID-19 attack ratio among children
Author: Jummy David, Nicola Luigi Bragazzi, Francesca Scarabel, Zachary McCarthy, Jianhong Wu Publication date: 16 March 2022 Publication info: Royal Society Open Science, 9(3), 16 March 2022 Cited by: David Price 6:42 PM 14 December 2023 GMT Citerank: (4) 679812Jianhong WuProfessor Jianhong Wu is a University Distinguished Research Professor and Senior Canada Research Chair in industrial and applied mathematics at York University. He is also the NSERC Industrial Research Chair in vaccine mathematics, modelling, and manufacturing. 10019D3ABAB, 679815Jude KongDr. Jude Dzevela Kong is an Assistant Professor in the Department of Mathematics and Statistics at York University and the founding Director of the Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC). 10019D3ABAB, 701037MfPH â Publications144B5ACA0, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6 URL: DOI: https://doi.org/10.1098/rsos.211863
| Excerpt / Summary [Royal Society Open Science, 16 March 2022]
The attack ratio in a subpopulation is defined as the total number of infections over the total number of individuals in this subpopulation. Using a methodology based on an age-stratified transmission dynamics model, we estimated the attack ratio of COVID-19 among children (individuals 0â11 years) when a large proportion of individuals eligible for vaccination (age 12 and above) are vaccinated to contain the epidemic among this subpopulation, or the effective herd immunity (with additional physical distancing measures). We describe the relationship between the attack ratio among children, the time to remove infected individuals from the transmission chain and the children-to-children daily contact rate while considering the increased transmissibility of virus variants (using the Delta variant as an example). We illustrate the generality and applicability of the methodology established by performing an analysis of the attack ratio of COVID-19 among children in the population of Canada and in its province of Ontario. The clinical attack ratio, defined as the number of symptomatic infections over the total population, can be informed from the attack ratio and both can be reduced substantially via a combination of reduced social mixing and rapid testing and isolation of the children. |
Link[125] Delayed Model for the Transmission and Control of COVID-19 with Fangcang Shelter Hospitals
Author: Guihong Fan, Juan Li, Jacques BĂ©lair, Huaiping Zhu Publication date: 1 February 2023 Publication info: Siam Journal on Applied Mathematics, 83(1), 276â301 Cited by: David Price 6:44 PM 14 December 2023 GMT Citerank: (4) 679797Huaiping ZhuProfessor of mathematics at the Department of Mathematics and Statistics at York University, a York Research Chair (YRC Tier I) in Applied Mathematics, the Director of the Laboratory of Mathematical Parallel Systems at the York University (LAMPS), the Director of the Canadian Centre for Diseases Modelling (CCDM) and the Director of the One Health Modelling Network for Emerging Infections (OMNI-RĂUNIS). 10019D3ABAB, 679803Jacques BĂ©lairProfessor, Department of Mathematics and Statistics, UniversitĂ© de MontrĂ©al10019D3ABAB, 685420Hospitals16289D5D4, 701037MfPH â Publications144B5ACA0 URL: DOI: https://doi.org/10.1137/21m146154x
| Excerpt / Summary [Siam Journal on Applied Mathematics, February 2023]
The ongoing coronavirus disease 2019 (COVID-19) pandemic poses a huge threat to global public health. Motivated by Chinaâs experience of using Fangcang shelter hospitals (FSHs) to successfully combat the epidemic in its initial stages, we present a two-stage delay model considering the average waiting time of patientsâ admission to study the impact of hospital beds and centralized quarantine on mitigating and controlling of the outbreak. We compute the basic reproduction number in terms of the hospital resources and perform a sensitivity analysis of the average waiting times of patients before admission to the hospitals. We conclude that, while designated hospitals save lives in severely infected individuals, the FSHs played a key role in mitigating and eventually curbing the epidemic. We also quantified some key epidemiological indicators, such as the final size of infections and deaths, the peak height and its timing, and the maximum occupation of beds in FSHs. Our study suggests that, for a jurisdiction (region or country) still struggling with COVID-19, when possible, it is essential to increase testing capacity and use a centralized quarantine to massively reduce the severity and magnitude of the epidemic that follows. |
Link[126] The Impact of Quarantine and Medical Resources on the Control of COVID-19 in Wuhan based on a Household Model
Author: Shanshan Feng, Juping Zhang, Juan Li, Xiao-Feng Luo, Huaiping Zhu, Michael Y. Li, Zhen Jin Publication date: 26 February 2022 Publication info: Bulletin of Mathematical Biology, 84(4), 47 Cited by: David Price 6:46 PM 14 December 2023 GMT Citerank: (4) 679797Huaiping ZhuProfessor of mathematics at the Department of Mathematics and Statistics at York University, a York Research Chair (YRC Tier I) in Applied Mathematics, the Director of the Laboratory of Mathematical Parallel Systems at the York University (LAMPS), the Director of the Canadian Centre for Diseases Modelling (CCDM) and the Director of the One Health Modelling Network for Emerging Infections (OMNI-RĂUNIS). 10019D3ABAB, 685387Michael Y LiProfessor of Mathematics in the Department of Mathematical and Statistical Sciences at the University of Alberta, and Director of the Information Research Lab (IRL).10019D3ABAB, 701037MfPH â Publications144B5ACA0, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6 URL: DOI: https://doi.org/10.1007/s11538-021-00989-y
| Excerpt / Summary [Bulletin of Mathematical Biology, 26 February 2022]
In order to understand how Wuhan curbed the COVID-19 outbreak in 2020, we build a network transmission model of 123 dimensions incorporating the impact of quarantine and medical resources as well as household transmission. Using our new model, the final infection size of Wuhan is predicted to be 50,662 (95%CI: 46,234, 55,493), and the epidemic would last until April 25 (95%CI: April 23, April 29), which are consistent with the actual situation. It is shown that quarantining close contacts greatly reduces the final size and shorten the epidemic duration. The opening of Fangcang shelter hospitals reduces the final size by about 17,000. Had the number of hospital beds been sufficient when the lockdown started, the number of deaths would have been reduced by at least 54.26%. We also investigate the distribution of infectious individuals in unquarantined households of different sizes. The high-risk households are those with size from two to four before the peak time, while the households with only one member have the highest risk after the peak time. Our findings provide a reference for the prevention, mitigation and control of COVID-19 in other cities of the world. |
Link[127] Age-stratified transmission model of COVID-19 in Ontario with human mobility during pandemicâs first wave
Author: R. Fields, L. Humphrey D. Flynn-Primrose, Z. Mohammadi, M. Nahirniak, E.W. Thommes, M.G. Cojocaru Publication date: 1 September 2021 Publication info: Heliyon, 7(9), e07905. Cited by: David Price 6:48 PM 14 December 2023 GMT Citerank: (6) 701037MfPH â Publications144B5ACA0, 701624Zahra MohammadiPostdoctoral Fellow, Mathematics for Public health, Fields Institute, Department of Mathematics and Statistics, University of Guelph, Memorial University of Newfoundland.10019D3ABAB, 703963Mobility859FDEF6, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6, 715419Edward Thommes Edward W. Thommes is an Adjunct Professor of Mathematics at the University of Guelph and at York University. He is a Global Modeling Lead in the Modeling, Epidemiology and Data Science (MEDS) team of Sanofi Vaccines, an Affiliate Researcher in the Waterloo Institute for Complexity and Innovation (WICI), and a member of the Strategic Advisory Committee for the Mathematics for Public Health program at the Fields Institute.10019D3ABAB, 715762Monica CojocaruProfessor in the Mathematics & Statistics Department at the University of Guelph. 10019D3ABAB URL: DOI: https://doi.org/10.1016/j.heliyon.2021.e07905
| Excerpt / Summary [Heliyon, 1 September 2021]
In this work, we employ a data-fitted compartmental model to visualize the progression and behavioral response to COVID-19 that match provincial case data in Ontario, Canada from February to June of 2020. This is a ârear-view mirrorâ glance at how this region has responded to the 1st wave of the pandemic, when testing was sparse and NPI measures were the only remedy to stave off the pandemic. We use an SEIR-type model with age-stratified subpopulations and their corresponding contact rates and asymptomatic rates in order to incorporate heterogeneity in our population and to calibrate the time-dependent reduction of Ontario-specific contact rates to reflect intervention measures in the province throughout lockdown and various stages of social-distancing measures. Cellphone mobility data taken from Google, combining several mobility categories, allows us to investigate the effects of mobility reduction and other NPI measures on the evolution of the pandemic. Of interest here is our quantification of the effectiveness of Ontario's response to COVID-19 before and after provincial measures and our conclusion that the sharp decrease in mobility has had a pronounced effect in the first few weeks of the lockdown, while its effect is harder to infer once other NPI measures took hold. |
Link[128] Estimated US Pediatric Hospitalizations and School Absenteeism Associated With Accelerated COVID-19 Bivalent Booster Vaccination
Author: Meagan C. Fitzpatrick, Seyed M. Moghadas, Thomas N. Vilches, Arnav Shah, Abhishek Pandey, Alison P. Galvani Publication date: 19 May 2023 Publication info: JAMA Network Open, 2023;6(5):e2313586. Cited by: David Price 6:49 PM 14 December 2023 GMT Citerank: (5) 679878Seyed MoghadasSeyed Moghadas is an infectious disease modeller whose research includes mathematical and computational modelling in epidemiology and immunology. In particular, he is interested in the theoretical and computational aspects of mathematical models describing the underlying dynamics of infectious diseases, with a particular emphasis on establishing strong links between micro (individual) and macro (population) levels.10019D3ABAB, 685420Hospitals16289D5D4, 701037MfPH â Publications144B5ACA0, 704041Vaccination859FDEF6, 715617Schools859FDEF6 URL: DOI: https://doi.org/10.1001/jamanetworkopen.2023.13586
| Excerpt / Summary [JAMA Network Open, 19 May 2023]
Importance: Adverse outcomes of COVID-19 in the pediatric population include disease and hospitalization, leading to school absenteeism. Booster vaccination for eligible individuals across all ages may promote health and school attendance.
Objective: To assess whether accelerating COVID-19 bivalent booster vaccination uptake across the general population would be associated with reduced pediatric hospitalizations and school absenteeism.
Design, Setting, and Participants: In this decision analytical model, a simulation model of COVID-19 transmission was fitted to reported incidence data from October 1, 2020, to September 30, 2022, with outcomes simulated from October 1, 2022, to March 31, 2023. The transmission model included the entire age-stratified US population, and the outcome model included children younger than 18 years.
Interventions: Simulated scenarios of accelerated bivalent COVID-19 booster campaigns to achieve uptake that was either one-half of or similar to the age-specific uptake observed for 2020 to 2021 seasonal influenza vaccination in the eligible population across all age groups.
Main Outcomes and Measures: The main outcomes were estimated hospitalizations, intensive care unit admissions, and isolation days of symptomatic infection averted among children aged 0 to 17 years and estimated days of school absenteeism averted among children aged 5 to 17 years under the accelerated bivalent booster campaign simulated scenarios.
Results: Among children aged 5 to 17 years, a COVID-19 bivalent booster campaign achieving age-specific coverage similar to influenza vaccination could have averted an estimated 5âŻ448âŻ694 (95% credible interval [CrI], 4âŻ936âŻ933-5âŻ957âŻ507) days of school absenteeism due to COVID-19 illness. In addition, the booster campaign could have prevented an estimated 10âŻ019 (95% CrI, 8756-11âŻ278) hospitalizations among the pediatric population aged 0 to 17 years, of which 2645 (95% CrI, 2152-3147) were estimated to require intensive care. A less ambitious booster campaign with only 50% of the age-specific uptake of influenza vaccination among eligible individuals could have averted an estimated 2âŻ875âŻ926 (95% CrI, 2âŻ524âŻ351-3âŻ332âŻ783) days of school absenteeism among children aged 5 to 17 years and an estimated 5791 (95% CrI, 4391-6932) hospitalizations among children aged 0 to 17 years, of which 1397 (95% CrI, 846-1948) were estimated to require intensive care.
Conclusions and Relevance: In this decision analytical model, increased uptake of bivalent booster vaccination among eligible age groups was associated with decreased hospitalizations and school absenteeism in the pediatric population. These findings suggest that although COVID-19 prevention strategies often focus on older populations, the benefits of booster campaigns for children may be substantial. |
Link[129] Modelling Disease Mitigation at Mass Gatherings: A Case Study of COVID-19 at the 2022 FIFA World Cup
Author: Martin Grunnill, Julien Arino, Zachary McCarthy, Nicola Luigi Bragazzi, Laurent Coudeville, Edward W. Thommes, Amine Amiche, Abbas Ghasemi, Lydia Bourouiba, Mohammadali Tofighi, Ali Asgary, Mortaza Baky-Haskuee, Jianhong Wu Publication date: 29 March 2023 Publication info: medRxiv 2023.03.27.23287214 Cited by: David Price 6:51 PM 14 December 2023 GMT
Citerank: (7) 679750Ali AsgaryAssociate Professor and Associate Director, Advanced Disaster, Emergency and Rapid Response Simulation (ADERSIM) in the School of Administrative Studies, and Adjunct Professor in the School of Information Technology, at York University.10019D3ABAB, 679812Jianhong WuProfessor Jianhong Wu is a University Distinguished Research Professor and Senior Canada Research Chair in industrial and applied mathematics at York University. He is also the NSERC Industrial Research Chair in vaccine mathematics, modelling, and manufacturing. 10019D3ABAB, 679817Julien ArinoProfessor and Faculty of Science Research Chair in Fundamental Science with the Department of Mathematics at the University of Manitoba.10019D3ABAB, 701037MfPH â Publications144B5ACA0, 703963Mobility859FDEF6, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6, 715419Edward Thommes Edward W. Thommes is an Adjunct Professor of Mathematics at the University of Guelph and at York University. He is a Global Modeling Lead in the Modeling, Epidemiology and Data Science (MEDS) team of Sanofi Vaccines, an Affiliate Researcher in the Waterloo Institute for Complexity and Innovation (WICI), and a member of the Strategic Advisory Committee for the Mathematics for Public Health program at the Fields Institute.10019D3ABAB URL: DOI: https://doi.org/10.1101/2023.03.27.23287214
| Excerpt / Summary [medRxiv, 29 March 2023]
The 2022 FIFA World Cup was the first major multi-continental sporting Mass Gathering Event (MGE) of the post COVID-19 era to allow foreign spectators. Such large-scale MGEs can potentially lead to outbreaks of infectious disease and contribute to the global dissemination of such pathogens. Here we adapt previous work and create a generalisable model framework for assessing the use of disease control strategies at such events, in terms of reducing infections and hospitalisations. This framework utilises a combination of meta-populations based on clusters of people and their vaccination status, Ordinary Differential Equation integration between fixed time events, and Latin Hypercube sampling. We use the FIFA 2022 World Cup as a case study for this framework. Pre-travel screenings of visitors were found to have little effect in reducing COVID-19 infections and hospitalisations. With pre-match screenings of spectators and match staff being more effective. Rapid Antigen (RA) screenings 0.5 days before match day outperformed RT-PCR screenings 1.5 days before match day. A combination of pre-travel RT-PCR and pre-match RA testing proved to be the most successful screening-based regime. However, a policy of ensuring that all visitors had a COVID-19 vaccination (second or booster dose) within a few months before departure proved to be much more efficacious. The State of Qatar abandoned all COVID-19 related travel testing and vaccination requirements over the period of the World Cup. Our work suggests that the State of Qatar may have been correct in abandoning the pre-travel testing of visitors. However, there was a spike in COVID-19 cases and hospitalisations within Qatar over the World Cup. The research outlined here suggests a policy requiring visitors to have had a recent COVID-19 vaccination may have prevented the increase in COVID-19 cases and hospitalisations during the world cup. |
Link[130] A distributed digital twin implementation of a hemodialysis unit aimed at helping prevent the spread of the Omicron COVID-19 variant
Author: Jalal Possik, Danielle Azar, Adriano O. Solis, Ali Asgary, Gregory Zacharewicz, Abir Karami, Mohammadali Tofighi, Mahdi Najafabadi, Mohammad A. Shafiee, Asad A. Merchant, Mehdi Aarabi, Jianhong Wu Publication date: 1 November 2022 Publication info: 2022 IEEE/ACM 26th International Symposium on Distributed Simulation and Real Time Applications (DS-RT), 26-28 September 2022 Cited by: David Price 6:54 PM 14 December 2023 GMT Citerank: (4) 679750Ali AsgaryAssociate Professor and Associate Director, Advanced Disaster, Emergency and Rapid Response Simulation (ADERSIM) in the School of Administrative Studies, and Adjunct Professor in the School of Information Technology, at York University.10019D3ABAB, 679812Jianhong WuProfessor Jianhong Wu is a University Distinguished Research Professor and Senior Canada Research Chair in industrial and applied mathematics at York University. He is also the NSERC Industrial Research Chair in vaccine mathematics, modelling, and manufacturing. 10019D3ABAB, 701037MfPH â Publications144B5ACA0, 715391Digital TwinsâA digital twin is a digital model of an intended or actual real-world physical product, system, or process (a physical twin) that serves as the effectively indistinguishable digital counterpart of it for practical purposes, such as simulation, integration, testing, monitoring, and maintenance.â [1]859FDEF6 URL: DOI: https://doi.org/10.1109/DS-RT55542.2022.9932047
| Excerpt / Summary [IEEE/ACM 26th International Symposium on Distributed Simulation and Real Time Applications (DS-RT), 26-28 September 2022]
In order to monitor and assess the spread of the Omicron variant of COVID-19, we propose a Distributed Digital Twin that virtually mirrors a hemodialysis unit in a hospital in Toronto, Canada. Since the solution involves heterogeneous components, we rely on the IEEE HLA distributed simulation standard. Based on the standard, we use an agent-based/discrete event simulator together with a virtual reality environment in order to provide to the medical staff an immersive experience that incorporates a platform showing predictive analytics during a simulation run. This can help professionals monitor the number of exposed, symptomatic, asymptomatic, recovered, and deceased agents. Agents are modeled using a redesigned version of the susceptible-exposed-infected-recovered (SEIR) model. A contact matrix is generated to help identify those agents that increase the risk of the virus transmission within the unit. |
Link[131] Vaccine hesitancy promotes emergence of new SARS-CoV-2 variants
Author: Shuanglin Jing, Russell Milne, Hao Wang, Ling Xue Publication date: 26 May 2023 Publication info: Journal of Theoretical Biology, Volume 570, 2023, 111522, ISSN 0022-5193, 7 August 2023 Cited by: David Price 6:55 PM 14 December 2023 GMT Citerank: (3) 679791Hao WangProfessor in the Department of Mathematical and Statistical Sciences at the University of Alberta.10019D3ABAB, 701037MfPH â Publications144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1016/j.jtbi.2023.111522
| Excerpt / Summary [Journal of Theoretical Biology, 26 May 2023]
The successive emergence of SARS-CoV-2 mutations has led to an unprecedented increase in COVID-19 incidence worldwide. Currently, vaccination is considered to be the best available solution to control the ongoing COVID-19 pandemic. However, public opposition to vaccination persists in many countries, which can lead to increased COVID-19 caseloads and hence greater opportunities for vaccine-evasive mutant strains to arise. To determine the extent that public opinion regarding vaccination can induce or hamper the emergence of new variants, we develop a model that couples a compartmental disease transmission framework featuring two strains of SARS-CoV-2 with game theoretical dynamics on whether or not to vaccinate. We combine semi-stochastic and deterministic simulations to explore the effect of mutation probability, perceived cost of receiving vaccines, and perceived risks of infection on the emergence and spread of mutant SARS-CoV-2 strains. We find that decreasing the perceived costs of being vaccinated and increasing the perceived risks of infection (that is, decreasing vaccine hesitation) will decrease the possibility of vaccine-resistant mutant strains becoming established by about fourfold for intermediate mutation rates. Conversely, we find increasing vaccine hesitation to cause both higher probability of mutant strains emerging and more wild-type cases after the mutant strain has appeared. We also find that once a new variant has emerged, perceived risk of being infected by the original variant plays a much larger role than perceptions of the new variant in determining future outbreak characteristics. Furthermore, we find that rapid vaccination under non-pharmaceutical interventions is a highly effective strategy for preventing new variant emergence, due to interaction effects between non-pharmaceutical interventions and public support for vaccination. Our findings indicate that policies that combine combating vaccine-related misinformation with non-pharmaceutical interventions (such as reducing social contact) will be the most effective for avoiding the establishment of harmful new variants. |
Link[132] COVID-19 in Ontario Long-term Care Facilities Project, a manually curated and validated database
Author: Mahakprit Kaur, Nicola Luigi Bragazzi, Jane Heffernan, Peter Tsasis, Jianhong Wu, Jude Dzevela Kong Publication date: 10 February 2023 Publication info: Frontiers in Public Health, Volume 11, 10 February 2023 Cited by: David Price 6:56 PM 14 December 2023 GMT Citerank: (4) 679806Jane HeffernanJane Heffernan is a professor of infectious disease modelling in the Mathematics & Statistics Department at York University. She is a co-director of the Canadian Centre for Disease Modelling, and she leads national and international networks in mathematical immunology and the modelling of waning and boosting immunity.10019D3ABAB, 679812Jianhong WuProfessor Jianhong Wu is a University Distinguished Research Professor and Senior Canada Research Chair in industrial and applied mathematics at York University. He is also the NSERC Industrial Research Chair in vaccine mathematics, modelling, and manufacturing. 10019D3ABAB, 679815Jude KongDr. Jude Dzevela Kong is an Assistant Professor in the Department of Mathematics and Statistics at York University and the founding Director of the Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC). 10019D3ABAB, 701037MfPH â Publications144B5ACA0 URL: DOI: https://doi.org/10.3389/fpubh.2023.1133419
| Excerpt / Summary [Frontiers in Public Health, 10 February 2023]
In late December 2019, a novel, emerging coronavirus, termed as âSevere Acute Respiratory Syndrome-related Coronavirus Type 2â (SARS-CoV-2) was identified as the infectious agent responsible for the generally mild, but sometimes life-threatening and even fatal âCoronavirus Disease 2019â (COVID-19).
As of December 7, 2021, COVID-19 has imposed a dramatic toll of infections (more than 265 million cases) and deaths (more than 2.5 million deaths).
Long-term care facilities, including nursing homes, residential aged care facilities, retirement homes, skilled nursing facilities and assisted living communities, among others, have represented and still represent healthcare settings particularly vulnerable to the COVID-19 spread (1). For instance, in Canada, residents living in these facilities, being elderly and particularly frail, often with many co-morbidities, have been disproportionately hit by the pandemic, contributing to approximately two thirds (67%) of the entire total toll of deaths (2).
As of December 5, 2021, 11.8% and 7.0% of COVID-19 outbreaks occurred in the Ontario region have affected long-term care facilities and retirement homes, respectively, according to Public Health Ontario (PHO).
A recently published systematic review (3) has identified an array of parameters, including bed size and location in a high SARS-CoV-2 prevalence and mortality area, and number of staff members, as variables predicting COVID-19 related outcomes.
However, in some cases, findings were contrasting, with a number of studies reporting that higher staffing was associated with a higher mortality rate and other investigations obtaining opposite results. Discrepancies in both the direction and magnitude of the effect could be found also for other parameters, such as quality indicators, like star rating, and ownership, or pandemic preparedness indicators, including implementation of public health interventions for controlling and managing prior infections and the number of previous outbreaks occurred in the facility.
Such conflicting findings may depend on the specific nature of the jurisdiction and the setting of each long-term care facility. As such, local data is of paramount importance to inform public health workers, policy- and decision-makers and relevant stakeholders in a data-driven and evidence-based fashion.
Several databases exist, mainly dedicated to (non-pharmaceutical and pharmaceutical) public health interventions (4, 5), underlying biological mechanisms, in terms of pathways and cascades (6), but, to the best of authors' knowledge, no one specifically on long-term care facilities. Specifically, there are websites that provide information for each long-term care home in Ontario such as the location of the home, type of facility, and general statistics pertaining to the care offered. However, the information is limited as the focus of this data is to provide guidance for people looking to send their loved ones to a long-term care home to assist with their daily needs. In contrast, British Columbia has one comprehensive resource curated by Seniors Advocate BC that is sponsored by the province of British Columbia called the Long-Term Care Facilities Quick Facts Directory (7). It contains detailed information regarding the facility, rooms, funding, care offered (e.g., direct care hours), licensing, incidents, resident profiles, and vaccine coverage that is specific to each long-term care home. Since this information is compiled into one reliable resource, it makes it possible for relevant information to be quickly accessed and analyzed. In Ontario, no such counterpart was found. Further, it was difficult to access relevant data that was directly available online. The only publicly available data pertaining to long-term care homes offered by the Ministry of Long-Term Care is data regarding the long-term care home location and data for publicly reported COVID-19 cases (MLTC datasets) (8). The present database was devised and implemented to fill in this gap. |
Link[133] Assessing Inequities in COVID-19 Vaccine Roll-Out Strategy Programs: A Cross-Country Study Using a Machine Learning Approach
Author: Merhdad Kazemi, Nicola Luigi Bragazzi, Jude Dzevela Kong Publication date: 3 September 2021 Publication info: SSRN Electronic Journal, 3 September 2021 Cited by: David Price 6:56 PM 14 December 2023 GMT Citerank: (5) 679815Jude KongDr. Jude Dzevela Kong is an Assistant Professor in the Department of Mathematics and Statistics at York University and the founding Director of the Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC). 10019D3ABAB, 701037MfPH â Publications144B5ACA0, 703953Machine learning859FDEF6, 703965Equity859FDEF6, 704041Vaccination859FDEF6 URL: DOI: http://dx.doi.org/10.2139/ssrn.3914835
| Excerpt / Summary [SSRN, 3 September 2021]
Background: After the start of the COVID-19 pandemic and its spread across the world, countries have adopted containment measures to stop its transmission, limit fatalities and relieve hospitals from strain and overwhelming imposed by the virus. Many countries implemented social distancing and lockdown strategies that negatively impacted their economies and the psychological wellbeing of their citizens, even though they contributed to saving lives. Recently approved and available, COVID-19 vaccines can provide a really viable and sustainable option for controlling the pandemic. However, their uptake represents a global challenge, due to vaccine hesitancy logistic-organizational hurdles that have made its distribution stagnant in several developed countries despite several appeal by the media, policy- and decision-makers, and community leaders. Vaccine distribution is a concern also in developing countries, where there is scarcity of doses.
Objective: To set up a metric to assess vaccination uptake and identify national socio-economic factors influencing this indicator.
Methods: We conducted a cross-country study. We first estimated the vaccination uptake rate across countries by fitting a logistic model to reported daily case numbers. Using the uptake rate, we estimated the vaccine roll-out index. Next, we used Random Forest, an âoff-the-shelfâ machine learning algorithm, to study the association between vaccination uptake rate and socio-economic factors.
Results: We found that the mean vaccine roll-out index is 0.016 (standard deviation 0.016), with a range between 0.0001 (Haiti) and 0.0829 (Mongolia). The top four factors associated with vaccine roll-out index are the median per capita income, human development index, percentage of individuals who have used the internet in the last three months, and health expenditure per capita.
Conclusion: The still ongoing COVID-19 pandemic has shed light on the chronic inequality in global health systems. The disparity in vaccine adoption across low- and high-income countries is a global public health challenge. We must pave the way for a universal access to vaccines and other approved treatments, regardless of demographic structures and underlying health conditions. Income disparity remains, instead, an important cause of vaccine inequity, and the tendency toward "vaccine nationalism" and âvaccine apartheidâ restricts the functioning of the global vaccine allocation framework and, thus, the ending of the pandemic. Stronger mechanisms are needed to foster countries' political willingness to promote vaccine and drug access equity in a globalized society, where future pandemics and other global health rises can be anticipated. |
Link[134] Assessing the epidemiological and economic impact of alternative vaccination strategies: a modeling study
Author: S. Kim, S. Athar, Y. LI, S. Koumarianos, T. Cheng, L. Amiri, W. Avusuglo, W.A. Woldegerima, A.A. Fall, A. John-Baptiste, A. Diener, J. Wu Publication date: 28 February 2022 Publication info: International Journal of Infectious Diseases, 116, S60âS60, March 2022. Cited by: David Price 6:59 PM 14 December 2023 GMT Citerank: (6) 679812Jianhong WuProfessor Jianhong Wu is a University Distinguished Research Professor and Senior Canada Research Chair in industrial and applied mathematics at York University. He is also the NSERC Industrial Research Chair in vaccine mathematics, modelling, and manufacturing. 10019D3ABAB, 686719Alan DienerDr. Diener is the Assistant Director of the Policy Research, Economics and Analytics unit, in the Strategic Policy Branch at Health Canada. Alan received his PhD in economics from McMaster University and he has previously held positions at the University of Nebraska Medical Center, the Public Health Agency of Canada, and the Organisation for Economic Cooperation and Development (OECD) where he was a consultant in the Health Division from 2011 to 2013.10019D3ABAB, 701037MfPH â Publications144B5ACA0, 703957Economics859FDEF6, 704041Vaccination859FDEF6, 715767Woldegebriel Assefa WoldegerimaDr. Woldegerima, knows as "Assefa", is an Assistant Professor at the Department of Mathematics and Statistics at York University.10019D3ABAB URL: DOI: https://doi.org/10.1016/j.ijid.2021.12.142
| Excerpt / Summary [International Journal of Infectious Diseases, 28 February 2022]
Purpose: Given limited supplies of vaccines, having information on the costs, and associated health and economic impacts, is important for the development of optimal vaccination strategies. This study explores the epidemiological and economic impact, in terms of the value of lost production, of four vaccination strategies â fixed-dose interval (M1), prioritization of the first dose (M2), screen and forego vaccine for those with COVID-19 infection history (M3), and prioritization of the first dose along with screen and forego vaccine for those with COVID-19 infection history(M4), under constraints limiting the daily vaccine supply.
Methods & Materials: Using mathematical and statistical modelling, we quantified the number quarantined, hospitalization days, vaccine doses saved, and deaths averted, and production losses, for each strategy, in comparison to M1. The model parameters and initial conditions were based on Canadian data, and the simulation ran over 365 days starting from June 1, 2021. Sensitivity analyses explored how each strategy changes with different conditions of daily vaccine supply, the initial proportion recovered from COVID19 infection, and initial coverage of the first dose.
Results: Strategy M2 results in a reduction of 67,130,775 doses of vaccine administered, 20 lives saved, and a reduction of $3.8 billion of lost production in comparison to M1. M3 does not save any vaccine dose administered, but results in 5 lives saved, and a reduction of $575,149 in lost production in comparison to strategy M1. Due to the large proportion of the Canadian population who have already received a first vaccine dose, no screening actually occurs under scenario M3 and the daily vaccine supply was used entirely to provide second doses. While M2 is the dominant strategy under the current Canadian setting, sensitivity analyses revealed that M3 dominates when the vaccine supply increased or when the initial recovered proportion from COVID-19 was large enough.
Conclusion: The findings quantify the potential benefits of alternative vaccination strategies that can save lives and costs. Our study findings can help policymakers identify the optimal COVID19 vaccination strategy and our study framework can be adapted to other settings. |
Link[135] Law of mass action and saturation in SIR model with application to Coronavirus modelling
Author: Theodore Kolokolnikov, David Iron Publication date: 10 December 2020 Publication info: Infectious Disease Modelling, Volume 6, 2021, Pages 91-97 Cited by: David Price 7:01 PM 14 December 2023 GMT Citerank: (3) 679886Theodore KolokolnikovKillam Professor of Mathematics and Statistics in the Department of Mathematics and Statistics at Dalhousie University.10019D3ABAB, 701037MfPH â Publications144B5ACA0, 701222OMNI â Publications144B5ACA0 URL: DOI: https://doi.org/10.1016/j.idm.2020.11.002
| Excerpt / Summary [Infectious Disease Modelling, 10 December 2020]
When using SIR and related models, it is common to assume that the infection rate is proportional to the product of susceptible and infected individuals. While this assumption works at the onset of the outbreak, the infection force saturates as the outbreak progresses, even in the absence of any interventions. We use a simple agentâbased model to illustrate this saturation effect. Its continuum limit leads a modified SIR model with exponential saturation. The derivation is based on first principles incorporating the spread radius and population density. We use the data for coronavirus outbreak for the period from March to June, to show that using SIR model with saturation is sufficient to capture the disease dynamics for many jurstictions, including the overall world-wide disease curve progression. Our model suggests the R0 value of above 8 at the onset of infection, but with infection quickly âflattening outâ, leading to a long-term sustained sub-exponential spread. |
Link[136] Effectiveness of chatbots on COVID vaccine confidence and acceptance in Thailand, Hong Kong, and Singapore
Author: Kristi Yoonsup Lee, Saudamini Vishwanath Dabak, Vivian Hanxiao Kong, Minah Park, Shirley L. L. Kwok, Madison Silzle, Chayapat Rachatan, Alex Cook, Aly Passanante, Ed Pertwee, Zhengdong Wu, Javier A. Elkin, Heidi J. Larson, Eric H. Y. Lau, Kathy Leung, Joseph T. Wu, Leesa Lin Publication date: 25 May 2023 Publication info: npj Digital Medicine, Volume 6, Article number: 96 (2023) Cited by: David Price 7:01 PM 14 December 2023 GMT Citerank: (2) 701037MfPH â Publications144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1038/s41746-023-00843-6
| Excerpt / Summary [npj Digital Medicine, 25 May 2023]
Chatbots have become an increasingly popular tool in the field of health services and communications. Despite chatbotsâ significance amid the COVID-19 pandemic, few studies have performed a rigorous evaluation of the effectiveness of chatbots in improving vaccine confidence and acceptance. In Thailand, Hong Kong, and Singapore, from February 11th to June 30th, 2022, we conducted multisite randomised controlled trials (RCT) on 2,045 adult guardians of children and seniors who were unvaccinated or had delayed vaccinations. After a week of using COVID-19 vaccine chatbots, the differences in vaccine confidence and acceptance were compared between the intervention and control groups. Compared to non-users, fewer chatbot users reported decreased confidence in vaccine effectiveness in the Thailand child group [Intervention: 4.3 % vs. Control: 17%, Pâ=â0.023]. However, more chatbot users reported decreased vaccine acceptance [26% vs. 12%, Pâ=â0.028] in Hong Kong child group and decreased vaccine confidence in safety [29% vs. 10%, Pâ=â0.041] in Singapore child group. There was no statistically significant change in vaccine confidence or acceptance in the Hong Kong senior group. Employing the RE-AIM framework, process evaluation indicated strong acceptance and implementation support for vaccine chatbots from stakeholders, with high levels of sustainability and scalability. This multisite, parallel RCT study on vaccine chatbots found mixed success in improving vaccine confidence and acceptance among unvaccinated Asian subpopulations. Further studies that link chatbot usage and real-world vaccine uptake are needed to augment evidence for employing vaccine chatbots to advance vaccine confidence and acceptance. |
Link[137] Volatility and heterogeneity of vaccine sentiments means continuous monitoring is needed when measuring message effectiveness
Author: Kathy Leung, Leesa K Lin, Elad Yom-Tov, Karolien Poels, Kristi Lee, Heidi J Larson, Gabriel M Leung, Joseph T Wu Publication date: 27 February 2023 Publication info: Research Square, 27 February 2023 Cited by: David Price 7:15 PM 14 December 2023 GMT Citerank: (2) 701037MfPH â Publications144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.21203/rs.3.rs-2590646/v1
| Excerpt / Summary [Research Square, 27 February 2023]
Background: The success of vaccination programs often depends on the effectiveness of the vaccine messages, particularly during emergencies such as the COVID-19 pandemic. The current suboptimal uptake of COVID-19 vaccines across many parts of the world highlights the tremendous challenges in overcoming vaccine hesitancy and refusal even in the context of a world-devastating pandemic.
Methods: We conducted a randomized controlled trial in Hong Kong to evaluate the impact of seven vaccine messages on COVID-19 vaccine uptake (with the government slogan as the control). The participants included 127,000 individuals who googled COVID-19-related information during July-October 2021.
Results: The impact of vaccine messages on uptake varied substantially over time and among different groups of users. For example, the message that emphasized the indirect protection of vaccination on family members (i) increased overall uptake by 30% (6-59%) in July but had no effect afterwards for English language users; and (ii) had no effect on overall uptake for Chinese language users throughout the study. Such volatility and heterogeneity in message effectiveness highlight the limitations of one-size-fits-all and static vaccine communication.
Conclusions: Epidemic nowcasting should include real-time monitoring of vaccine hesitancy and message effectiveness, in order to adapt messaging appropriately. This dynamic dimension of surveillance has so far been underinvested. |
Link[138] Modelling the impact of timelines of testing and isolation on disease control
Author: Ao Li, Zhen Wang, Seyed M. Moghadas Publication date: 22 December 2022 Publication info: Infectious Disease Modelling, Volume 8, Issue 1, March 2023, Pages 58-71 Cited by: David Price 7:22 PM 14 December 2023 GMT Citerank: (4) 679878Seyed MoghadasSeyed Moghadas is an infectious disease modeller whose research includes mathematical and computational modelling in epidemiology and immunology. In particular, he is interested in the theoretical and computational aspects of mathematical models describing the underlying dynamics of infectious diseases, with a particular emphasis on establishing strong links between micro (individual) and macro (population) levels.10019D3ABAB, 701037MfPH â Publications144B5ACA0, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6, 715831Diagnostic testing859FDEF6 URL: DOI: https://doi.org/10.1016/j.idm.2022.11.008
| Excerpt / Summary [Infectious Disease Modelling, 14 December 2022]
Testing and isolation remain a key component of public health responses to both persistent and emerging infectious diseases. Although the value of these measures have been demonstrated in combating recent outbreaks including the COVID-19 pandemic and monkeypox, their impact depends critically on the timelines of testing and start of isolation during the course of disease. To investigate this impact, we developed a delay differential model and incorporated age-since-symptom-onset as a parameter for delay in testing. We then used the model to compare the outcomes of reverse-transcription polymerase chain reaction (RT-PCR) and rapid antigen (RA) testing methods when isolation starts either at the time of testing or at the time of test result. Parameterizing the model with estimates of SARS-CoV-2 infection and diagnostic sensitivity of the tests, we found that the reduction of disease transmission using the RA test can be comparable to that achieved by applying the RT-PCR test. Given constraints and inevitable delays associated with sample collection and laboratory assays in RT-PCR testing post symptom onset, self-administered RA tests with short turnaround times present a viable alternative for timely isolation of infectious cases. |
Link[139] The effects of disease control measures on the reproduction number of COVID-19 in British Columbia, Canada
Author: Meili Li, Ruijun Zhai, Junling Ma Publication date: 19 June 2023 Publication info: Mathematical Biosciences and Engineering, 20(8), 13849â13863. Cited by: David Price 7:28 PM 14 December 2023 GMT Citerank: (3) 679818Junling MaI am an associate professor in Department of Mathematics and Statistics, University of Victoria. I received B.Sc. in Applied Mathematics in 1994, and M.Sc in Applied Mathematics in 1997, from Xi'an Jiaotong University, China. I received Ph.D. in Applied Mathematics from Princeton University in 2003.10019D3ABAB, 701037MfPH â Publications144B5ACA0, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6 URL: DOI: https://doi.org/10.3934/mbe.2023616
| Excerpt / Summary [Mathematical Biosciences and Engineering, 19 June 2023]
We propose a new method to estimate the change of the effective reproduction number with time, due to either disease control measures or seasonally varying transmission rate. We validate our method using a simulated epidemic curve and show that our method can effectively estimate both sudden changes and gradual changes in the reproduction number. We apply our method to the COVID-19 case counts in British Columbia, Canada in 2020, and we show that strengthening control measures had a significant effect on the reproduction number, while relaxations in May (business reopening) and September (school reopening) had significantly increased the reproduction number from around 1 to around 1.7 at its peak value. Our method can be applied to other infectious diseases, such as pandemics and seasonal influenza. |
Link[140] Adaptive behaviors and vaccination on curbing COVID-19 transmission: Modeling simulations in eight countries
Author: Zhaowan Li, Jianguo Zhao, Yuhao Zhou, Lina Tian, Qihuai Liu, Huaiping Zhu, Guanghu Zhu Publication date: 14 December 2022 Publication info: Journal of Theoretical Biology, Volume 559, 2023, 111379, ISSN 0022-5193, Cited by: David Price 7:30 PM 14 December 2023 GMT Citerank: (3) 679797Huaiping ZhuProfessor of mathematics at the Department of Mathematics and Statistics at York University, a York Research Chair (YRC Tier I) in Applied Mathematics, the Director of the Laboratory of Mathematical Parallel Systems at the York University (LAMPS), the Director of the Canadian Centre for Diseases Modelling (CCDM) and the Director of the One Health Modelling Network for Emerging Infections (OMNI-RĂUNIS). 10019D3ABAB, 701037MfPH â Publications144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1016/j.jtbi.2022.111379
| Excerpt / Summary [Journal of Theoretical Biology, 21 February 2023]
Current persistent outbreak of COVID-19 is triggering a series of collective responses to avoid infection. To further clarify the impact mechanism of adaptive protection behavior and vaccination, we developed a new transmission model via a delay differential system, which parameterized the roles of adaptive behaviors and vaccination, and allowed to simulate the dynamic infection process among people. By validating the model with surveillance data during March 2020 and October 2021 in America, India, South Africa, Philippines, Brazil, UK, Spain and Germany, we quantified the protection effect of adaptive behaviors by different forms of activity function. The modeling results indicated that (1) the adaptive activity function can be used as a good indicator for fitting the intervention outcome, which exhibited short-term awareness in these countries, and it could reduce the total human infections by 3.68, 26.16, 15.23, 4.23, 7.26, 1.65, 5.51 and 7.07 times, compared with the reporting; (2) for complete prevention, the average proportions of people with immunity should be larger than 90%, 92%, 86%, 71%, 92%, 84%, 82% and 76% with adaptive protection behaviors, or 91%, 97%, 94%, 77%, 92%, 88%, 85% and 90% without protection behaviors; and (3) the required proportion of humans being vaccinated is a sub-linear decreasing function of vaccine efficiency, with small heterogeneity in different countries. This manuscript was submitted as part of a theme issue on âModelling COVID-19 and Preparedness for Future Pandemicsâ. |
Link[141] Big data- and artificial intelligence-based hot-spot analysis of COVID-19: Gauteng, South Africa, as a case study
Author: Benjamin Lieberman, Jude Dzevela Kong, Roy Gusinow, Ali Asgary, Nicola Luigi Bragazzi, Joshua Choma, Salah-Eddine Dahbi, Kentaro Hayashi, Deepak Kar, Mary Kawonga, Mduduzi Mbada, Kgomotso Monnakgotla, James Orbinski, Xifeng Ruan, Finn Stevenson, Jianhong Wu, Bruce Mellado Publication date: 26 January 2023 Publication info: BMC Medical Informatics and Decision Making, Volume 23, Article number: 19 (2023) Cited by: David Price 7:32 PM 14 December 2023 GMT Citerank: (5) 679750Ali AsgaryAssociate Professor and Associate Director, Advanced Disaster, Emergency and Rapid Response Simulation (ADERSIM) in the School of Administrative Studies, and Adjunct Professor in the School of Information Technology, at York University.10019D3ABAB, 679812Jianhong WuProfessor Jianhong Wu is a University Distinguished Research Professor and Senior Canada Research Chair in industrial and applied mathematics at York University. He is also the NSERC Industrial Research Chair in vaccine mathematics, modelling, and manufacturing. 10019D3ABAB, 679815Jude KongDr. Jude Dzevela Kong is an Assistant Professor in the Department of Mathematics and Statistics at York University and the founding Director of the Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC). 10019D3ABAB, 701037MfPH â Publications144B5ACA0, 704019Artificial intelligence859FDEF6 URL: DOI: 26 January 2023
| Excerpt / Summary [BMC Medical Informatics and Decision Making, 26 January 2023]
The coronavirus disease 2019 (COVID-19) has developed into a pandemic. Data-driven techniques can be used to inform and guide public health decision- and policy-makers. In generalizing the spread of a virus over a large area, such as a province, it must be assumed that the transmission occurs as a stochastic process. It is therefore very difficult for policy and decision makers to understand and visualize the location specific dynamics of the virus on a more granular level. A primary concern is exposing local virus hot-spots, in order to inform and implement non-pharmaceutical interventions. A hot-spot is defined as an area experiencing exponential growth relative to the generalised growth of the pandemic. This paper uses the first and second waves of the COVID-19 epidemic in Gauteng Province, South Africa, as a case study. The study aims provide a data-driven methodology and comprehensive case study to expose location specific virus dynamics within a given area. The methodology uses an unsupervised Gaussian Mixture model to cluster cases at a desired granularity. This is combined with an epidemiological analysis to quantify each clusterâs severity, progression and whether it can be defined as a hot-spot. |
Link[142] Mitigating co-circulation of seasonal influenza and COVID-19 pandemic in the presence of vaccination: A mathematical modeling approach
Author: Bushra Majeed, Jummy Funke David, Nicola Luigi Bragazzi, Zack McCarthy, Martin David Grunnill, Jane Heffernan, Jianhong Wu, Woldegebriel Assefa Woldegerima Publication date: 4 January 2023 Publication info: Frontiers in Public Health, 4 January 2023 Cited by: David Price 7:37 PM 14 December 2023 GMT Citerank: (6) 679806Jane HeffernanJane Heffernan is a professor of infectious disease modelling in the Mathematics & Statistics Department at York University. She is a co-director of the Canadian Centre for Disease Modelling, and she leads national and international networks in mathematical immunology and the modelling of waning and boosting immunity.10019D3ABAB, 679812Jianhong WuProfessor Jianhong Wu is a University Distinguished Research Professor and Senior Canada Research Chair in industrial and applied mathematics at York University. He is also the NSERC Industrial Research Chair in vaccine mathematics, modelling, and manufacturing. 10019D3ABAB, 701037MfPH â Publications144B5ACA0, 703974Influenza859FDEF6, 704041Vaccination859FDEF6, 715767Woldegebriel Assefa WoldegerimaDr. Woldegerima, knows as "Assefa", is an Assistant Professor at the Department of Mathematics and Statistics at York University.10019D3ABAB URL: DOI: https://doi.org/10.3389/fpubh.2022.1086849
| Excerpt / Summary [Frontiers in Public Health, 4 January 2023]
The co-circulation of two respiratory infections with similar symptoms in a population can significantly overburden a healthcare system by slowing the testing and treatment. The persistent emergence of contagious variants of SARS-CoV-2, along with imperfect vaccines and their waning protections, have increased the likelihood of new COVID-19 outbreaks taking place during a typical flu season. Here, we developed a mathematical model for the co-circulation dynamics of COVID-19 and influenza, under different scenarios of influenza vaccine coverage, COVID-19 vaccine booster coverage and efficacy, and testing capacity. We investigated the required minimal and optimal coverage of COVID-19 booster (third) and fourth doses, in conjunction with the influenza vaccine, to avoid the coincidence of infection peaks for both diseases in a single season. We show that the testing delay brought on by the high number of influenza cases impacts the dynamics of influenza and COVID-19 transmission. The earlier the peak of the flu season and the greater the number of infections with flu-like symptoms, the greater the risk of flu transmission, which slows down COVID-19 testing, resulting in the delay of complete isolation of patients with COVID-19 who have not been isolated before the clinical presentation of symptoms and have been continuing their normal daily activities. Furthermore, our simulations stress the importance of vaccine uptake for preventing infection, severe illness, and hospitalization at the individual level and for disease outbreak control at the population level to avoid putting strain on already weak and overwhelmed healthcare systems. As such, ensuring optimal vaccine coverage for COVID-19 and influenza to reduce the burden of these infections is paramount. We showed that by keeping the influenza vaccine coverage about 35% and increasing the coverage of booster or fourth dose of COVID-19 not only reduces the infections with COVID-19 but also can delay its peak time. If the influenza vaccine coverage is increased to 55%, unexpectedly, it increases the peak size of influenza infections slightly, while it reduces the peak size of COVID-19 as well as significantly delays the peaks of both of these diseases. Mask-wearing coupled with a moderate increase in the vaccine uptake may mitigate COVID-19 and prevent an influenza outbreak. |
Link[143] Extensive SARS-CoV-2 testing reveals BA.1/BA.2 asymptomatic rates and underreporting in school children
Author: Maria M Martignoni, Zahra Mohammadi, J ConcepciĂłn Loredo-Osti, Amy Hurford Publication date: 1 April 2023 Publication info: Can Commun Dis Rep 2023;49(4):155â65. Cited by: David Price 7:39 PM 14 December 2023 GMT Citerank: (5) 679752Amy HurfordAmy Hurford is an Associate Professor jointly appointed in the Department of Biology and the Department of Mathematics and Statistics at Memorial University of Newfoundland and Labrador. 10019D3ABAB, 701037MfPH â Publications144B5ACA0, 701624Zahra MohammadiPostdoctoral Fellow, Mathematics for Public health, Fields Institute, Department of Mathematics and Statistics, University of Guelph, Memorial University of Newfoundland.10019D3ABAB, 715617Schools859FDEF6, 715831Diagnostic testing859FDEF6 URL: DOI: https://doi.org/10.14745/ccdr.v49i04a08
| Excerpt / Summary [Canada Communicable Disease Report, April 2023]
Background: Case underreporting during the coronavirus disease 2019 (COVID-19) pandemic has been a major challenge to the planning and evaluation of public health responses. School children were often considered a less vulnerable population and underreporting rates may have been particularly high. In January 2022, the Canadian province of Newfoundland and Labrador (NL) was experiencing an Omicron variant outbreak (BA.1/BA.2 subvariants) and public health officials recommended that all returning students complete two rapid antigen tests (RATs) to be performed three days apart.
Methods: To estimate the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we asked parents and guardians to report the results of the RATs completed by Kâ12 students (approximately 59,000 students) using an online survey.
Results: When comparing the survey responses with the number of cases and tests reported by the NL testing system, we found that one out of every 4.3 (95% CI, 3.1â5.3) positive households were captured by provincial case count, with 5.1% positivity estimated from the RAT results and 1.2% positivity reported by the provincial testing system. Of positive test results, 62.9% (95% CI, 44.3â83.0) were reported for elementary school students, and the remaining 37.1% (95% CI, 22.7â52.9) were reported for junior high and high school students. Asymptomatic infections were 59.8% of the positive cases. Given the low survey participation rate (3.5%), our results may suffer from sample selection biases and should be interpreted with caution.
Conclusion: The underreporting ratio is consistent with ratios calculated from serology data and provides insights into infection prevalence and asymptomatic infections in school children; a currently understudied population. |
Link[144] Rotational worker vaccination provides indirect protection to vulnerable groups in regions with low COVID-19 prevalence
Author: Maria M. Martignoni, Proton Rahman, Amy Hurford Publication date: 13 December 2021 Publication info: AIMS Mathematics, 2022, Volume 7, Issue 3: 3988-4003. Cited by: David Price 7:41 PM 14 December 2023 GMT Citerank: (4) 679752Amy HurfordAmy Hurford is an Associate Professor jointly appointed in the Department of Biology and the Department of Mathematics and Statistics at Memorial University of Newfoundland and Labrador. 10019D3ABAB, 701037MfPH â Publications144B5ACA0, 704041Vaccination859FDEF6, 715454Workforce impact859FDEF6 URL: DOI: https://doi.org/10.3934/math.2022220
| Excerpt / Summary [AIMS Mathematics, 13 December 2021]
As COVID-19 vaccines become available, different model-based approaches have been developed to evaluate strategic priorities for vaccine allocation to reduce severe illness. One strategy is to directly prioritize groups that are likely to experience medical complications due to COVID-19, such as older adults. A second strategy is to limit community spread by reducing importations, for example by vaccinating members of the mobile labour force, such as rotational workers. This second strategy may be appropriate for regions with low disease prevalence, where importations are a substantial fraction of all cases and reducing the importation rate reduces the risk of community outbreaks, which can provide significant indirect protection for vulnerable individuals. Current studies have focused on comparing vaccination strategies in the absence of importations, and have not considered allocating vaccines to reduce the importation rate. Here, we provide an analytical criteria to compare the reduction in the risk of hospitalization and intensive care unit (ICU) admission over four months when either older adults or rotational workers are prioritized for vaccination. Vaccinating rotational workers (assumed to be 6,000 individuals and about 1% of the Newfoundland and Labrador (NL) population) could reduce the average risk of hospitalization and ICU admission by 42%, if no community spread is observed at the time of vaccination, because epidemic spread is reduced and vulnerable individuals are indirectly protected. In contrast, vaccinating all individuals aged 75 and older (about 43,300 individuals, or 8% of the NL population) would lead to a 24% reduction in the average risk of hospitalization, and to a 45% reduction in the average risk of ICU admission, because a large number of individuals at high risk from COVID-19 are now vaccinated. Therefore, reducing the risk of hospitalization and ICU admission of the susceptible population by reducing case importations would require a significantly lower number of vaccines. Benefits of vaccinating rotational workers decrease with increasing infection prevalence in the community. Prioritizing members of the mobile labour force should be considered as an efficient strategy to indirectly protect vulnerable groups from COVID-19 exposure in regions with low disease prevalence. |
Link[145] Downsizing of COVID-19 contact tracing in highly immune populations
Author: Maria M. Martignoni, Josh Renault, Joseph Baafi, Amy Hurford Publication date: 10 June 2022 Publication info: PLoS ONE 17(6): e0268586 Cited by: David Price 7:42 PM 14 December 2023 GMT Citerank: (3) 679752Amy HurfordAmy Hurford is an Associate Professor jointly appointed in the Department of Biology and the Department of Mathematics and Statistics at Memorial University of Newfoundland and Labrador. 10019D3ABAB, 701037MfPH â Publications144B5ACA0, 715294Contact tracing859FDEF6 URL: DOI: https://doi.org/10.1371/journal.pone.0268586
| Excerpt / Summary [PLoS ONE, 10 June 2022]
Contact tracing is a key component of successful management of COVID-19. Contacts of infected individuals are asked to quarantine, which can significantly slow down (or prevent) community spread. Contact tracing is particularly effective when infections are detected quickly, when contacts are traced with high probability, when the initial number of cases is low, and when social distancing and border restrictions are in place. However, the magnitude of the individual contribution of these factors in reducing epidemic spread and the impact of population immunity (due to either previous infection or vaccination), in determining contact tracing outputs is not fully understood. We present a delayed differential equation model to investigate how the immunity status and the relaxation of social distancing requirements affect contact tracing practices. We investigate how the minimal contact tracing efficiency required to keep an outbreak under control depends on the contact rate and on the proportion of immune individuals. Additionally, we consider how delays in outbreak detection and increased case importation rates affect the number of contacts to be traced daily. We show that in communities that have reached a certain immunity status, a lower contact tracing efficiency is required to avoid a major outbreak, and delayed outbreak detection and relaxation of border restrictions do not lead to a significantly higher risk of overwhelming contact tracing. We find that investing in testing programs, rather than increasing the contact tracing capacity, has a larger impact in determining whether an outbreak will be controllable. This is because early detection activates contact tracing, which will slow, and eventually reverse exponential growth, while the contact tracing capacity is a threshold that will easily become overwhelmed if exponential growth is not curbed. Finally, we evaluate quarantine effectiveness in relation to the immunity status of the population and for different viral variants. We show that quarantine effectiveness decreases with increasing proportion of immune individuals, and increases in the presence of more transmissible variants. These results suggest that a cost-effective approach is to establish different quarantine rules for immune and nonimmune individuals, where rules should depend on viral transmissibility after vaccination or infection. Altogether, our study provides quantitative information for contact tracing downsizing in vaccinated populations or in populations that have already experienced large community outbreaks, to guide COVID-19 exit strategies. |
Link[146] Quantifying the shift in social contact patterns in response to non-pharmaceutical interventions
Author: Zachary McCarthy, Yanyu Xiao, Francesca Scarabel, Biao Tang, Nicola Luigi Bragazzi, Kyeongah Nah, Jane M. Heffernan, Ali Asgary, V. Kumar Murty, Nicholas H. Ogden, Jianhong Wu Publication date: 1 December 2020 Publication info: Journal of Mathematics in Industry, Volume 10, Article number: 28 (2020) Cited by: David Price 7:46 PM 14 December 2023 GMT
Citerank: (9) 679750Ali AsgaryAssociate Professor and Associate Director, Advanced Disaster, Emergency and Rapid Response Simulation (ADERSIM) in the School of Administrative Studies, and Adjunct Professor in the School of Information Technology, at York University.10019D3ABAB, 679806Jane HeffernanJane Heffernan is a professor of infectious disease modelling in the Mathematics & Statistics Department at York University. She is a co-director of the Canadian Centre for Disease Modelling, and she leads national and international networks in mathematical immunology and the modelling of waning and boosting immunity.10019D3ABAB, 679812Jianhong WuProfessor Jianhong Wu is a University Distinguished Research Professor and Senior Canada Research Chair in industrial and applied mathematics at York University. He is also the NSERC Industrial Research Chair in vaccine mathematics, modelling, and manufacturing. 10019D3ABAB, 679893Kumar MurtyProfessor Kumar Murty is in the Department of Mathematics at the University of Toronto. His research fields are Analytic Number Theory, Algebraic Number Theory, Arithmetic Algebraic Geometry and Information Security. He is the founder of the GANITA lab, co-founder of Prata Technologies and PerfectCloud. His interest in mathematics ranges from the pure study of the subject to its applications in data and information security.10019D3ABAB, 701037MfPH â Publications144B5ACA0, 714608Charting a FutureCharting a Future for Emerging Infectious Disease Modelling in Canada â April 2023 [1] 2794CAE1, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6, 715329Nick OgdenNicholas Ogden is a senior research scientist and Director of the Public Health Risk Sciences Division within the National Microbiology Laboratory at the Public Health Agency of Canada.10019D3ABAB, 715617Schools859FDEF6 URL: DOI: https://doi.org/10.1186/s13362-020-00096-y
| Excerpt / Summary [Journal of Mathematics in Industry, 1 December 2020]
Social contact mixing plays a critical role in influencing the transmission routes of infectious diseases. Moreover, quantifying social contact mixing patterns and their variations in a rapidly evolving pandemic intervened by changing public health measures is key for retroactive evaluation and proactive assessment of the effectiveness of different age- and setting-specific interventions. Contact mixing patterns have been used to inform COVID-19 pandemic public health decision-making; but a rigorously justified methodology to identify setting-specific contact mixing patterns and their variations in a rapidly developing pandemic, which can be informed by readily available data, is in great demand and has not yet been established. Here we fill in this critical gap by developing and utilizing a novel methodology, integrating social contact patterns derived from empirical data with a disease transmission model, that enables the usage of age-stratified incidence data to infer age-specific susceptibility, daily contact mixing patterns in workplace, household, school and community settings; and transmission acquired in these settings under different physical distancing measures. We demonstrated the utility of this methodology by performing an analysis of the COVID-19 epidemic in Ontario, Canada. We quantified the age- and setting (household, workplace, community, and school)-specific mixing patterns and their evolution during the escalation of public health interventions in Ontario, Canada. We estimated a reduction in the average individual contact rate from 12.27 to 6.58 contacts per day, with an increase in household contacts, following the implementation of control measures. We also estimated increasing trends by age in both the susceptibility to infection by SARS-CoV-2 and the proportion of symptomatic individuals diagnosed. Inferring the age- and setting-specific social contact mixing and key age-stratified epidemiological parameters, in the presence of evolving control measures, is critical to inform decision- and policy-making for the current COVID-19 pandemic. |
Link[147] Importation models for travel-related SARS-CoV-2 cases reported in Newfoundland and Labrador during the COVID-19 pandemic
Author: Zahra Mohammadi, Monica Cojocaru, Julien Arino, Amy Hurford Publication date: 12 June 2023 Publication info: medRxiv 2023.06.08.23291136 Cited by: David Price 7:46 PM 14 December 2023 GMT
Citerank: (7) 679752Amy HurfordAmy Hurford is an Associate Professor jointly appointed in the Department of Biology and the Department of Mathematics and Statistics at Memorial University of Newfoundland and Labrador. 10019D3ABAB, 679817Julien ArinoProfessor and Faculty of Science Research Chair in Fundamental Science with the Department of Mathematics at the University of Manitoba.10019D3ABAB, 701037MfPH â Publications144B5ACA0, 701148Implementation of mobility restrictionsThe implementation of mobility restrictions, in combination with vaccination and non-pharmaceutical interventions, to meet the needs of small communities during a pandemic.859FDEF6, 701624Zahra MohammadiPostdoctoral Fellow, Mathematics for Public health, Fields Institute, Department of Mathematics and Statistics, University of Guelph, Memorial University of Newfoundland.10019D3ABAB, 703963Mobility859FDEF6, 715762Monica CojocaruProfessor in the Mathematics & Statistics Department at the University of Guelph. 10019D3ABAB URL: DOI: https://doi.org/10.1101/2023.06.08.23291136
| Excerpt / Summary [medRxiv, 12 June 2023]
During the COVID-19 pandemic there was substantial variation between countries in the severity of the travel restrictions implemented suggesting a need for better importation models. Data to evaluate the accuracy of importation models is available for the Canadian province of Newfoundland and Labrador (NL; September 2020 to June 2021) as arriving travelers were frequently tested for SARS-CoV-2 and travel-related cases were reported. Travel volume to NL was estimated from flight data, and travel declaration forms completed at entry to Canada, and at entry to NL during the pandemic. We found that during the pandemic travel to NL decreased by 82%, the percentage of travelers arriving from QuĂ©bec decreased (from 14 to 4%), and from Alberta increased (from 7 to 17%). We derived and validated an epidemiological model predicting the number of travelers testing positive for SARS-CoV-2 after arrival in NL, but found that statistical models with less description of SARS-CoV-2 epidemiology, and with parameters fitted from the validation data more accurately predicted the daily number of travel-related cases reported in NL originating from Canada (R2 = 0.55, ÎAICc = 137). Our results highlight the importance of testing travelers and reporting travel-related cases as these data are needed for importation models to support public health decisions. |
Link[148] Human behaviour, NPI and mobility reduction effects on COVID-19 transmission in different countries of the world
Author: Zahra Mohammadi, Monica Gabriela Cojocaru, Edward Wolfgang Thommes Publication date: 22 August 2022 Publication info: BMC Public Health volume 22, Article number: 1594 (2022) Cited by: David Price 7:47 PM 14 December 2023 GMT
Citerank: (9) 701037MfPH â Publications144B5ACA0, 701624Zahra MohammadiPostdoctoral Fellow, Mathematics for Public health, Fields Institute, Department of Mathematics and Statistics, University of Guelph, Memorial University of Newfoundland.10019D3ABAB, 703963Mobility859FDEF6, 704036Immunology859FDEF6, 704041Vaccination859FDEF6, 704045Covid-19859FDEF6, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6, 715419Edward Thommes Edward W. Thommes is an Adjunct Professor of Mathematics at the University of Guelph and at York University. He is a Global Modeling Lead in the Modeling, Epidemiology and Data Science (MEDS) team of Sanofi Vaccines, an Affiliate Researcher in the Waterloo Institute for Complexity and Innovation (WICI), and a member of the Strategic Advisory Committee for the Mathematics for Public Health program at the Fields Institute.10019D3ABAB, 715762Monica CojocaruProfessor in the Mathematics & Statistics Department at the University of Guelph. 10019D3ABAB URL: DOI: https://doi.org/10.1186/s12889-022-13921-3
| Excerpt / Summary [BMC Public Health, 22 August 2022]
Background: The outbreak of Coronavirus disease, which originated in Wuhan, China in 2019, has affected the lives of billions of people globally. Throughout 2020, the reproduction number of COVID-19 was widely used by decision-makers to explain their strategies to control the pandemic.
Methods: In this work, we deduce and analyze both initial and effective reproduction numbers for 12 diverse world regions between February and December of 2020. We consider mobility reductions, mask wearing and compliance with masks, mask efficacy values alongside other non-pharmaceutical interventions (NPIs) in each region to get further insights in how each of the above factored into each regionâs SARS-COV-2 transmission dynamic.
Results: We quantify in each region the following reductions in the observed effective reproduction numbers of the pandemic: i) reduction due to decrease in mobility (as captured in Google mobility reports); ii) reduction due to mask wearing and mask compliance; iii) reduction due to other NPIâs, over and above the ones identified in i) and ii).
Conclusion: In most cases mobility reduction coming from nationwide lockdown measures has helped stave off the initial wave in countries who took these types of measures. Beyond the first waves, mask mandates and compliance, together with social-distancing measures (which we refer to as other NPIâs) have allowed some control of subsequent disease spread. The methodology we propose here is novel and can be applied to other respiratory diseases such as influenza or RSV. |
Link[149] Human behaviour, NPI and mobility reduction effects on COVID-19 transmission in different countries of the world
Author: Zahra Mohammadi, Monica Gabriela Cojocaru, Edward Wolfgang Thommes Publication date: 22 August 2022 Publication info: BMC Public Health volume 22, Article number: 1594 (2022) Cited by: David Price 7:48 PM 14 December 2023 GMT
Citerank: (9) 701037MfPH â Publications144B5ACA0, 701624Zahra MohammadiPostdoctoral Fellow, Mathematics for Public health, Fields Institute, Department of Mathematics and Statistics, University of Guelph, Memorial University of Newfoundland.10019D3ABAB, 703963Mobility859FDEF6, 704036Immunology859FDEF6, 704041Vaccination859FDEF6, 704045Covid-19859FDEF6, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6, 715419Edward Thommes Edward W. Thommes is an Adjunct Professor of Mathematics at the University of Guelph and at York University. He is a Global Modeling Lead in the Modeling, Epidemiology and Data Science (MEDS) team of Sanofi Vaccines, an Affiliate Researcher in the Waterloo Institute for Complexity and Innovation (WICI), and a member of the Strategic Advisory Committee for the Mathematics for Public Health program at the Fields Institute.10019D3ABAB, 715762Monica CojocaruProfessor in the Mathematics & Statistics Department at the University of Guelph. 10019D3ABAB URL: DOI: https://doi.org/10.1186/s12889-022-13921-3
| Excerpt / Summary [BMC Public Health, 22 August 2022]
Background: The outbreak of Coronavirus disease, which originated in Wuhan, China in 2019, has affected the lives of billions of people globally. Throughout 2020, the reproduction number of COVID-19 was widely used by decision-makers to explain their strategies to control the pandemic.
Methods: In this work, we deduce and analyze both initial and effective reproduction numbers for 12 diverse world regions between February and December of 2020. We consider mobility reductions, mask wearing and compliance with masks, mask efficacy values alongside other non-pharmaceutical interventions (NPIs) in each region to get further insights in how each of the above factored into each regionâs SARS-COV-2 transmission dynamic.
Results: We quantify in each region the following reductions in the observed effective reproduction numbers of the pandemic: i) reduction due to decrease in mobility (as captured in Google mobility reports); ii) reduction due to mask wearing and mask compliance; iii) reduction due to other NPIâs, over and above the ones identified in i) and ii).
Conclusion: In most cases mobility reduction coming from nationwide lockdown measures has helped stave off the initial wave in countries who took these types of measures. Beyond the first waves, mask mandates and compliance, together with social-distancing measures (which we refer to as other NPIâs) have allowed some control of subsequent disease spread. The methodology we propose here is novel and can be applied to other respiratory diseases such as influenza or RSV. |
Link[150] Identifying Vaccine-hesitant Subgroups in the Western Pacific: A Latent Class Analysis
Author: Yongjin Choi, Kathy Leung, Joseph Wu, Leesa Lin, Heidi Larson Publication date: 4 May 2023 Publication info: Research Square, 4 May 2023 Cited by: David Price 7:52 PM 14 December 2023 GMT Citerank: (2) 701037MfPH â Publications144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.21203/rs.3.rs-2702702/v1
| Excerpt / Summary [Research Square, 4 May 2023]
Background: Vaccine hesitancy has seriously compromised the COVID-19 vaccine roll-out across the Western Pacific; nevertheless, evidence-based recommendations that account for the heterogeneity of vaccine-hesitant populations in this region remain lacking. To help design customized vaccine communication strategies, we sought to investigate the profile of the vaccine-hesitant populations in Cambodia, Japan, Lao PDR, Malaysia, Mongolia, Papua New Guinea, Philippines, Republic of Korea, and Viet Nam.
Methods: Using 16,408 survey responses from an international survey distributed in 2021 and 2022, we identified hidden subgroups by conducting latent class analysis (LCA) and examined their vaccine acceptance and booster uptake by using Ordinary Least Square (OLS) regressions.
Findings: Our LCA approach identified six classes: college students, distrusters of health care providers (HCPs), stay-at-home mothers, the elderly, compliant pragmatists, and general working population. Booster uptake were significantly low in two groups: college students [13 percentage points; 95% CI -0.21 to -0.05] and HCP distrusters [8 percentage points; 95% CI -0.15 to -0.01]; these groupsâ acceptance were also similarly low. Stay-at-home mothersâ acceptance and uptake were comparable, but this group took a large portion of vaccine-hesitant people in the Philippines. The profiles of the vaccine-hesitant populations in each country were compared and categorized into four groups, depending on the composition of classes that account for the unvaccination population.
Interpretation: The results of this study suggest that drivers of vaccine hesitancy may vary by country and indicate that each country needs a customized strategy that reflects the profile of its vaccine-hesitant population. The proposed recommendations for each country can identify the target population for designing effective vaccine communication strategies. |
Link[151] A cross-country analysis of macroeconomic responses to COVID-19 pandemic using Twitter sentiments
Author: Zahra Movahedi Nia, Ali Ahmadi, Nicola L. Bragazzi, Woldegebriel Assefa Woldegerima, Bruce Mellado, Jianhong Wu, James Orbinski, Ali Asgary, Jude Dzevela Kong Publication date: 24 August 2022 Publication info: PLOS ONE, 17(8), e0272208 Cited by: David Price 7:57 PM 14 December 2023 GMT
Citerank: (7) 679750Ali AsgaryAssociate Professor and Associate Director, Advanced Disaster, Emergency and Rapid Response Simulation (ADERSIM) in the School of Administrative Studies, and Adjunct Professor in the School of Information Technology, at York University.10019D3ABAB, 679812Jianhong WuProfessor Jianhong Wu is a University Distinguished Research Professor and Senior Canada Research Chair in industrial and applied mathematics at York University. He is also the NSERC Industrial Research Chair in vaccine mathematics, modelling, and manufacturing. 10019D3ABAB, 679815Jude KongDr. Jude Dzevela Kong is an Assistant Professor in the Department of Mathematics and Statistics at York University and the founding Director of the Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC). 10019D3ABAB, 701037MfPH â Publications144B5ACA0, 703957Economics859FDEF6, 715666Social networks859FDEF6, 715767Woldegebriel Assefa WoldegerimaDr. Woldegerima, knows as "Assefa", is an Assistant Professor at the Department of Mathematics and Statistics at York University.10019D3ABAB URL: DOI: https://doi.org/10.1371/journal.pone.0272208
| Excerpt / Summary [PLOS ONE, 24 August 2022]
The COVID-19 pandemic has had a devastating impact on the global economy. In this paper, we use the Phillips curve to compare and analyze the macroeconomics of three different countries with distinct income levels, namely, lower-middle (Nigeria), upper-middle (South Africa), and high (Canada) income. We aim to (1) find macroeconomic changes in the three countries during the pandemic compared to pre-pandemic time, (2) compare the countries in terms of response to the COVID-19 economic crisis, and (3) compare their expected economic reaction to the COVID-19 pandemic in the near future. An advantage to our work is that we analyze macroeconomics on a monthly basis to capture the shocks and rapid changes caused by on and off rounds of lockdowns. We use the volume and social sentiments of the Twitter data to approximate the macroeconomic statistics. We apply four different machine learning algorithms to estimate the unemployment rate of South Africa and Nigeria on monthly basis. The results show that at the beginning of the pandemic the unemployment rate increased for all the three countries. However, Canada was able to control and reduce the unemployment rate during the COVID-19 pandemic. Nonetheless, in line with the Phillips curve short-run, the inflation rate of Canada increased to a level that has never occurred in more than fifteen years. Nigeria and South Africa have not been able to control the unemployment rate and did not return to the pre-COVID-19 level. Yet, the inflation rate has increased in both countries. The inflation rate is still comparable to the pre-COVID-19 level in South Africa, but based on the Phillips curve short-run, it will increase further, if the unemployment rate decreases. Unfortunately, Nigeria is experiencing a horrible stagflation and a wild increase in both unemployment and inflation rates. This shows how vulnerable lower-middle-income countries could be to lockdowns and economic restrictions. In the near future, the main concern for all the countries is the high inflation rate. This work can potentially lead to more targeted and publicly acceptable policies based on social media content. |
Link[152] A wastewater-based epidemic model for SARS-CoV-2 with application to three Canadian cities
Author: Shokoofeh Nourbakhsh, Aamir Fazil, Michael Li, Chand S. Mangat, Shelley W. Peterson, Jade Daigle, Stacie Langner, Jayson Shurgold, Patrick DâAoust, Robert Delatolla, Elizabeth Mercier, Xiaoli Pang, Bonita E. Lee, Rebecca Stuart, Shinthuja Wijayasri, David Champredon Publication date: 21 April 2022 Publication info: Epidemics, Volume 39, June 2022, 100560, ISSN 1755-4365, Cited by: David Price 8:00 PM 14 December 2023 GMT Citerank: (5) 685445Michael WZ LiMichael Li is Senior Scientist in the Public Health Risk Science Division (PHRS) of the Public Health Agency of Canada (PHAC) and a Research Associate at the South African Centre for Epidemiological Modelling and Analysis (SACEMA).10019D3ABAB, 701037MfPH â Publications144B5ACA0, 704022Surveillance859FDEF6, 708744Wastewater-based surveillance (WBS) 859FDEF6, 715283David ChampredonDr. David Champredon is a senior scientist at the Public Health Agency of Canada. His work focuses on modelling the spread of infectious diseases at the population level, especially respiratory and sexually transmitted infections. During the past two years, he supported the modelling efforts to respond to the COVID-19 pandemic, particularly wastewater-based modelling.10019D3ABAB URL: DOI: https://doi.org/10.1016/j.epidem.2022.100560
| Excerpt / Summary [Epidemics, 21 April 2022]
The COVID-19 pandemic has stimulated wastewater-based surveillance, allowing public health to track the epidemic by monitoring the concentration of the genetic fingerprints of SARS-CoV-2 shed in wastewater by infected individuals. Wastewater-based surveillance for COVID-19 is still in its infancy. In particular, the quantitative link between clinical cases observed through traditional surveillance and the signals from viral concentrations in wastewater is still developing and hampers interpretation of the data and actionable public-health decisions. We present a modelling framework that includes both SARS-CoV-2 transmission at the population level and the fate of SARS-CoV-2 RNA particles in the sewage system after faecal shedding by infected persons in the population. Using our mechanistic representation of the combined clinical/wastewater system, we perform exploratory simulations to quantify the effect of surveillance effectiveness, public-health interventions and vaccination on the discordance between clinical and wastewater signals. We also apply our model to surveillance data from three Canadian cities to provide wastewater-informed estimates for the actual prevalence, the effective reproduction number and incidence forecasts. We find that wastewater-based surveillance, paired with this model, can complement clinical surveillance by supporting the estimation of key epidemiological metrics and hence better triangulate the state of an epidemic using this alternative data source. |
Link[153] Under-reporting of COVID-19 in the Northern Health Authority region of British Columbia
Author: Matthew R. P. Parker, Yangming Li, Lloyd T. Elliott, Junling Ma, Laura L. E. Cowen Publication date: 1 November 2021 Publication info: Canadian Journal of Statistics, Volume 49, Issue 4 p. 1018-1038 Cited by: David Price 8:01 PM 14 December 2023 GMT Citerank: (4) 679826Laura CowenAssociate Professor in the Department of Mathematics and Statistics at the University of Victoria.10019D3ABAB, 701037MfPH â Publications144B5ACA0, 7147033e N-mixture (hidden Markov) type models123AECCD8, 715254Lloyd T. ElliottAssistant Professor, Statistics and Actuarial Science at Simon Fraser University.10019D3ABAB URL: DOI: https://doi.org/10.1002/cjs.11664
| Excerpt / Summary [Canadian Journal of Statistics, 1 November 2021]
Asymptomatic and pauci-symptomatic presentations of COVID-19 along with restrictive testing protocols result in undetected COVID-19 cases. Estimating undetected cases is crucial to understanding the true severity of the outbreak. We introduce a new hierarchical disease dynamics model based on the N-mixtures hidden population framework. The new models make use of three sets of disease count data per region: reported cases, recoveries and deaths. Treating the first two as under-counted through binomial thinning, we model the true population state at each time point by partitioning the diseased population into the active, recovered and died categories. Both domestic spread and imported cases are considered. These models are applied to estimate the level of under-reporting of COVID-19 in the Northern Health Authority region of British Columbia, Canada, during 30 weeks of the provincial recovery plan. Parameter covariates are easily implemented and used to improve model estimates. We compare two distinct methods of model-fitting for this case study: (1) maximum likelihood estimation, and (2) Bayesian Markov chain Monte Carlo. The two methods agreed exactly in their estimates of under-reporting rate. When accounting for changes in weekly testing volumes, we found under-reporting rates varying from 60.2% to 84.2%. |
Link[154] Estimation of Epidemiological Parameters and Ascertainment Rate from Early Transmission of COVID-19 across Africa
Author: Qing Han, Nicola Luigi Bragazzi, Ali Asgary, James Orbinski, Jianhong Wu, Jude Dzevela Kong Publication date: 6 July 2022 Publication info: SSRN, 6 July 2023 Cited by: David Price 8:05 PM 14 December 2023 GMT Citerank: (4) 679750Ali AsgaryAssociate Professor and Associate Director, Advanced Disaster, Emergency and Rapid Response Simulation (ADERSIM) in the School of Administrative Studies, and Adjunct Professor in the School of Information Technology, at York University.10019D3ABAB, 679812Jianhong WuProfessor Jianhong Wu is a University Distinguished Research Professor and Senior Canada Research Chair in industrial and applied mathematics at York University. He is also the NSERC Industrial Research Chair in vaccine mathematics, modelling, and manufacturing. 10019D3ABAB, 679815Jude KongDr. Jude Dzevela Kong is an Assistant Professor in the Department of Mathematics and Statistics at York University and the founding Director of the Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC). 10019D3ABAB, 701037MfPH â Publications144B5ACA0 URL: DOI: http://dx.doi.org/10.2139/ssrn.4135496
| Excerpt / Summary [SSRN, 6 July 2023]
Country reported case counts suggested a slow spread of SARS-CoV-2 in the initial phase of the COVID-19 pandemic in Africa. However, due to inadequate public awareness, unestablished monitoring practices, limited testing, ineffective diagnosis, stigmas attached to being infected with SARS-CoV-2, self-medication, and the use of complementary/alternative medicine that are common among Africans for social, economic, and psychological reasons, there might exist extensive under-ascertainment and therefore an underestimation of the true number of cases, especially at the beginning of the novel epidemic. We developed a compartmentalized epidemiological model based on an augmented susceptible-exposed-infectious-recovered (SEIR) model to track the early epidemics in 54 African countries. Data on the reported cumulative number of cases and daily confirmed cases were used to fit the model for the time period with no or little massive national interventions yet in each country. We estimated that the mean basic reproduction number is 2.02 (SD 0.7), with a range between 1.12 (Zambia) and 3.64 (Nigeria), whereas the mean basic reproduction number for observed cases was estimated to be 0.17 (SD 0.17), with a range between 0 (Sao Tome and Principe, Seychelles, Tanzania, South Sudan, Mozambique, Liberia, Togo) and 0.68 (South Africa). It was estimated that the mean overall report rate is 5.37% (SD 5.71%), with the highest 30.41% in Libya and the lowest 0.02% in Sao Tome and Principe. An average of 5.46% (SD 6.4%) of all infected cases were severe cases and 66.74% (SD 17.28%) were asymptomatic ones, with Libya having the most (39.45%) fraction of severe cases and Togo the most (97.38%) fraction of asymptomatic cases. The estimated low reporting rates in Africa suggested a clear need for improved reporting and surveillance system in these countries. |
Link[155] Recursive Zero-COVID model and quantitation of control efforts of the Omicron epidemic in Jilin province
Author: Xinmiao Rong, Huidi Chu, Liu Yang, Shaosi Tan, Chao Yang, Pei Yuan, Yi Tan, Linhua Zhou, Yawen Liu, Qing Zhen, Shishen Wang, Meng Fan, Huaiping Zhu Publication date: 13 December 2022 Publication info: Infectious Disease Modelling, Volume 8, Issue 1, 2023, Pages 11-26, ISSN 2468-0427 Cited by: David Price 8:06 PM 14 December 2023 GMT Citerank: (3) 679797Huaiping ZhuProfessor of mathematics at the Department of Mathematics and Statistics at York University, a York Research Chair (YRC Tier I) in Applied Mathematics, the Director of the Laboratory of Mathematical Parallel Systems at the York University (LAMPS), the Director of the Canadian Centre for Diseases Modelling (CCDM) and the Director of the One Health Modelling Network for Emerging Infections (OMNI-RĂUNIS). 10019D3ABAB, 701037MfPH â Publications144B5ACA0, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6 URL: DOI: https://doi.org/10.1016/j.idm.2022.11.007
| Excerpt / Summary [Infectious Disease Modelling, 13 December 2022]
Since the beginning of March 2022, the epidemic due to the Omicron variant has developed rapidly in Jilin Province. To figure out the key controlling factors and validate the model to show the success of the Zero-COVID policy in the province, we constructed a Recursive Zero-COVID Model quantifying the strength of the control measures, and defined the control reproduction number as an index for describing the intensity of interventions. Parameter estimation and sensitivity analysis were employed to estimate and validate the impact of changes in the strength of different measures on the intensity of public health preventions qualitatively and quantitatively. The recursive Zero-COVID model predicted that the dates of elimination of cases at the community level of Changchun and Jilin Cities to be on April 8 and April 17, respectively, which are consistent with the real situation. Our results showed that the strict implementation of control measures and adherence of the public are crucial for controlling the epidemic. It is also essential to strengthen the control intensity even at the final stage to avoid the rebound of the epidemic. In addition, the control reproduction number we defined in the paper is a novel index to measure the intensity of the prevention and control measures of public health. |
Link[156] Asymptomatic SARS-CoV-2 infection: A systematic review and meta-analysis
Author: Pratha Sah, Meagan C. Fitzpatrick, Charlotte F. Zimmer, Elaheh Abdollahi, Lyndon Juden-Kelly, Seyed M. Moghadas, Burton H. Singer, Alison P. Galvani Publication date: 10 August 2021 Publication info: PNAS, 118 (34) e2109229118 Cited by: David Price 8:08 PM 14 December 2023 GMT Citerank: (3) 679878Seyed MoghadasSeyed Moghadas is an infectious disease modeller whose research includes mathematical and computational modelling in epidemiology and immunology. In particular, he is interested in the theoretical and computational aspects of mathematical models describing the underlying dynamics of infectious diseases, with a particular emphasis on establishing strong links between micro (individual) and macro (population) levels.10019D3ABAB, 701037MfPH â Publications144B5ACA0, 715294Contact tracing859FDEF6 URL: DOI: https://doi.org/10.1073/pnas.2109229118
| Excerpt / Summary [PNAS, 10 August 2021]
Quantification of asymptomatic infections is fundamental for effective public health responses to the COVID-19 pandemic. Discrepancies regarding the extent of asymptomaticity have arisen from inconsistent terminology as well as conflation of index and secondary cases which biases toward lower asymptomaticity. We searched PubMed, Embase, Web of Science, and World Health Organization Global Research Database on COVID-19 between January 1, 2020 and April 2, 2021 to identify studies that reported silent infections at the time of testing, whether presymptomatic or asymptomatic. Index cases were removed to minimize representational bias that would result in overestimation of symptomaticity. By analyzing over 350 studies, we estimate that the percentage of infections that never developed clinical symptoms, and thus were truly asymptomatic, was 35.1% (95% CI: 30.7 to 39.9%). At the time of testing, 42.8% (95% prediction interval: 5.2 to 91.1%) of cases exhibited no symptoms, a group comprising both asymptomatic and presymptomatic infections. Asymptomaticity was significantly lower among the elderly, at 19.7% (95% CI: 12.7 to 29.4%) compared with children at 46.7% (95% CI: 32.0 to 62.0%). We also found that cases with comorbidities had significantly lower asymptomaticity compared to cases with no underlying medical conditions. Without proactive policies to detect asymptomatic infections, such as rapid contact tracing, prolonged efforts for pandemic control may be needed even in the presence of vaccination. |
Link[157] Implications of suboptimal COVID-19 vaccination coverage in Florida and Texas
Author: Pratha Sah, Seyed M Moghadas, Thomas N Vilches, Affan Shoukat, Burton H Singer, Peter J Hotez, Eric C Schneider, Alison P Galvani Publication date: 7 October 2021 Publication info: The Lancet Infectious Diseases, VOLUME 21, ISSUE 11, P1493-1494, NOVEMBER 2021 Cited by: David Price 8:09 PM 14 December 2023 GMT Citerank: (3) 679878Seyed MoghadasSeyed Moghadas is an infectious disease modeller whose research includes mathematical and computational modelling in epidemiology and immunology. In particular, he is interested in the theoretical and computational aspects of mathematical models describing the underlying dynamics of infectious diseases, with a particular emphasis on establishing strong links between micro (individual) and macro (population) levels.10019D3ABAB, 701037MfPH â Publications144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1016/S1473-3099(21)00620-4
| Excerpt / Summary [The Lancet Infectious Diseases, 7 October 2021]
In July, 2021, another wave of COVID-19 began in the USA as the highly infectious delta (B.1.617.2) SARS-CoV-2 variant drove outbreaks predominantly affecting states with relatively low vaccination coverage. Some US states have shown the feasibility of rapidly achieving high vaccination coverage. Specifically, an average of 74·0% of adults had been fully vaccinated in Vermont, Connecticut, Massachusetts, Maine, and Rhode Island by July 31. By contrast, two states facing substantial delta-driven surges, Florida and Texas, had fully vaccinated only 59·5% and 55·8% of their adult residents, respectively.1 Here, we estimate the deaths, hospital admissions, and infections that could have been averted if Florida and Texas had matched the average vaccination pace of the top-performing states and vaccinated 74·0% of their adult populations by the end of July. |
Link[158] Return on Investment of the COVID-19 Vaccination Campaign in New York City
Author: Pratha Sah, Thomas N. Vilches, Seyed M. Moghadas, Abhishek Pandey, Suhas Gondi, Eric C. Schneider, Jesse Singer, Dave A. Chokshi, Alison P. Galvani Publication date: 21 November 2022 Publication info: JAMA Network Open, 21 November 2022, 2022;5(11):e2243127 Cited by: David Price 8:09 PM 14 December 2023 GMT Citerank: (3) 679878Seyed MoghadasSeyed Moghadas is an infectious disease modeller whose research includes mathematical and computational modelling in epidemiology and immunology. In particular, he is interested in the theoretical and computational aspects of mathematical models describing the underlying dynamics of infectious diseases, with a particular emphasis on establishing strong links between micro (individual) and macro (population) levels.10019D3ABAB, 701037MfPH â Publications144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1001/jamanetworkopen.2022.43127
| Excerpt / Summary [JAMA Network Open, 21 November 2022]
Importance: New York City, an early epicenter of the pandemic, invested heavily in its COVID-19 vaccination campaign to mitigate the burden of disease outbreaks. Understanding the return on investment (ROI) of this campaign would provide insights into vaccination programs to curb future COVID-19 outbreaks.
Objective: To estimate the ROI of the New York City COVID-19 vaccination campaign by estimating the tangible direct and indirect costs from a societal perspective.
Design, Setting, and Participants: This decision analytical model of disease transmission was calibrated to confirmed and probable cases of COVID-19 in New York City between December 14, 2020, and January 31, 2022. This simulation model was validated with observed patterns of reported hospitalizations and deaths during the same period.
Exposures: An agent-based counterfactual scenario without vaccination was simulated using the calibrated model.
Main Outcomes and Measures: Costs of health care and deaths were estimated in the actual pandemic trajectory with vaccination and in the counterfactual scenario without vaccination. The savings achieved by vaccination, which were associated with fewer outpatient visits, emergency department visits, emergency medical services, hospitalizations, and intensive care unit admissions, were also estimated. The value of a statistical life (VSL) lost due to COVID-19 death and the productivity loss from illness were accounted for in calculating the ROI.
Results: During the study period, the vaccination campaign averted an estimated $27.96 (95% credible interval [CrI], $26.19-$29.84) billion in health care expenditures and 315âŻ724 (95% CrI, 292âŻ143-340âŻ420) potential years of life lost, averting VSL loss of $26.27 (95% CrI, $24.39-$28.21) billion. The estimated net savings attributable to vaccination were $51.77 (95% CrI, $48.50-$55.85) billion. Every $1 invested in vaccination yielded estimated savings of $10.19 (95% CrI, $9.39-$10.87) in direct and indirect costs of health outcomes that would have been incurred without vaccination.
Conclusions and Relevance: Results of this modeling study showed an association of the New York City COVID-19 vaccination campaign with reduction in severe outcomes and avoidance of substantial economic losses. This significant ROI supports continued investment in improving vaccine uptake during the ongoing pandemic. |
Link[159] Estimating the impact of vaccination on reducing COVID-19 burden in the United States: December 2020 to March 2022
Author: Pratha Sah, Thomas N. Vilches, Abhishek Pandey, Eric C. Schneider, Seyed M. Moghadas, Alison P Galvani Publication date: 1 September 2022 Publication info: J Glob Health 2022;12:03062. Cited by: David Price 8:10 PM 14 December 2023 GMT Citerank: (3) 679878Seyed MoghadasSeyed Moghadas is an infectious disease modeller whose research includes mathematical and computational modelling in epidemiology and immunology. In particular, he is interested in the theoretical and computational aspects of mathematical models describing the underlying dynamics of infectious diseases, with a particular emphasis on establishing strong links between micro (individual) and macro (population) levels.10019D3ABAB, 701037MfPH â Publications144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.7189/jogh.12.03062
| Excerpt / Summary [Journal of Global Health, September 2022]
Since the start of COVID-19 vaccination in the United States (US), over 560 million doses of authorized vaccines were administered, and 69.7% of the eligible population were fully vaccinated as of March 31, 2022 [1]. Much attention has focused on the public health toll of the pandemic. The positive impact of the rapid development and deployment of highly efficacious vaccines, ie, the reduction in deaths, hospitalizations, and health care costs, remains unclear. We estimated the reduction in COVID-19 cases, hospitalizations and mortality, as well as averted health care costs achieved by the vaccination program from December 12, 2020 to March 31, 2022. |
Link[160] Emergency Calls in the City of Vaughan (Canada) During the COVID-19 Pandemic: A Spatiotemporal Analysis
Author: Ali Asgary, Adriano O. Solis, Nawar Khan, Janithra Wimaladasa, Maryam S. Sabet Publication date: 23 March 2023 Publication info: Polytechnic University of Valencia Congress, CARMA 2022 - 4th International Conference on Advanced Research Methods and Analytics Cited by: David Price 8:11 PM 14 December 2023 GMT Citerank: (3) 679750Ali AsgaryAssociate Professor and Associate Director, Advanced Disaster, Emergency and Rapid Response Simulation (ADERSIM) in the School of Administrative Studies, and Adjunct Professor in the School of Information Technology, at York University.10019D3ABAB, 701037MfPH â Publications144B5ACA0, 703960Spatio-temporal analysis859FDEF6 URL: DOI: https://doi.org/10.4995/carma2022.2022.15087
| Excerpt / Summary [CARMA 2022]
The COVID-19 pandemic has required governments to introduce various public health measures in order to contain and manage the pandemicâs unprecedented impacts in terms of illnesses and deaths. This study analyzes the spatiotemporal distribution of emergency incidents in Vaughan, a medium-sized city in the Canadian province of Ontario, comparing occurrences prior to and during the pandemic. Emergency calls received and responded to by the Vaughan Fire and Rescue Service were examined using spatial density and emerging hotspot analysis based on 11 periods of various public health measures and restrictions set in place from 17 March 2020 to 15 July 2021, as compared with corresponding pre-pandemic periods in the preceding three years (2017-2019). The resulting analyses show significant spatiotemporal changes in emergency incident patterns, particularly during periods of more stringent public health measures such as âstay at homeâ orders or lockdowns of nonessential business establishments. Results of the study could provide useful insights for managing emergency service resources and operations during public health emergencies. |
Link[161] Modeling the second outbreak of COVID-19 with isolation and contact tracing
Author: Haitao Song, Fang Liu, Feng Li, Xiaochun Cao, Hao Wang, Zhongwei Jia, Huaiping Zhu, Michael Y. Li, Wei Lin, Hong Yang, Jianghong Hu, Zhen Jin Publication date: 1 October 2022 Publication info: Discrete & Continuous Dynamical Systems - B, 2022, Volume 27, Issue 10: 5757-5777. Cited by: David Price 8:12 PM 14 December 2023 GMT Citerank: (5) 679791Hao WangProfessor in the Department of Mathematical and Statistical Sciences at the University of Alberta.10019D3ABAB, 679797Huaiping ZhuProfessor of mathematics at the Department of Mathematics and Statistics at York University, a York Research Chair (YRC Tier I) in Applied Mathematics, the Director of the Laboratory of Mathematical Parallel Systems at the York University (LAMPS), the Director of the Canadian Centre for Diseases Modelling (CCDM) and the Director of the One Health Modelling Network for Emerging Infections (OMNI-RĂUNIS). 10019D3ABAB, 685387Michael Y LiProfessor of Mathematics in the Department of Mathematical and Statistical Sciences at the University of Alberta, and Director of the Information Research Lab (IRL).10019D3ABAB, 701037MfPH â Publications144B5ACA0, 715294Contact tracing859FDEF6 URL: DOI: https://doi.org/10.3934/dcdsb.2021294
| Excerpt / Summary [Discrete & Continuous Dynamical Systems - B, October 2022]
The first case of Corona Virus Disease 2019 (COVID-19) was reported in Wuhan, China in December 2019. Since then, COVID-19 has quickly spread out to all provinces in China and over 150 countries or territories in the world. With the first level response to public health emergencies (FLRPHE) launched over the country, the outbreak of COVID-19 in China is achieving under control in China. We develop a mathematical model based on the epidemiology of COVID-19, incorporating the isolation of healthy people, confirmed cases and contact tracing measures. We calculate the basic reproduction numbers 2.5 in China (excluding Hubei province) and 2.9 in Hubei province with the initial time on January 30 which shows the severe infectivity of COVID-19, and verify that the current isolation method effectively contains the transmission of COVID-19. Under the isolation of healthy people, confirmed cases and contact tracing measures, we find a noteworthy phenomenon that is the second epidemic of COVID-19 and estimate the peak time and value and the cumulative number of cases. Simulations show that the contact tracing measures can efficiently contain the transmission of the second epidemic of COVID-19. With the isolation of all susceptible people or all infectious people or both, there is no second epidemic of COVID-19. Furthermore, resumption of work and study can increase the transmission risk of the second epidemic of COVID-19. |
Link[162] Managing SARS-CoV-2 Testing in Schools with an Artificial Intelligence Model and Application Developed by Simulation Data
Author: Svetozar Zarko Valtchev, Ali Asgary, Michael Chen, Felippe A. Cronemberger, Mahdi M. Najafabadi, Monica Gabriela Cojocaru, Jianhong Wu Publication date: 7 July 2021 Publication info: Electronics, 10(14), 1626â1626. Cited by: David Price 8:12 PM 14 December 2023 GMT
Citerank: (7) 679750Ali AsgaryAssociate Professor and Associate Director, Advanced Disaster, Emergency and Rapid Response Simulation (ADERSIM) in the School of Administrative Studies, and Adjunct Professor in the School of Information Technology, at York University.10019D3ABAB, 679812Jianhong WuProfessor Jianhong Wu is a University Distinguished Research Professor and Senior Canada Research Chair in industrial and applied mathematics at York University. He is also the NSERC Industrial Research Chair in vaccine mathematics, modelling, and manufacturing. 10019D3ABAB, 701037MfPH â Publications144B5ACA0, 704019Artificial intelligence859FDEF6, 708812Simulation859FDEF6, 715617Schools859FDEF6, 715762Monica CojocaruProfessor in the Mathematics & Statistics Department at the University of Guelph. 10019D3ABAB URL: DOI: https://doi.org/10.3390/electronics10141626
| Excerpt / Summary [Electronics, 7 July 2021]
Research on SARS-CoV-2 and its social implications have become a major focus to interdisciplinary teams worldwide. As interest in more direct solutions, such as mass testing and vaccination grows, several studies appear to be dedicated to the operationalization of those solutions, leveraging both traditional and new methodologies, and, increasingly, the combination of both. This research examines the challenges anticipated for preventative testing of SARS-CoV-2 in schools and proposes an artificial intelligence (AI)-powered agent-based model crafted specifically for school scenarios. This research shows that in the absence of real data, simulation-based data can be used to develop an artificial intelligence model for the application of rapid assessment of school testing policies. |
Link[163] The stochasticity in adherence to nonpharmaceutical interventions and booster doses and the mitigation of COVID-19
Author: Yi Tan, Pei Yuan, Iain Moyles, Jane Heffernan, James Watmough, Sanyi Tang, Huaiping Zhu Publication date: 1 March 2023 Publication info: Discrete and Continuous Dynamical Systems - S, 2023, Volume 16, Issue 3&4: 602-626. Cited by: David Price 8:15 PM 14 December 2023 GMT Citerank: (6) 679797Huaiping ZhuProfessor of mathematics at the Department of Mathematics and Statistics at York University, a York Research Chair (YRC Tier I) in Applied Mathematics, the Director of the Laboratory of Mathematical Parallel Systems at the York University (LAMPS), the Director of the Canadian Centre for Diseases Modelling (CCDM) and the Director of the One Health Modelling Network for Emerging Infections (OMNI-RĂUNIS). 10019D3ABAB, 679799Iain MoylesAssistant Professor in the Department of Mathematics and Statistics at York University. 10019D3ABAB, 679805James WatmoughProfessor in the Department of Mathematics and Statistics at the University of New Brunswick.10019D3ABAB, 679806Jane HeffernanJane Heffernan is a professor of infectious disease modelling in the Mathematics & Statistics Department at York University. She is a co-director of the Canadian Centre for Disease Modelling, and she leads national and international networks in mathematical immunology and the modelling of waning and boosting immunity.10019D3ABAB, 701037MfPH â Publications144B5ACA0, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6 URL: DOI: https://doi.org/10.3934/dcdss.2023044
| Excerpt / Summary [Discrete and Continuous Dynamical Systems - S, March 2023]
Facing the more contagious COVID-19 variant, Omicron, nonpharmaceutical interventions (NPIs) were still in place and booster doses were proposed to mitigate the epidemic. However, the uncertainty and stochasticity in individuals' behaviours toward the NPIs and booster dose increase, and how this randomness affects the transmission remains poorly understood. We present a model framework to incorporate demographic stochasticity and two kinds of environmental stochasticity (notably variations in adherence to NPIs and booster dose acceptance) to analyze the effects of different forms of stochasticity on transmission. The model is calibrated using the data from December 31, 2021, to March 8, 2022, on daily reported cases and hospitalizations, cumulative cases, deaths and vaccinations for booster doses in Toronto, Canada. An approximate Bayesian computational (ABC) method is used for calibration. We observe that demographic stochasticity could dramatically worsen the outbreak with more incidence compared with the results of the corresponding deterministic model. We found that large variations in adherence to NPIs increase infections. The randomness in booster dose acceptance will not affect the number of reported cases significantly and it is acceptable in the mitigation of COVID-19. The stochasticity in adherence to NPIs needs more attention compared to booster dose hesitancy. |
Link[164] The minimal COVID-19 vaccination coverage and efficacy to compensate for a potential increase of transmission contacts, and increased transmission probability of the emerging strains
Author: Biao Tang, Xue Zhang, Qian Li, Nicola Luigi Bragazzi, Dasantila Golemi-Kotra, Jianhong Wu Publication date: 27 June 2022 Publication info: BMC Public Health, Volume 22, Article number: 1258 (2022) Cited by: David Price 8:16 PM 14 December 2023 GMT Citerank: (3) 679812Jianhong WuProfessor Jianhong Wu is a University Distinguished Research Professor and Senior Canada Research Chair in industrial and applied mathematics at York University. He is also the NSERC Industrial Research Chair in vaccine mathematics, modelling, and manufacturing. 10019D3ABAB, 701037MfPH â Publications144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1186/s12889-022-13429-w
| Excerpt / Summary [BMC Public Health, 27 June 2022]
Background: Mass immunization is a potentially effective approach to finally control the local outbreak and global spread of the COVID-19 pandemic. However, it can also lead to undesirable outcomes if mass vaccination results in increased transmission of effective contacts and relaxation of other public health interventions due to the perceived immunity from the vaccine.
Methods: We designed a mathematical model of COVID-19 transmission dynamics that takes into consideration the epidemiological status, public health intervention status (quarantined/isolated), immunity status of the population, and strain variations. Comparing the control reproduction numbers and the final epidemic sizes (attack rate) in the cases with and without vaccination, we quantified some key factors determining when vaccination in the population is beneficial for preventing and controlling future outbreaks.
Results: Our analyses predicted that there is a critical (minimal) vaccine efficacy rate (or a critical quarantine rate) below which the control reproduction number with vaccination is higher than that without vaccination, and the final attack rate in the population is also higher with the vaccination. We also predicted the worst case scenario occurs when a high vaccine coverage rate is achieved for a vaccine with a lower efficacy rate and when the vaccines increase the transmission efficient contacts.
Conclusions: The analyses show that an immunization program with a vaccine efficacy rate below the predicted critical values will not be as effective as simply investing in the contact tracing/quarantine/isolation implementation. We reached similar conclusions by considering the final epidemic size (or attack rates). This research then highlights the importance of monitoring the impact on transmissibility and vaccine efficacy of emerging strains. |
Link[165] Harnessing Artificial Intelligence to assess the impact of nonpharmaceutical interventions on the second wave of the Coronavirus Disease 2019 pandemic across the world
Author: Sile Tao, Nicola Luigi Bragazzi, Jianhong Wu, Bruce Mellado, Jude Dzevela Kong Publication date: 18 January 2022 Publication info: Scientific Reports, Volume 12, Article number: 944 (2022) Cited by: David Price 8:16 PM 14 December 2023 GMT Citerank: (5) 679812Jianhong WuProfessor Jianhong Wu is a University Distinguished Research Professor and Senior Canada Research Chair in industrial and applied mathematics at York University. He is also the NSERC Industrial Research Chair in vaccine mathematics, modelling, and manufacturing. 10019D3ABAB, 679815Jude KongDr. Jude Dzevela Kong is an Assistant Professor in the Department of Mathematics and Statistics at York University and the founding Director of the Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC). 10019D3ABAB, 701037MfPH â Publications144B5ACA0, 704019Artificial intelligence859FDEF6, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6 URL: DOI: https://doi.org/10.1038/s41598-021-04731-5
| Excerpt / Summary [Scientific Reports, 18 January 2022]
In the present paper, we aimed to determine the influence of various non-pharmaceutical interventions (NPIs) enforced during the first wave of COVID-19 across countries on the spreading rate of COVID-19 during the second wave. For this purpose, we took into account national-level climatic, environmental, clinical, health, economic, pollution, social, and demographic factors. We estimated the growth of the first and second wave across countries by fitting a logistic model to daily-reported case numbers, up to the first and second epidemic peaks. We estimated the basic and effective (second wave) reproduction numbers across countries. Next, we used a random forest algorithm to study the association between the growth rate of the second wave and NPIs as well as pre-existing country-specific characteristics. Lastly, we compared the growth rate of the first and second waves of COVID-19. The top three factors associated with the growth of the second wave were body mass index, the number of days that the government sets restrictions on requiring facial coverings outside the home at all times, and restrictions on gatherings of 10 people or less. Artificial intelligence techniques can help scholars as well as decision and policy-makers estimate the effectiveness of public health policies, and implement âsmartâ interventions, which are as efficacious as stringent ones. |
Link[166] Modelling COVID-19 transmission in a hemodialysis centre using simulation generated contacts matrices
Author: Mohammadali Tofighi, Ali Asgary, Asad A. Merchant, Mohammad Ali Shafiee, Mahdi M. Najafabadi, Nazanin Nadri, Mehdi Aarabi, Jane Heffernan, Jianhong Wu Publication date: 19 November 2021 Publication info: PLoS ONE 16(11): e0259970. Cited by: David Price 8:18 PM 14 December 2023 GMT
Citerank: (7) 679750Ali AsgaryAssociate Professor and Associate Director, Advanced Disaster, Emergency and Rapid Response Simulation (ADERSIM) in the School of Administrative Studies, and Adjunct Professor in the School of Information Technology, at York University.10019D3ABAB, 679806Jane HeffernanJane Heffernan is a professor of infectious disease modelling in the Mathematics & Statistics Department at York University. She is a co-director of the Canadian Centre for Disease Modelling, and she leads national and international networks in mathematical immunology and the modelling of waning and boosting immunity.10019D3ABAB, 679812Jianhong WuProfessor Jianhong Wu is a University Distinguished Research Professor and Senior Canada Research Chair in industrial and applied mathematics at York University. He is also the NSERC Industrial Research Chair in vaccine mathematics, modelling, and manufacturing. 10019D3ABAB, 685420Hospitals16289D5D4, 701037MfPH â Publications144B5ACA0, 708812Simulation859FDEF6, 715294Contact tracing859FDEF6 URL: DOI: https://doi.org/10.1371/journal.pone.0259970
| Excerpt / Summary [PLoS ONE, 19 November 2021]
The COVID-19 pandemic has been particularly threatening to patients with end-stage kidney disease (ESKD) on intermittent hemodialysis and their care providers. Hemodialysis patients who receive life-sustaining medical therapy in healthcare settings, face unique challenges as they need to be at a dialysis unit three or more times a week, where they are confined to specific settings and tended to by dialysis nurses and staff with physical interaction and in close proximity. Despite the importance and critical situation of the dialysis units, modelling studies of the SARS-CoV-2 spread in these settings are very limited. In this paper, we have used a combination of discrete event and agent-based simulation models, to study the operations of a typical large dialysis unit and generate contact matrices to examine outbreak scenarios. We present the details of the contact matrix generation process and demonstrate how the simulation calculates a micro-scale contact matrix comprising the number and duration of contacts at a micro-scale time step. We have used the contacts matrix in an agent-based model to predict disease transmission under different scenarios. The results show that micro-simulation can be used to estimate contact matrices, which can be used effectively for disease modelling in dialysis and similar settings. |
Link[167] COVID-19 Hospitalizations, ICU Admissions and Deaths Associated with the New Variants of Concern
Author: Ashleigh R. Tuite, David N. Fisman, Ayodele Odutayo, et al., on behalf of the Ontario COVID-19 Science Advisory Table - Pavlos Bobos, Vanessa Allen, Isaac I. Bogoch, Adalsteinn D. Brown, Gerald A. Evans, Anna Greenberg, Jessica Hopkins, Antonina Maltsev, Douglas G. Manuel, Allison McGeer, Andrew M. Morris, Samira Mubareka, Laveena Munshi, V. Kumar Murty, Samir N. Patel, Fahad Razak, Robert J. Reid, Beate Sander, Michael Schull, Brian Schwartz, Arthur S. Slutsky, Nathan M. Stall, Peter JĂŒni Publication date: 29 March 2021 Publication info: [Science Briefs of the Ontario COVID-19 Science Advisory Table, 2021;1(18) Cited by: David Price 8:21 PM 14 December 2023 GMT
Citerank: (11) 679746Steini BrownProfessor and Dean of the Dalla Lana School of Public Health at the University of Toronto.10019D3ABAB, 679755Ashleigh TuiteAshleigh Tuite is an Assistant Professor in the Epidemiology Division at the Dalla Lana School of Public Health at the University of Toronto.10019D3ABAB, 679757Beate SanderCanada Research Chair in Economics of Infectious Diseases and Director, Health Modeling & Health Economics and Population Health Economics Research at THETA (Toronto Health Economics and Technology Assessment Collaborative).10019D3ABAB, 679777David FismanI am a Professor in the Division of Epidemiology at Division of Epidemiology, Dalla Lana School of Public Health at the University of Toronto. I am a Full Member of the School of Graduate Studies. I also have cross-appointments at the Institute of Health Policy, Management and Evaluation and the Department of Medicine, Faculty of Medicine. I serve as a Consultant in Infectious Diseases at the University Health Network.10019D3ABAB, 679802Isaac BogochClinician Investigator, Toronto General Hospital Research Institute (TGHRI)10019D3ABAB, 679893Kumar MurtyProfessor Kumar Murty is in the Department of Mathematics at the University of Toronto. His research fields are Analytic Number Theory, Algebraic Number Theory, Arithmetic Algebraic Geometry and Information Security. He is the founder of the GANITA lab, co-founder of Prata Technologies and PerfectCloud. His interest in mathematics ranges from the pure study of the subject to its applications in data and information security.10019D3ABAB, 685230Doug ManuelDr. Manuel is a Medical Doctor with a Masters in Epidemiology and Royal College specialization in Public Health and Preventive Medicine. He is a Senior Scientist in the Clinical Epidemiology Program at Ottawa Hospital Research Institute, and a Professor in the Departments of Family Medicine and Epidemiology and Community Medicine.10019D3ABAB, 685420Hospitals16289D5D4, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 701037MfPH â Publications144B5ACA0, 715390Mortality859FDEF6 URL: DOI: https://doi.org/10.47326/ocsat.2021.02.18.1.0
| Excerpt / Summary [Science Briefs of the Ontario COVID-19 Science Advisory Table, 29 March 2021]
Background: As of March 28, 2021 new variants of concern (VOCs) account for 67% of all Ontario SARS-CoV-2 infections. The B.1.1.7 variant originally detected in Kent, United Kingdom accounts for more than 90% of all VOCs in Ontario, with emerging evidence that it is both more transmissible and virulent.
Questions: What are the risks of COVID-19 hospitalization, ICU admission and death caused by VOCs as compared with the early variants of SARS-CoV-2?
What is the early impact of new VOCs on Ontarioâs healthcare system?
Findings: A retrospective cohort study of 26,314 people in Ontario testing positive for SARS-CoV-2 between February 7 and March 11, 2021, showed that 9,395 people (35.7%) infected with VOCs had a 62% relative increase in COVID-19 hospitalizations (odds ratio [OR] 1.62, 95% confidence interval [CI] 1.41 to 1.87), a 114% relative increase in ICU admissions (OR 2.14, 95% CI 1.52 to 3.02), and a 40% relative increase in COVID-19 deaths (OR 1.40, 95% CI 1.01 to 1.94), after adjusting for age, sex and comorbidities.
A meta-analysis including the Ontario cohort study and additional cohort studies in the United Kingdom and Denmark showed that people infected with VOCs had a 63% higher risk of hospitalization (RR 1.63, 95% CI 1.44 to 1.83), a doubling of the risk of ICU admission (RR 2.03, 95% CI 1.69 to 2.45), and a 56% higher risk of all-cause death (RR 1.56, 95% CI 1.30 to 1.87). Estimates observed in different studies and regions were completely consistent, and the B.1.1.7 variant was dominant in all three jurisdictions over the study periods.
The number of people hospitalized with COVID-19 on March 28, 2021, is 21% higher than at the start of the province-wide lockdown during the second wave on December 26, 2020, while ICU occupancy is 28% higher.
Between December 14 to 20, 2020, there were 149 new admissions to ICU; people aged 59 years and younger accounted for 30% of admissions. Between March 15, 2021 and March 21, 2021, there were 157 new admissions to ICU; people aged 59 years and younger accounted for 46% of admissions.
Interpretation: The new VOCs will result in a considerably higher burden to Ontarioâs health care system during the third wave compared to the impact of early SARS-CoV-2 variants during Ontarioâs second wave.
Since the start of the third wave on March 1, 2021, the number of new cases of SARS-CoV-2 infection, and the COVID-19 hospital and ICU occupancies have surpassed prior thresholds at the start of the province-wide lockdown on December 26, 2020. |
Link[168] Impact of non-pharmaceutical interventions and vaccination on COVID-19 outbreaks in Nunavut, Canada: a Canadian Immunization Research Network (CIRN) study
Author: Thomas N. Vilches, Elaheh Abdollahi, Lauren E. Cipriano, Margaret Haworth-Brockman, Yoav Keynan, Holden Sheffield, Joanne M. Langley, Seyed M. Moghadas Publication date: 25 May 2022 Publication info: BMC Public Health, Volume 22, Article number: 1042 (2022) Cited by: David Price 8:24 PM 14 December 2023 GMT Citerank: (3) 679878Seyed MoghadasSeyed Moghadas is an infectious disease modeller whose research includes mathematical and computational modelling in epidemiology and immunology. In particular, he is interested in the theoretical and computational aspects of mathematical models describing the underlying dynamics of infectious diseases, with a particular emphasis on establishing strong links between micro (individual) and macro (population) levels.10019D3ABAB, 701037MfPH â Publications144B5ACA0, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6 URL: DOI: https://doi.org/10.1186/s12889-022-13432-1
| Excerpt / Summary [BMC Public Health, 25 May 2022]
Background: Nunavut, the northernmost Arctic territory of Canada, experienced three community outbreaks of the coronavirus disease 2019 (COVID-19) from early November 2020 to mid-June 2021. We sought to investigate how non-pharmaceutical interventions (NPIs) and vaccination affected the course of these outbreaks.
Methods: We used an agent-based model of disease transmission to simulate COVID-19 outbreaks in Nunavut. The model encapsulated demographics and household structure of the population, the effect of NPIs, and daily number of vaccine doses administered. We fitted the model to inferred, back-calculated infections from incidence data reported from October 2020 to June 2021. We then compared the fit of the scenario based on case count data with several counterfactual scenarios without the effect of NPIs, without vaccination, and with a hypothetical accelerated vaccination program whereby 98% of the vaccine supply was administered to eligible individuals.
Results: We found that, without a territory-wide lockdown during the first COVID-19 outbreak in November 2020, the peak of infections would have been 4.7 times higher with a total of 5,404 (95% CrI: 5,015â5,798) infections before the start of vaccination on January 6, 2021. Without effective NPIs, we estimated a total of 4,290 (95% CrI: 3,880â4,708) infections during the second outbreak under the pace of vaccination administered in Nunavut. In a hypothetical accelerated vaccine rollout, the total infections during the second Nunavut outbreak would have been 58% lower, to 1,812 (95% CrI: 1,593â2,039) infections. Vaccination was estimated to have the largest impact during the outbreak in April 2021, averting 15,196 (95% CrI: 14,798â15,591) infections if the disease had spread through Nunavut communities. Accelerated vaccination would have further reduced the total infections to 243 (95% CrI: 222â265) even in the absence of NPIs.
Conclusions: NPIs have been essential in mitigating pandemic outbreaks in this large, geographically distanced and remote territory. While vaccination has the greatest impact to prevent infection and severe outcomes, public health implementation of NPIs play an essential role in the short term before attaining high levels of immunity in the population. |
Link[169] Estimating COVID-19 Infections, Hospitalizations, and Deaths Following the US Vaccination Campaigns During the Pandemic
Author: Thomas N. Vilches, Seyed M. Moghadas, Pratha Sah, Meagan C. Fitzpatrick, Affan Shoukat, Abhishek Pandey, Alison P. Galvani Publication date: 11 January 2022 Publication info: JAMA Network Open. 2022;5(1):e2142725. Cited by: David Price 8:24 PM 14 December 2023 GMT Citerank: (4) 679878Seyed MoghadasSeyed Moghadas is an infectious disease modeller whose research includes mathematical and computational modelling in epidemiology and immunology. In particular, he is interested in the theoretical and computational aspects of mathematical models describing the underlying dynamics of infectious diseases, with a particular emphasis on establishing strong links between micro (individual) and macro (population) levels.10019D3ABAB, 685420Hospitals16289D5D4, 701037MfPH â Publications144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1001/jamanetworkopen.2021.42725
| Excerpt / Summary [JAMA Network Open, 11 January 2022]
Introduction: The COVID-19 pandemic has caused more than 745âŻ000 deaths in the US. However, the toll might have been higher without the rapid development and delivery of effective vaccines. As of October 28, 2021, 69% of 258 million US adults had been fully vaccinated.
Quantifying the population impact of COVID-19 vaccination can inform future vaccination strategies. Randomized clinical trials have established individual-level efficacy of authorized vaccines against the original strain, which exceeds 90% in preventing symptomatic and severe disease.1-3 However, the population-level effectiveness of the vaccination campaign in the US, in terms of association with reduced infections, hospitalizations, and deaths, is not as well documented, and we evaluated this using a simulation model.
Methods: This decision analytic model adheres to Consolidated Health Economic Evaluation Reporting Standards (CHEERS) reporting guideline. The institutional review of this study was waived by York University for the use of publicly available, deidentified data of the COVID-19 infections, deaths, and vaccination. Informed consent was not required to access the data.
We expanded our previous agent-based model4 to include transmission dynamics of the Alpha (B.1.1.7), Gamma (P.1), and Delta (B.1.617.2) variants in addition to the original strain (eMethods in the Supplement). The model was parameterized with the US demographics and age-specific risks of severe COVID-19 outcomes (eTable 1 and eTable 2 in the Supplement).5 A 2-dose vaccination strategy was implemented based on the daily vaccines administered in different age groups.6 Vaccine efficacies against infection, symptomatic disease and severe disease after each dose and for each variant were derived from published estimates (eTable 3 in the Supplement). The model was calibrated and fitted to reported national level incidence from October 1, 2020, to June 30, 2021 (eMethods in the Supplement).
We simulated pandemic trajectory under 2 counterfactuals: a no vaccination scenario and a program that achieved only half the daily vaccination rate of actual rollout. For each scenario, cumulative infections, hospitalizations, and deaths were compared with the simulated trends under the US vaccination program.
Credible intervals (CrIs) were generated from simulation outputs using the bias-corrected and accelerated bootstrap method (with 500 replications) in June 2021. The model was implemented in Julia Language Programming, version 1.6 (Julia), and outputs were analyzed in MATLAB, version 2017a (MathWorks). No significance tests were performed for this simulation study.
Results: Compared with the no vaccination scenario, the actual vaccination campaign saved an estimated 240âŻ797 (95% CrI, 200âŻ665-281âŻ230) lives and prevented an estimated 1âŻ133âŻ617 (95% CrI, 967âŻ487-1âŻ301âŻ881) hospitalizations from December 12, 2020, to June 30, 2021. The number of cases averted during the same period was projected to exceed 14 million. Vaccination prevented a wave of COVID-19 cases driven by the Alpha variant that would have occurred in April 2021 without vaccination (Figure 1), with a projected peak of 4409 (95% CrI, 2865-6312) deaths and 17âŻ979 (95% CrI, 13âŻ191-23âŻ219) hospitalizations. Under the second counterfactual with daily vaccination rates at half the reported pace, we projected that the US would have still endured an additional 77âŻ283 (95% CrI, 48âŻ499-104âŻ519) deaths and 336âŻ000 (95% CrI, 225âŻ330- 440âŻ109) hospitalizations (Figure 2).
Discussion: Our analytical model suggested that the US COVID-19 vaccination program was associated with a reduction in the total hospitalizations and deaths by nearly half during the first 6 months of 2021. It was also associated with decreased impact of the more transmissible and lethal Alpha variant that was circulating during the same period. As new variants of SARS-CoV-2 continue to emerge, a renewed commitment to vaccine access, particularly among underserved groups and in counties with low vaccination coverage, will be crucial to preventing avoidable COVID-19 cases and bringing the pandemic to a close.
Limitations of our model included the use of reported cases for fitting, which may not reflect the true incidence. This fit does not completely match the temporal trends of reported hospitalizations and deaths. The model was nationally homogeneous; however, parameters may have varied across geographic regions. Furthermore, we did not consider waning immunity after vaccination or recovery within the study time frame. |
Link[170] Economic evaluation of COVID-19 rapid antigen screening programs in the workplace
Author: Thomas N. Vilches, Ellen Rafferty, Chad R. Wells, Alison P. Galvani, Seyed M. Moghadas Publication date: 23 November 2022 Publication info: BMC Medicine, Volume 20, Article number: 452 (2022) Cited by: David Price 8:25 PM 14 December 2023 GMT Citerank: (6) 679878Seyed MoghadasSeyed Moghadas is an infectious disease modeller whose research includes mathematical and computational modelling in epidemiology and immunology. In particular, he is interested in the theoretical and computational aspects of mathematical models describing the underlying dynamics of infectious diseases, with a particular emphasis on establishing strong links between micro (individual) and macro (population) levels.10019D3ABAB, 701037MfPH â Publications144B5ACA0, 703957Economics859FDEF6, 704041Vaccination859FDEF6, 704045Covid-19859FDEF6, 715831Diagnostic testing859FDEF6 URL: DOI: https://doi.org/10.1186/s12916-022-02641-5
| Excerpt / Summary [BMC Medicine, 23 November 2022]
Background: Diagnostic testing has been pivotal in detecting SARS-CoV-2 infections and reducing transmission through the isolation of positive cases. We quantified the value of implementing frequent, rapid antigen (RA) testing in the workplace to identify screening programs that are cost-effective.
Methods: To project the number of cases, hospitalizations, and deaths under alternative screening programs, we adapted an agent-based model of COVID-19 transmission and parameterized it with the demographics of Ontario, Canada, incorporating vaccination and waning of immunity. Taking into account healthcare costs and productivity losses associated with each program, we calculated the incremental cost-effectiveness ratio (ICER) with quality-adjusted life year (QALY) as the measure of effect. Considering RT-PCR testing of only severe cases as the baseline scenario, we estimated the incremental net monetary benefits (iNMB) of the screening programs with varying durations and initiation times, as well as different booster coverages of working adults.
Results: Assuming a willingness-to-pay threshold of CDN$30,000 per QALY loss averted, twice weekly workplace screening was cost-effective only if the program started early during a surge. In most scenarios, the iNMB of RA screening without a confirmatory RT-PCR or RA test was comparable or higher than the iNMB for programs with a confirmatory test for RA-positive cases. When the program started early with a duration of at least 16 weeks and no confirmatory testing, the iNMB exceeded CDN$1.1 million per 100,000 population. Increasing booster coverage of working adults improved the iNMB of RA screening.
Conclusions: Our findings indicate that frequent RA testing starting very early in a surge, without a confirmatory test, is a preferred screening program for the detection of asymptomatic infections in workplaces. |
Link[171] Economic evaluation of COVID-19 rapid antigen screening programs in the workplace
Author: Thomas N. Vilches, Ellen Rafferty, Chad R. Wells, Alison P. Galvani, Seyed M. Moghadas Publication date: 23 November 2022 Publication info: BMC Medicine, Volume 20, Article number: 452 (2022) Cited by: David Price 8:26 PM 14 December 2023 GMT Citerank: (6) 679878Seyed MoghadasSeyed Moghadas is an infectious disease modeller whose research includes mathematical and computational modelling in epidemiology and immunology. In particular, he is interested in the theoretical and computational aspects of mathematical models describing the underlying dynamics of infectious diseases, with a particular emphasis on establishing strong links between micro (individual) and macro (population) levels.10019D3ABAB, 701037MfPH â Publications144B5ACA0, 703957Economics859FDEF6, 704041Vaccination859FDEF6, 704045Covid-19859FDEF6, 715831Diagnostic testing859FDEF6 URL: DOI: https://doi.org/10.1186/s12916-022-02641-5
| Excerpt / Summary [BMC Medicine, 23 November 2022]
Background: Diagnostic testing has been pivotal in detecting SARS-CoV-2 infections and reducing transmission through the isolation of positive cases. We quantified the value of implementing frequent, rapid antigen (RA) testing in the workplace to identify screening programs that are cost-effective.
Methods: To project the number of cases, hospitalizations, and deaths under alternative screening programs, we adapted an agent-based model of COVID-19 transmission and parameterized it with the demographics of Ontario, Canada, incorporating vaccination and waning of immunity. Taking into account healthcare costs and productivity losses associated with each program, we calculated the incremental cost-effectiveness ratio (ICER) with quality-adjusted life year (QALY) as the measure of effect. Considering RT-PCR testing of only severe cases as the baseline scenario, we estimated the incremental net monetary benefits (iNMB) of the screening programs with varying durations and initiation times, as well as different booster coverages of working adults.
Results: Assuming a willingness-to-pay threshold of CDN$30,000 per QALY loss averted, twice weekly workplace screening was cost-effective only if the program started early during a surge. In most scenarios, the iNMB of RA screening without a confirmatory RT-PCR or RA test was comparable or higher than the iNMB for programs with a confirmatory test for RA-positive cases. When the program started early with a duration of at least 16 weeks and no confirmatory testing, the iNMB exceeded CDN$1.1 million per 100,000 population. Increasing booster coverage of working adults improved the iNMB of RA screening.
Conclusions: Our findings indicate that frequent RA testing starting very early in a surge, without a confirmatory test, is a preferred screening program for the detection of asymptomatic infections in workplaces. |
Link[172] Studying the mixed transmission in a community with age heterogeneity: COVID-19 as a case study
Author: Xiaoying Wang, Qing Han, Jude Dzevela Kong Publication date: 28 May 2022 Publication info: Infectious Disease Modelling, Volume 7, Issue 2, 2022, Pages 250-260, ISSN 2468-0427 Cited by: David Price 8:28 PM 14 December 2023 GMT Citerank: (2) 679815Jude KongDr. Jude Dzevela Kong is an Assistant Professor in the Department of Mathematics and Statistics at York University and the founding Director of the Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC). 10019D3ABAB, 701037MfPH â Publications144B5ACA0 URL: DOI: https://doi.org/10.1016/j.idm.2022.05.006
| Excerpt / Summary [Infectious Disease Modelling, 28 May 2022]
COVID-19 has been prevalent worldwide for about 2 years now and has brought unprecedented challenges to our society. Before vaccines were available, the main disease intervention strategies were non-pharmaceutical. Starting December 2020, in Ontario, Canada, vaccines were approved for administering to vulnerable individuals and gradually expanded to all individuals above the age of 12. As the vaccine coverage reached a satisfactory level among the eligible population, normal social activities resumed and schools reopened starting September 2021. However, when schools reopen for in-person learning, children under the age of 12 are unvaccinated and are at higher risks of contracting the virus. We propose an age-stratified model based on the age and vaccine eligibility of the individuals. We fit our model to the data in Ontario, Canada and obtain a good fitting result. The results show that a relaxed between-group contact rate may trigger future epidemic waves more easily than an increased within-group contact rate. An increasing mixed contact rate of the older group quickly amplifies the daily incidence numbers for both groups whereas an increasing mixed contact rate of the younger group mainly leads to future waves in the younger group alone. The results indicate the importance of accelerating vaccine rollout for younger individuals in mitigating disease spread. |
Link[173] Quarantine and serial testing for variants of SARS-CoV-2 with benefits of vaccination and boosting on consequent control of COVID-19
Author: Chad R Wells, Abhishek Pandey, Senay Gokcebel, Gary Krieger, A Michael Donoghue, Burton H Singer, Seyed M Moghadas, Alison P Galvani, Jeffrey P Townsend Publication date: 27 July 2022 Publication info: PNAS Nexus, Volume 1, Issue 3, July 2022, pgac100, 27 July 2022 Cited by: David Price 8:29 PM 14 December 2023 GMT
Citerank: (7) 679878Seyed MoghadasSeyed Moghadas is an infectious disease modeller whose research includes mathematical and computational modelling in epidemiology and immunology. In particular, he is interested in the theoretical and computational aspects of mathematical models describing the underlying dynamics of infectious diseases, with a particular emphasis on establishing strong links between micro (individual) and macro (population) levels.10019D3ABAB, 701037MfPH â Publications144B5ACA0, 703963Mobility859FDEF6, 704041Vaccination859FDEF6, 704045Covid-19859FDEF6, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6, 715831Diagnostic testing859FDEF6 URL: DOI: https://doi.org/10.1093/pnasnexus/pgac100
| Excerpt / Summary [PNAS Nexus, 27 July 2022]
Quarantine and serial testing strategies for a disease depend principally on its incubation period and infectiousness profile. In the context of COVID-19, these primary public health tools must be modulated with successive SARS CoV-2 variants of concern that dominate transmission. Our analysis shows that (1) vaccination status of an individual makes little difference to the determination of the appropriate quarantine duration of an infected case, whereas vaccination coverage of the population can have a substantial effect on this duration, (2) successive variants can challenge disease control efforts by their earlier and increased transmission in the disease time course relative to prior variants, and (3) sufficient vaccine boosting of a population substantially aids the suppression of local transmission through frequent serial testing. For instance, with Omicron, increasing immunity through vaccination and boostersâfor instance with 100% of the population is fully immunized and at least 24% having received a third doseâcan reduce quarantine durations by up to 2 d, as well as substantially aid in the repression of outbreaks through serial testing. Our analysis highlights the paramount importance of maintaining high population immunity, preferably by booster uptake, and the role of quarantine and testing to control the spread of SARS CoV-2. |
Link[174] Comparative analyses of eighteen rapid antigen tests and RT-PCR for COVID-19 quarantine and surveillance-based isolation
Author: Chad R. Wells, Abhishek Pandey, Seyed M. Moghadas, Burton H. Singer, Gary Krieger, Richard J. L. Heron, David E. Turner, Justin P. Abshire, Kimberly M. Phillips, A. Michael Donoghue, Alison P. Galvani, Jeffrey P. Townsend Publication date: 9 July 2022 Publication info: Communications Medicine, Volume 2, Article number: 84 (2022) Cited by: David Price 8:29 PM 14 December 2023 GMT Citerank: (3) 679878Seyed MoghadasSeyed Moghadas is an infectious disease modeller whose research includes mathematical and computational modelling in epidemiology and immunology. In particular, he is interested in the theoretical and computational aspects of mathematical models describing the underlying dynamics of infectious diseases, with a particular emphasis on establishing strong links between micro (individual) and macro (population) levels.10019D3ABAB, 701037MfPH â Publications144B5ACA0, 715831Diagnostic testing859FDEF6 URL: DOI: https://doi.org/10.1038/s43856-022-00147-y
| Excerpt / Summary [Communications Medicine, 9 July 2022]
Background: Rapid antigen (RA) tests are being increasingly employed to detect SARS-CoV-2 infections in quarantine and surveillance. Prior research has focused on RT-PCR testing, a single RA test, or generic diagnostic characteristics of RA tests in assessing testing strategies.
Methods: We have conducted a comparative analysis of the post-quarantine transmission, the effective reproduction number during serial testing, and the false-positive rates for 18 RA tests with emergency use authorization from The United States Food and Drug Administration and an RT-PCR test. To quantify the extent of transmission, we developed an analytical mathematical framework informed by COVID-19 infectiousness, test specificity, and temporal diagnostic sensitivity data.
Results: We demonstrate that the relative effectiveness of RA tests and RT-PCR testing in reducing post-quarantine transmission depends on the quarantine duration and the turnaround time of testing results. For quarantines of two days or shorter, conducting a RA test on exit from quarantine reduces onward transmission more than a single RT-PCR test (with a 24-h delay) conducted upon exit. Applied to a complementary approach of performing serial testing at a specified frequency paired with isolation of positives, we have shown that RA tests outperform RT-PCR with a 24-h delay. The results from our modeling framework are consistent with quarantine and serial testing data collected from a remote industry setting.
Conclusions: These RA test-specific results are an important component of the tool set for policy decision-making, and demonstrate that judicious selection of an appropriate RA test can supply a viable alternative to RT-PCR in efforts to control the spread of disease. |
Link[175] Resurgence of Omicron BA.2 in SARS-CoV-2 infection-naive Hong Kong
Author: Ruopeng Xie, Kimberly M. Edwards, Dillon C. Adam, Kathy S. M. Leung, Tim K. Tsang, Shreya Gurung, Weijia Xiong, Xiaoman Wei, Daisy Y. M. Ng, Gigi Y. Z. Liu, Pavithra Krishnan, Lydia D. J. Chang, Samuel M. S. Cheng, Haogao Gu, Gilman K. H. Siu, Joseph T. Wu, Gabriel M. Leung, Malik Peiris, Benjamin J. Cowling, Leo L. M. Poon, Vijaykrishna Dhanasekaran Publication date: 27 April 2023 Publication info: Nature Communications volume 14, Article number: 2422 (2023) Cited by: David Price 8:32 PM 14 December 2023 GMT Citerank: (1) 701037MfPH â Publications144B5ACA0 URL: DOI: https://doi.org/10.1038/s41467-023-38201-5
| Excerpt / Summary [Nature Communications, 27 April 2023]
Hong Kong experienced a surge of Omicron BA.2 infections in early 2022, resulting in one of the highest per-capita death rates of COVID-19. The outbreak occurred in a dense population with low immunity towards natural SARS-CoV-2 infection, high vaccine hesitancy in vulnerable populations, comprehensive disease surveillance and the capacity for stringent public health and social measures (PHSMs). By analyzing genome sequences and epidemiological data, we reconstructed the epidemic trajectory of BA.2 wave and found that the initial BA.2 community transmission emerged from cross-infection within hotel quarantine. The rapid implementation of PHSMs suppressed early epidemic growth but the effective reproduction number (Re) increased again during the Spring festival in early February and remained around 1 until early April. Independent estimates of point prevalence and incidence using phylodynamics also showed extensive superspreading at this time, which likely contributed to the rapid expansion of the epidemic. Discordant inferences based on genomic and epidemiological data underscore the need for research to improve near real-time epidemic growth estimates by combining multiple disparate data sources to better inform outbreak response policy. |
Link[176] The importance of quarantine: modelling the COVID-19 testing process
Author: Wanxiao Xu, Hongying Shu, Lin Wang, Xiang-Sheng Wang, James Watmough Publication date: 25 April 2023 Publication info: Journal of Mathematical Biology, 86, Article number: 81 (2023) Cited by: David Price 8:33 PM 14 December 2023 GMT Citerank: (4) 679805James WatmoughProfessor in the Department of Mathematics and Statistics at the University of New Brunswick.10019D3ABAB, 701037MfPH â Publications144B5ACA0, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6, 715831Diagnostic testing859FDEF6 URL: DOI: https://doi.org/10.1007/s00285-023-01916-6
| Excerpt / Summary [Journal of Mathematical Biology, 25 April 2023]
We incorporate the disease state and testing state into the formulation of a COVID-19 epidemic model. For this model, the basic reproduction number is identified and its dependence on model parameters related to the testing process and isolation efficacy is discussed. The relations between the basic reproduction number, the final epidemic and peak sizes, and the model parameters are further explored numerically. We find that fast test reporting does not always benefit the control of the COVID-19 epidemic if good quarantine while awaiting test results is implemented. Moreover, the final epidemic and peak sizes do not always increase along with the basic reproduction number. Under some circumstances, lowering the basic reproduction number increases the final epidemic and peak sizes. Our findings suggest that properly implementing isolation for individuals who are waiting for their testing results would lower the basic reproduction number as well as the final epidemic and peak sizes. |
Link[177] School and community reopening during the COVID-19 pandemic: a mathematical modelling study
Author: Pei Yuan, Elena Aruffo, Evgenia Gatov, Yi Tan, Qi Li, Nick Ogden, Sarah Collier, Bouchra Nasri, Iain Moyles, Huaiping Zhu Publication date: 2 February 2022 Publication info: R. Soc. open sci.9211883211883 Cited by: David Price 8:34 PM 14 December 2023 GMT
Citerank: (9) 679759Bouchra NasriProfessor Nasri is a faculty member of Biostatistics in the Department of Social and Preventive Medicine at the University of Montreal. Prof. Nasri is an FRQS Junior 1 Scholar in Artificial Intelligence in Health and Digital Health. She holds an NSERC Discovery Grant in Statistics for time series dependence modelling for complex data.10019D3ABAB, 679797Huaiping ZhuProfessor of mathematics at the Department of Mathematics and Statistics at York University, a York Research Chair (YRC Tier I) in Applied Mathematics, the Director of the Laboratory of Mathematical Parallel Systems at the York University (LAMPS), the Director of the Canadian Centre for Diseases Modelling (CCDM) and the Director of the One Health Modelling Network for Emerging Infections (OMNI-RĂUNIS). 10019D3ABAB, 679799Iain MoylesAssistant Professor in the Department of Mathematics and Statistics at York University. 10019D3ABAB, 701037MfPH â Publications144B5ACA0, 701222OMNI â Publications144B5ACA0, 714608Charting a FutureCharting a Future for Emerging Infectious Disease Modelling in Canada â April 2023 [1] 2794CAE1, 715329Nick OgdenNicholas Ogden is a senior research scientist and Director of the Public Health Risk Sciences Division within the National Microbiology Laboratory at the Public Health Agency of Canada.10019D3ABAB, 715617Schools859FDEF6, 715617Schools859FDEF6 URL: DOI: https://doi.org/10.1098/rsos.211883
| Excerpt / Summary Operating schools safely during the COVID-19 pandemic requires a balance between health risks and the need for in-person learning. Using demographic and epidemiological data between 31 July and 23 November 2020 from Toronto, Canada, we developed a compartmental transmission model with age, household and setting structure to study the impact of schools reopening in September 2020. The model simulates transmission in the home, community and schools, accounting for differences in infectiousness between adults and children, and accounting for work-from-home and virtual learning. While we found a slight increase in infections among adults (2.2%) and children (4.5%) within the first eight weeks of school reopening, transmission in schools was not the key driver of the virus resurgence in autumn 2020. Rather, it was community spread that determined the outbreak trajectory, primarily due to increases in contact rates among adults in the community after school reopening. Analyses of cross-infection among households, communities and schools revealed that home transmission is crucial for epidemic progression and safely operating schools, while the degree of in-person attendance has a larger impact than other control measures in schools. This study suggests that safe school reopening requires the strict maintenance of public health measures in the community. |
Link[178] Projections of the transmission of the Omicron variant for Toronto, Ontario, and Canada using surveillance data following recent changes in testing policies
Author: Pei Yuan, Elena Aruffo, Yi Tan, Liu Yang, Nicholas H. Ogden, Aamir Fazil, Huaiping Zhu Publication date: 12 April 2022 Publication info: Infectious Disease Modelling, Volume 7, Issue 2, June 2022, Pages 83-93, ISSN 2468-0427 Cited by: David Price 8:36 PM 14 December 2023 GMT Citerank: (6) 679797Huaiping ZhuProfessor of mathematics at the Department of Mathematics and Statistics at York University, a York Research Chair (YRC Tier I) in Applied Mathematics, the Director of the Laboratory of Mathematical Parallel Systems at the York University (LAMPS), the Director of the Canadian Centre for Diseases Modelling (CCDM) and the Director of the One Health Modelling Network for Emerging Infections (OMNI-RĂUNIS). 10019D3ABAB, 701037MfPH â Publications144B5ACA0, 701222OMNI â Publications144B5ACA0, 704022Surveillance859FDEF6, 715329Nick OgdenNicholas Ogden is a senior research scientist and Director of the Public Health Risk Sciences Division within the National Microbiology Laboratory at the Public Health Agency of Canada.10019D3ABAB, 715831Diagnostic testing859FDEF6 URL: DOI: https://doi.org/10.1016/j.idm.2022.03.004
| Excerpt / Summary At the end of 2021, with the rapid escalation of COVID19 cases due to the Omicron variant, testing centers in Canada were overwhelmed. To alleviate the pressure on the PCR testing capacity, many provinces implemented new strategies that promote self testing and adjust the eligibility for PCR tests, making the count of new cases underreported. We designed a novel compartmental model which captures the new testing guidelines, social behaviours, booster vaccines campaign and features of the newest variant Omicron. To better describe the testing eligibility, we considered the population divided into high risk and non-high-risk settings. The model is calibrated using data from January 1 to February 9, 2022, on cases and severe outcomes in Canada, the province of Ontario and City of Toronto. We conduct analyses on the impact of PCR testing capacity, self testing, different levels of reopening and vaccination coverage on cases and severe outcomes. Our results show that the total number of cases in Canada, Ontario and Toronto are 2.34 (95%CI: 1.22â3.38), 2.20 (95%CI: 1.15â3.72), and 1.97(95%CI: 1.13â3.41), times larger than reported cases, respectively. The current testing strategy is efficient if partial restrictions, such as limited capacity in public spaces, are implemented. Allowing more people to have access to PCR reduces the daily cases and severe outcomes; however, if PCR test capacity is insufficient, then it is important to promote self testing. Also, we found that reopening to a pre-pandemic level will lead to a resurgence of the infections, peaking in late March or April 2022. Vaccination and adherence to isolation protocols are important supports to the testing policies to mitigate any possible spread of the virus. |
Link[179] Using simulation modelling and systems science to help contain COVIDâ19: A systematic review
Author: Weiwei Zhang, Shiyong Liu, Nathaniel Osgood, Hongli Zhu, Ying Qian, Peng Jia Publication date: 19 August 2022 Publication info: Systems Research and Behavioral ScienceVolume 40, Issue 1 p. 207-234 Cited by: David Price 9:14 PM 14 December 2023 GMT Citerank: (3) 679855Nathaniel OsgoodNathaniel D. Osgood is a Professor in the Department of Computer Science and Associate Faculty in the Department of Community Health & Epidemiology at the University of Saskatchewan.10019D3ABAB, 701037MfPH â Publications144B5ACA0, 708812Simulation859FDEF6 URL: DOI: https://doi.org/10.1002/sres.2897
| Excerpt / Summary [Systems Research and Behavioral Science, 19 August 2022]
This study systematically reviews applications of three simulation approaches, that is, system dynamics model (SDM), agent-based model (ABM) and discrete event simulation (DES), and their hybrids in COVID-19 research and identifies theoretical and application innovations in public health. Among the 372 eligible papers, 72 focused on COVID-19 transmission dynamics, 204 evaluated both pharmaceutical and non-pharmaceutical interventions, 29 focused on the prediction of the pandemic and 67 investigated the impacts of COVID-19. ABM was used in 275 papers, followed by 54 SDM papers, 32 DES papers and 11 hybrid model papers. Evaluation and design of intervention scenarios are the most widely addressed area accounting for 55% of the four main categories, that is, the transmission of COVID-19, prediction of the pandemic, evaluation and design of intervention scenarios and societal impact assessment. The complexities in impact evaluation and intervention design demand hybrid simulation models that can simultaneously capture micro and macro aspects of the socio-economic systems involved. |
Link[180] Patch model for border reopening and control to prevent new outbreaks of COVID-19
Author: Tingting Zheng, Huaiping Zhu, Zhidong Teng, Linfei Nie, Yantao Luo Publication date: 10 February 2023 Publication info: Mathematical biosciences and engineering : MBE, 20(4), 7171â7192 Cited by: David Price 9:14 PM 14 December 2023 GMT Citerank: (4) 679797Huaiping ZhuProfessor of mathematics at the Department of Mathematics and Statistics at York University, a York Research Chair (YRC Tier I) in Applied Mathematics, the Director of the Laboratory of Mathematical Parallel Systems at the York University (LAMPS), the Director of the Canadian Centre for Diseases Modelling (CCDM) and the Director of the One Health Modelling Network for Emerging Infections (OMNI-RĂUNIS). 10019D3ABAB, 701037MfPH â Publications144B5ACA0, 703963Mobility859FDEF6, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6 DOI: https://doi.org/10.3934/mbe.2023310
| Excerpt / Summary [Mathematical Biosciences and Engineering, 10 February 2023]
In this paper, we propose a two-patch model with border control to investigate the effect of border control measures and local non-pharmacological interventions (NPIs) on the transmission of COVID-19. The basic reproduction number of the model is calculated, and the existence and stability of the boundary equilibria and the existence of the coexistence equilibrium of the model are obtained. Through numerical simulation, when there are no unquarantined virus carriers in the patch-2, it can be concluded that the reopening of the border with strict border control measures to allow people in patch-1 to move into patch-2 will not lead to disease outbreaks. Also, when there are unquarantined virus carriers in patch-2 (or lax border control causes people carrying the virus to flow into patch-2), the border control is more strict, and the slower the growth of number of new infectious in patch-2, but the strength of border control does not affect the final state of the disease, which is still dependent on local NPIs. Finally, when the border reopens during an outbreak of disease in patch-2, then a second outbreak will happen. |
Link[181] Modeling and Evaluation of the Joint Prevention and Control Mechanism for Curbing COVID-19 in Wuhan
Author: Linhua Zhou, Xinmiao Rong, Meng Fan, Liu Yang, Huidi Chu, Ling Xue, Guorong Hu, Siyu Liu, Zhijun Zeng, Ming Chen, Wei Sun, Jiamin Liu, Yawen Liu, Shishen Wang, Huaiping Zhu Publication date: 4 January 2022 Publication info: Bulletin of Mathematical Biology, 84(2) Cited by: David Price 9:15 PM 14 December 2023 GMT Citerank: (3) 679797Huaiping ZhuProfessor of mathematics at the Department of Mathematics and Statistics at York University, a York Research Chair (YRC Tier I) in Applied Mathematics, the Director of the Laboratory of Mathematical Parallel Systems at the York University (LAMPS), the Director of the Canadian Centre for Diseases Modelling (CCDM) and the Director of the One Health Modelling Network for Emerging Infections (OMNI-RĂUNIS). 10019D3ABAB, 685420Hospitals16289D5D4, 701037MfPH â Publications144B5ACA0 URL: DOI: https://doi.org/10.1007/s11538-021-00983-4
| Excerpt / Summary [Bulletin of Mathematical Biology, 4 January 2022]
The spread of COVID-19 in Wuhan was successfully curbed under the strategy of âJoint Prevention and Control Mechanism.â To understand how this measure stopped the epidemics in Wuhan, we establish a compartmental model with time-varying parameters over different stages. In the early stage of the epidemic, due to resource limitations, the number of daily reported cases may lower than the actual number. We employ a dynamic-based approach to calibrate the accumulated clinically diagnosed data with a sudden jump on February 12 and 13. The model simulation shows reasonably good match with the adjusted data which allows the prediction of the cumulative confirmed cases. Numerical results reveal that the âJoint Prevention and Control Mechanismâ played a significant role on the containment of COVID-19. The spread of COVID-19 cannot be inhibited if any of the measures was not effectively implemented. Our analysis also illustrates that the Fangcang Shelter Hospitals are very helpful when the beds in the designated hospitals are insufficient. Comprised with Fangcang Shelter Hospitals, the designated hospitals can contain the transmission of COVID-19 more effectively. Our findings suggest that the combined multiple measures are essential to curb an ongoing epidemic if the prevention and control measures can be fully implemented. |
Link[182] A Network Dynamics Model for the Transmission of COVID-19 in Diamond Princess and a Response to Reopen Large-Scale Public Facilities
Author: Yuchen Zhu, Ying Wang, Chunyu Li, Lili Liu, Chang Qi, Yan Jia, Kaili She, Tingxuan Liu, Huaiping Zhu, Xiujun Li Publication date: 12 January 2022 Publication info: Healthcare, 10(1), 139â139. Cited by: David Price 9:16 PM 14 December 2023 GMT Citerank: (4) 679797Huaiping ZhuProfessor of mathematics at the Department of Mathematics and Statistics at York University, a York Research Chair (YRC Tier I) in Applied Mathematics, the Director of the Laboratory of Mathematical Parallel Systems at the York University (LAMPS), the Director of the Canadian Centre for Diseases Modelling (CCDM) and the Director of the One Health Modelling Network for Emerging Infections (OMNI-RĂUNIS). 10019D3ABAB, 701037MfPH â Publications144B5ACA0, 703963Mobility859FDEF6, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6 URL: DOI: https://doi.org/10.3390/healthcare10010139
| Excerpt / Summary [Healthcare, 12 January 2022]
Background: The current epidemic of COVID-19 has become the new normal. However, the novel coronavirus is constantly mutating. In public transportation or large entertainment venues, it can spread more quickly once an infected person is introduced. This study aims to discuss whether large public facilities can be opened and operated under the current epidemic situation.
Methods: The dual BarabĂĄsiâAlbert (DBA) model was used to build a contact network. A dynamics compartmental modeling framework was used to simulate the COVID-19 epidemic with different interventions on the Diamond Princess.
Results: The effect of isolation only was minor. Regardless of the transmission rate of the virus, joint interventions can prevent 96.95% (95% CI: 96.70â97.15%) of infections. Compared with evacuating only passengers, evacuating the crew and passengers can avoid about 11.90% (95% CI: 11.83â12.06%) of infections.
Conclusions: It is feasible to restore public transportation services and reopen large-scale public facilities if monitoring and testing can be in place. Evacuating all people as soon as possible is the most effective way to contain the outbreak in large-scale public facilities. |
Link[183] Modelling the impact of household size distribution on the transmission dynamics of COVID-19
Author: Pengyu Liu, Lisa McQuarrie, Yexuan Song, Caroline Colijn Publication date: 28 April 2021 Cited by: David Price 9:20 PM 14 December 2023 GMT Citerank: (2) 679761Caroline ColijnDr. Caroline Colijn works at the interface of mathematics, evolution, infection and public health, and leads the MAGPIE research group. She joined SFU's Mathematics Department in 2018 as a Canada 150 Research Chair in Mathematics for Infection, Evolution and Public Health. She has broad interests in applications of mathematics to questions in evolution and public health, and was a founding member of Imperial College London's Centre for the Mathematics of Precision Healthcare.10019D3ABAB, 690180British Columbia COVID-19 GroupThe BC COVID-19 Modelling Group works on rapid response modelling of the COVID-19 pandemic, with a special focus on British Columbia and Canada.10015D3D3AB URL: DOI: https://doi.org/10.1098/rsif.2021.0036
| Excerpt / Summary Under the implementation of non-pharmaceutical interventions such as social distancing and lockdowns, household transmission has been shown to be significant for COVID-19, posing challenges for reducing incidence in settings where people are asked to self-isolate at home and to spend increasing amounts of time at home due to distancing measures. Accordingly, characteristics of households in a region have been shown to relate to transmission heterogeneity of the virus. We introduce a stochastic epidemiological model to examine the impact of the household size distribution in a region on the transmission dynamics. We choose parameters to reflect incidence in two health regions of the Greater Vancouver area in British Columbia and simulate the impact of distancing measures on transmission, with household size distribution the only different parameter between simulations for the two regions. Our result suggests that the dissimilarity in household size distribution alone can cause significant differences in incidence of the two regions, and the distributions drive distinct dynamics that match reported cases. Furthermore, our model suggests that offering individuals a place to isolate outside their household can speed the decline in cases, and does so more effectively where there are more larger households. |
Link[184] Containing and Managing an Emerging Disease Outbreak: A Stochastic Modelling Approach
Author: Idriss Sekkak, Jude Dzevela Kong, Mohamed El Fatini Publication date: 15 April 2022 Publication info: Social Science Research Network Cited by: David Price 9:20 PM 14 December 2023 GMT Citerank: (2) 679815Jude KongDr. Jude Dzevela Kong is an Assistant Professor in the Department of Mathematics and Statistics at York University and the founding Director of the Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC). 10019D3ABAB, 701037MfPH â Publications144B5ACA0 URL: DOI: https://doi.org/10.2139/ssrn.4040246
| Excerpt / Summary [Social Science Research Network, 15 April 2022]
The aim of this work is to design and analyze a novel stochastic model for an infectious disease transmission dynamics, that captures human responses to information about the disease, policy, and disease progression in the event of an outbreak. We design a behaviour-structured stochastic Susceptible-Infected-Quarantine-Recovered model incorporating a population logistic growth, non pharmaceutical interventions and a general functional response in order to capture respectively the long time growth of a population size, instant measures established by decision makers and human response behaviour. We carry out a thorough analysis to investigate the existence of the global and positive solutions and to explore the extinction and the persistence of the disease regarding the basic reproduction number of the model. Moreover, we use suitable Lyapunov functions and establish sufficient conditions for the existence of ergodic stationary distribution of the solution to the stochastic SIQR model. In addition, we estimate the parameters of the model by fitting it to confirmed COVID-19 cases in Morocco using least squares method. |
Link[185] A Stochastic Analysis of a Siqr Epidemic Model With Short and Long-term Prophylaxis
Author: Idriss Sekkak, Nasri, B., RĂ©millard, B., Jude Dzevela Kong, Mohamed El Fatini Publication date: 13 September 2022 Publication info: Research Square, 13 September 2022 Cited by: David Price 9:21 PM 14 December 2023 GMT Citerank: (3) 679759Bouchra NasriProfessor Nasri is a faculty member of Biostatistics in the Department of Social and Preventive Medicine at the University of Montreal. Prof. Nasri is an FRQS Junior 1 Scholar in Artificial Intelligence in Health and Digital Health. She holds an NSERC Discovery Grant in Statistics for time series dependence modelling for complex data.10019D3ABAB, 679815Jude KongDr. Jude Dzevela Kong is an Assistant Professor in the Department of Mathematics and Statistics at York University and the founding Director of the Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC). 10019D3ABAB, 701037MfPH â Publications144B5ACA0 URL: DOI: https://doi.org/10.21203/rs.3.rs-1762043/v1
| Excerpt / Summary [Research Square, 13 September 2022]
This paper aims to incorporate a high order stochastic perturbation into a SIQR epidemic model with transient prophylaxis and lasting prophylaxis. The existence and uniqueness of the global positive solution is proven and a stochastic condition in order to study the extinction of an infectious disease is established. The existence of a stationary distribution for the stochastic epidemic model is investigated as well. Numerical simulations are conducted to support our theoretical results and an example of application with COVID-19 data from Canada is used to estimate the transmission rate and basic reproduction number while constructing a model fitting the data. |
Link[186] A generalized distributed delay model of COVID-19: An endemic model with immunity waning
Author: Sarafa A. Iyaniwura, Rabiu Musa, Jude D. Kong Publication date: 12 January 2023 Publication info: Mathematical Biosciences and Engineering, 20(3), 5379â5412 Cited by: David Price 9:22 PM 14 December 2023 GMT Citerank: (3) 679815Jude KongDr. Jude Dzevela Kong is an Assistant Professor in the Department of Mathematics and Statistics at York University and the founding Director of the Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC). 10019D3ABAB, 701037MfPH â Publications144B5ACA0, 704036Immunology859FDEF6 URL: DOI: https://doi.org/10.3934/mbe.2023249
| Excerpt / Summary [Mathematical Biosciences and Engineering, 12 January 2023]
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been spreading worldwide for over two years, with millions of reported cases and deaths. The deployment of mathematical modeling in the fight against COVID-19 has recorded tremendous success. However, most of these models target the epidemic phase of the disease. The development of safe and effective vaccines against SARS-CoV-2 brought hope of safe reopening of schools and businesses and return to pre-COVID normalcy, until mutant strains like the Delta and Omicron variants, which are more infectious, emerged. A few months into the pandemic, reports of the possibility of both vaccine- and infection-induced immunity waning emerged, thereby indicating that COVID-19 may be with us for longer than earlier thought. As a result, to better understand the dynamics of COVID-19, it is essential to study the disease with an endemic model. In this regard, we developed and analyzed an endemic model of COVID-19 that incorporates the waning of both vaccine- and infection-induced immunities using distributed delay equations. Our modeling framework assumes that the waning of both immunities occurs gradually over time at the population level. We derived a nonlinear ODE system from the distributed delay model and showed that the model could exhibit either a forward or backward bifurcation depending on the immunity waning rates. Having a backward bifurcation implies that Rc < 1 is not sufficient to guarantee disease eradication, and that the immunity waning rates are critical factors in eradicating COVID-19. Our numerical simulations show that vaccinating a high percentage of the population with a safe and moderately effective vaccine could help in eradicating COVID-19. |
Link[187] The basic reproduction number of COVID-19 across Africa
Author: Sarafa A. Iyaniwura, Musa Rabiu, Jummy F. David, Jude D. Kong Publication date: 25 February 2022 Publication info: PLOS ONE, 17(2), e0264455. Cited by: David Price 9:24 PM 14 December 2023 GMT Citerank: (2) 679815Jude KongDr. Jude Dzevela Kong is an Assistant Professor in the Department of Mathematics and Statistics at York University and the founding Director of the Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC). 10019D3ABAB, 701037MfPH â Publications144B5ACA0 URL: DOI: https://doi.org/10.1371/journal.pone.0264455
| Excerpt / Summary [PLOS ONE, 25 February 2022]
The pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) took the world by surprise. Following the first outbreak of COVID-19 in December 2019, several models have been developed to study and understand its transmission dynamics. Although the spread of COVID-19 is being slowed down by vaccination and other interventions, there is still a need to have a clear understanding of the evolution of the pandemic across countries, states and communities. To this end, there is a need to have a clearer picture of the initial spread of the disease in different regions. In this project, we used a simple SEIR model and a Bayesian inference framework to estimate the basic reproduction number of COVID-19 across Africa. Our estimates vary between 1.98 (Sudan) and 9.66 (Mauritius), with a median of 3.67 (90% CrI: 3.31â4.12). The estimates provided in this paper will help to inform COVID-19 modeling in the respective countries/regions. |
Link[188] An Agent-Based Modeling and Virtual Reality Application Using Distributed Simulation: Case of a COVID-19 Intensive Care Unit
Author: Jalal Possik, Ali Asgary, Adriano O. Solis, Gregory Zacharewicz, Mohammad A. Shafiee, Mahdi M. Najafabadi, Nazanin Nadri, Abel Guimaraes, Hossein Iranfar, Philip Ma, Christie M. Lee, Mohammadali Tofighi, Mehdi Aarabi, Simon Gorecki, Jianhong Wu Publication date: 1 August 2023 Publication info: IEEE Transactions on Engineering Management, 70(8), 2931â2943 Cited by: David Price 9:24 PM 14 December 2023 GMT Citerank: (5) 679750Ali AsgaryAssociate Professor and Associate Director, Advanced Disaster, Emergency and Rapid Response Simulation (ADERSIM) in the School of Administrative Studies, and Adjunct Professor in the School of Information Technology, at York University.10019D3ABAB, 679812Jianhong WuProfessor Jianhong Wu is a University Distinguished Research Professor and Senior Canada Research Chair in industrial and applied mathematics at York University. He is also the NSERC Industrial Research Chair in vaccine mathematics, modelling, and manufacturing. 10019D3ABAB, 685420Hospitals16289D5D4, 701037MfPH â Publications144B5ACA0, 708813Agent-based models859FDEF6 URL: DOI: https://doi.org/10.1109/tem.2022.3195813
| Excerpt / Summary [IEEE Transactions on Engineering Management, August 2023]
Hospitals and other healthcare settings use various simulation methods to improve their operations, management, and training. The COVID-19 pandemic, with the resulting necessity for rapid and remote assessment, has highlighted the critical role of modeling and simulation in healthcare, particularly distributed simulation (DS). DS enables integration of heterogeneous simulations to further increase the usability and effectiveness of individual simulations. This article presents a DS system that integrates two different simulations developed for a hospital intensive care unit (ICU) ward dedicated to COVID-19 patients. AnyLogic has been used to develop a simulation model of the ICU ward using agent-based and discrete event modeling methods. This simulation depicts and measures physical contacts between healthcare providers and patients. The Unity platform has been utilized to develop a virtual reality simulation of the ICU environment and operations. The high-level architecture, an IEEE standard for DS, has been used to build a cloud-based DS system by integrating and synchronizing the two simulation platforms. While enhancing the capabilities of both simulations, the DS system can be used for training purposes and assessment of different managerial and operational decisions to minimize contacts and disease transmission in the ICU ward by enabling data exchange between the two simulations. |
Link[189] Endemic means change as SARS-CoV-2 evolves
Author: Sarah P. Otto, Ailene MacPherson, Caroline Colijn Publication date: 29 September 2023 Publication info: medRxiv 2023.09.28.23296264 Cited by: David Price 4:44 PM 15 December 2023 GMT Citerank: (4) 679761Caroline ColijnDr. Caroline Colijn works at the interface of mathematics, evolution, infection and public health, and leads the MAGPIE research group. She joined SFU's Mathematics Department in 2018 as a Canada 150 Research Chair in Mathematics for Infection, Evolution and Public Health. She has broad interests in applications of mathematics to questions in evolution and public health, and was a founding member of Imperial College London's Centre for the Mathematics of Precision Healthcare.10019D3ABAB, 679875Sarah OttoProfessor in Zoology. Theoretical biologist, Canada Research Chair in Theoretical and Experimental Evolution, and Killam Professor at the University of British Columbia.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 708734Genomics859FDEF6 URL: DOI: https://doi.org/10.1101/2023.09.28.23296264
| Excerpt / Summary [medRxiv, 29 September 2023]
COVID-19 has become endemic, with dynamics that reflect the waning of immunity and re-exposure, by contrast to the epidemic phase driven by exposure in immunologically naĂŻve populations. Endemic does not, however, mean constant. Further evolution of SARS-CoV-2, as well as changes in behaviour and public health policy, continue to play a major role in the endemic load of disease and mortality. In this paper, we analyse evolutionary models to explore the impact that newly arising variants can have on the short-term and longer-term endemic load, characterizing how these impacts depend on the transmission and immunological properties of variants. We describe how evolutionary changes in the virus will increase the endemic load most for persistently immune-escape variants, by an intermediate amount for more transmissible variants, and least for transiently immune-escape variants. Balancing the tendency for evolution to favour variants that increase the endemic load, we explore the impact of vaccination strategies and non-pharmaceutical interventions (NPIs) that can counter these increases in the impact of disease. We end with some open questions about the future of COVID-19 as an endemic disease. |
Link[190] Vaccine rollout strategies: The case for vaccinating essential workers early
Author: Nicola Mulberry, Paul Tupper, Erin Kirwin, Christopher McCabe, Caroline Colijn Publication date: 13 October 2021 Publication info: PLOS Glob Public Health 1(10): e0000020 Cited by: David Price 4:58 PM 15 December 2023 GMT
Citerank: (11) 679761Caroline ColijnDr. Caroline Colijn works at the interface of mathematics, evolution, infection and public health, and leads the MAGPIE research group. She joined SFU's Mathematics Department in 2018 as a Canada 150 Research Chair in Mathematics for Infection, Evolution and Public Health. She has broad interests in applications of mathematics to questions in evolution and public health, and was a founding member of Imperial College London's Centre for the Mathematics of Precision Healthcare.10019D3ABAB, 679770Christopher McCabeDr. Christopher McCabe is the CEO and Executive Director of the Institute of Health Economics (IHE).10019D3ABAB, 679862Paul TupperProfessor in the Department of Mathematics at Simon Fraser University.10019D3ABAB, 685420Hospitals16289D5D4, 686720Erin KirwinErin Kirwin (she/her) is a Health Economist at the Institute of Health Economics (IHE) in Alberta, Canada. She holds a Bachelor of Arts (Honours) in Economics and International Development Studies from McGill University and a Master of Arts in Economics from the University of Alberta. Prior to joining the IHE, Erin was the Manager of Advanced Analytics at Alberta Health. Erin is a PhD candidate at the University of Manchester.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6, 708794Health economics859FDEF6, 714608Charting a FutureCharting a Future for Emerging Infectious Disease Modelling in Canada â April 2023 [1] 2794CAE1, 715454Workforce impact859FDEF6, 728545Long COVIDPost-acute sequelae of COVID-19 (PASC).859FDEF6 URL: DOI: https://doi.org/10.1371/journal.pgph.0000020
| Excerpt / Summary [PLOS Global Public Health, 13 October 2021]
In vaccination campaigns against COVID-19, many jurisdictions are using age-based rollout strategies, reflecting the much higher risk of severe outcomes of infection in older groups. In the wake of growing evidence that approved vaccines are effective at preventing not only adverse outcomes, but also infection, we show that such strategies are less effective than strategies that prioritize essential workers. This conclusion holds across numerous outcomes, including cases, hospitalizations, Long COVID (cases with symptoms lasting longer than 28 days), deaths and net monetary benefit. Our analysis holds in regions where the vaccine supply is limited, and rollout is prolonged for several months. In such a setting with a population of 5M, we estimate that vaccinating essential workers sooner prevents over 200,000 infections, over 600 deaths, and produces a net monetary benefit of over $500M. |
Link[191] SARS-CoV-2 Incubation Period during Omicron BA.5âDominant Period, Japan
Author: Hao-Yuan Cheng, Andrei R. Akhmetzhanov, Jonathan Dushoff Publication date: 1 January 2024 Publication info: Emerging Infectious Diseases. 2024;30(1):206-207, Cited by: David Price 9:45 PM 10 January 2024 GMT Citerank: (2) 679814Jonathan DushoffProfessor in the Department Of Biology at McMaster University.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0 URL: DOI: https://doi.org/10.3201/eid3001.230208
| Excerpt / Summary [Emerging Infectious Diseases, 1 January 2024]
To the Editor: Ogata and Tanaka (1) estimated the mean incubation period was 2.9 (95% CI 2.6â3.2) days for SARS-CoV-2 strain Omicron BA.1 and 2.6 (95% CI 2.5â2.8) days for Omicron BA.5 during the Omicron-dominant period in Japan. Their earlier study reported a similar mean incubation period of 3.1 days for BA.1 (2). Their findings were derived from data collected through contact tracing efforts in Ibaraki Prefecture, Japan, which provided high accuracy in determining exposure time windows.
A potential concern is that their study only included cases that had a single exposure event and a 1-day exposure window. Although this concern was recognized by the authors as a study limitation, we emphasize that those criteria might bias results downward, especially when the disease is widespread. Persons that had longer incubation periods might have more opportunity for contacts or multiple exposure dates; thus, those with shorter incubation periods would be favored for inclusion. A more flexible case-selection approach might reduce bias, even though this approach would require methods to address uncertainty in actual infection timing.
In Taiwan, we collected data from the first 100 local symptomatic cases during the BA.1âdominant period (December 25, 2021âJanuary 18, 2022), which were characterized by intensive case finding and contact tracing (A. Akhmetzhanov et al., unpub. data, https://doi.org/10.110...). Among 69 cases with an identified exposure, only 4 had a 1-day exposure window. Using more comprehensive exposure windows, the estimated mean incubation period in Taiwan was 3.5 (95% CI 3.1â4.0) days, longer than Tanaka et al.âs estimates (1,2) but similar to estimates of 3.5 days from Italy (data collected during January 2022) (3) and South Korea (data collected during NovemberâDecember 2021) (4) and estimates from a systematic review (3.6 days) (5). The estimates from Japan (2) appear to be the shortest periods reported across previously reviewed studies (5). |
Link[192] Incubation-period estimates of Omicron (BA.1) variant from Taiwan, December 2021âJanuary 2022, and its comparison to other SARS-CoV-2 variants: a statistical modeling, systematic search and meta-analysis
Author: Andrei R. Akhmetzhanov, Hao-Yuan Cheng, Jonathan Dushoff Publication date: 24 July 2023 Publication info: medRxiv 2023.07.20.23292983 Cited by: David Price 10:04 PM 10 January 2024 GMT Citerank: (2) 679814Jonathan DushoffProfessor in the Department Of Biology at McMaster University.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0 URL: DOI: https://doi.org/10.1101/2023.07.20.23292983
| Excerpt / Summary [medRxiv, 24 July 2023]
Background: The ongoing COVID-19 pandemic has seen several variants of concern, including the Omicron (BA.1) variant which emerged in October 2021. Accurately estimating the incubation period of these variants is crucial for predicting disease spread and formulating effective public health strategies. However, existing estimates often conflict because of biases arising from the dynamic nature of epidemic growth and selective inclusion of cases. This study aims to accurately estimate of the Omicron (BA.1) variant incubation period based on data from Taiwan, where disease incidence remained low and contact tracing was comprehensive during the first months of the Omicron outbreak.
Methods: We reviewed 100 contact-tracing records for cases of the Omicron BA.1 variant reported between December 2021 and January 2022, and found enough information to analyze 70 of these. The incubation period distribution was estimated by fitting data on exposure and symptom onset within a Bayesian mixture model using gamma, Weibull, and lognormal distributions as candidates. Additionally, a systematic literature search was conducted to accumulate data for estimates of the incubation period for Omicron (BA.1/2, BA.4/5) subvariants, which was then used for meta-analysis and comparison.
Results: The mean incubation period was estimated at 3.5 days (95% credible interval: 3.1â4.0 days), with no clear differences when stratified by vaccination status or age. This estimate aligns closely with the pooled mean of 3.4 days (3.0â3.8 days) obtained from a meta-analysis of other published studies on Omicron subvariants.
Conclusions: The relatively shorter incubation period of the Omicron variant, as compared to previous SARS-CoV2 variants, implies its potential for rapid spread but also opens the possibility for individuals to voluntarily adopt shorter, more resource-efficient quarantine periods. Continual updates to incubation period estimates, utilizing data from comprehensive contact tracing, are crucial for effectively guiding these voluntary actions and adjusting high socio-economic cost interventions. |
Link[193] Trends in outpatient and inpatient visits for separate ambulatory-care-sensitive conditions during the first year of the COVID-19 pandemic: a province-based study
Author: Tetyana Kendzerska, David T. Zhu, Michael Pugliese, Douglas Manuel, Mohsen Sadatsafavi, Marcus Povitz, Therese A. Stukel, Teresa To, Shawn D. Aaron, Sunita Mulpuru, Melanie Chin, Claire E. Kendall, Kednapa Thavorn, Rebecca Robillard, Andrea S. Gershon Publication date: 18 December 2023 Publication info: Frontiers in Public Health, Volume 11, 18 December 2023 Cited by: David Price 0:09 AM 12 January 2024 GMT Citerank: (3) 685230Doug ManuelDr. Manuel is a Medical Doctor with a Masters in Epidemiology and Royal College specialization in Public Health and Preventive Medicine. He is a Senior Scientist in the Clinical Epidemiology Program at Ottawa Hospital Research Institute, and a Professor in the Departments of Family Medicine and Epidemiology and Community Medicine.10019D3ABAB, 685420Hospitals16289D5D4, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0 URL: DOI: https://doi.org/10.3389/fpubh.2023.1251020
| Excerpt / Summary [Frontiers in Public Health, 18 December 2023]
Background: The COVID-19 pandemic led to global disruptions in non-urgent health services, affecting health outcomes of individuals with ambulatory-care-sensitive conditions (ACSCs).
Methods: We conducted a province-based study using Ontario health administrative data (Canada) to determine trends in outpatient visits and hospitalization rates (per 100,000 people) in the general adult population for seven ACSCs during the first pandemic year (March 2020âMarch 2021) compared to previous years (2016â2019), and how disruption in outpatient visits related to acute care use. ACSCs considered were chronic obstructive pulmonary disease (COPD), asthma, angina, congestive heart failure (CHF), hypertension, diabetes, and epilepsy. We used time series auto-regressive integrated moving-average models to compare observed versus projected rates.
Results: Following an initial reduction (MarchâMay 2020) in all types of visits, primary care outpatient visits (combined in-person and virtual) returned to pre-pandemic levels for asthma, angina, hypertension, and diabetes, remained below pre-pandemic levels for COPD, and rose above pre-pandemic levels for CHF (104.8 vs. 96.4, 95% CI: 89.4â104.0) and epilepsy (29.6 vs. 24.7, 95% CI: 22.1â27.5) by the end of the first pandemic year. Specialty visits returned to pre-pandemic levels for COPD, angina, CHF, hypertension, and diabetes, but remained above pre-pandemic levels for asthma (95.4 vs. 79.5, 95% CI: 70.7â89.5) and epilepsy (53.3 vs. 45.6, 95% CI: 41.2â50.5), by the end of the year. Virtual visit rates increased for all ACSCs. Among ACSCs, reductions in hospitalizations were most pronounced for COPD and asthma. CHF-related hospitalizations also decreased, albeit to a lesser extent. For angina, hypertension, diabetes, and epilepsy, hospitalization rates reduced initially, but returned to pre-pandemic levels by the end of the year.
Conclusion: This study demonstrated variation in outpatient visit trends for different ACSCs in the first pandemic year. No outpatient visit trends resulted in increased hospitalizations for any ACSC; however, reductions in rates of asthma, COPD, and CHF hospitalizations persisted. |
Link[194] SARS-CoV-2 Exposures at a Large Gathering Event and Acquisition of COVID-19 in the Post-Vaccination Era: A Randomized Trial Is Possible During the Pandemic
Author: John M Conly, Mark Loeb Publication date: 15 December 2023 Publication info: Clinical Infectious Diseases, Volume 77, Issue 12, 15 December 2023, Pages 1656â1658, Cited by: David Price 0:23 AM 12 January 2024 GMT Citerank: (2) 679843Mark LoebProfessor at Pathology and Molecular Medicine (primary), Clinical Epidemiology and Biostatistics in the Department of Pathology and Molecular Medicine at McMaster University. Associate Member, Medicine and Michael G. DeGroote Chair in Infectious Diseases.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0 URL: DOI: https://doi.org/10.1093/cid/ciad609
| Excerpt / Summary [Clinical Infectious Diseases, 15 December 2023]
The coronavirus disease 2019 (COVID-19) pandemic has had a major impact on all facets of life, including outcomes that were very significant to the health of the general public but also deleterious to the economy, culture, politics, social cohesion, food security, travel, human rights, education, and access to accurate information (1,2). The response to the COVID-19 pandemic was hampered by tensions between the dichotomous perspectives of public health and the acceptance (or lack thereof) of social measures intended to curb transmission, such as lockdowns, school closures, mask mandates, and curfews. Among other unintended consequences, these measures exposed considerable conflict, in part fed by a variety of opinions that emerged from the lack of clear scientific evidence.
It is widely accepted that randomized, controlled trials (RCTs) provide the least biased evidence when testing interventions (3). Randomization provides balanced groups of participants with respect to known and unknown bias, whereas observational studies are prone to confounding and cannot address unknown confounders. RCTs of pharmaceutical interventions including antivirals and vaccines were designed, funded, and deployed at an unprecedented pace during the COVID-19 pandemic. However, the same expediency was not seen for RCTs for nonpharmaceutical interventions (NPIs). For reasons that are not well understood, RCTs failed to be designed and implemented for some of the most disruptive policies applied to address COVID-19, a situation that has been described as a âpandemic tragedyâ (4,5). Some have suggested that RCTs in a pandemic are too difficult or impossible to conduct and that mechanistic or observational evidence is sufficient (6). Unfortunately, pursuing this type of evidence, to the exclusion of knowledge derived from RCTs, will not provide the best information that is essential to guide public health decisions during a pandemic. |
Link[195] Modeling the role of respiratory droplets in Covid-19 type pandemics
Author: Swetaprovo Chaudhuri, Saptarshi Basu, Prasenjit Kabi, Vishnu R. Unni, Abhishek Saha Publication date: 30 June 2020 Publication info: Physics of Fluids 32, 063309 (2020) Cited by: David Price 12:10 PM 23 January 2024 GMT Citerank: (2) 701037MfPH â Publications144B5ACA0, 717032Swetaprovo ChaudhuriSwetaprovo is an Associate Professor in the Institute for Aerospace Studies in the Faculty of Applied Science and Engineering at the University of Toronto.10019D3ABAB URL: DOI: https://doi.org/10.1063/5.0015984
| Excerpt / Summary [Physics of Fluids, 30 June 2020]
In this paper, we develop a first principles model that connects respiratory droplet physics with the evolution of a pandemic such as the ongoing Covid-19. The model has two parts. First, we model the growth rate of the infected population based on a reaction mechanism. The advantage of modeling the pandemic using the reaction mechanism is that the rate constants have sound physical interpretation. The infection rate constant is derived using collision rate theory and shown to be a function of the respiratory droplet lifetime. In the second part, we have emulated the respiratory droplets responsible for disease transmission as salt solution droplets and computed their evaporation time, accounting for droplet cooling, heat and mass transfer, and finally, crystallization of the dissolved salt. The model output favourably compares with the experimentally obtained evaporation characteristics of levitated droplets of pure water and salt solution, respectively, ensuring fidelity of the model. The droplet evaporation/desiccation time is, indeed, dependent on ambient temperature and is also a strong function of relative humidity. The multi-scale model thus developed and the firm theoretical underpinning that connects the two scalesâmacro-scale pandemic dynamics and micro-scale droplet physicsâthus could emerge as a powerful tool in elucidating the role of environmental factors on infection spread through respiratory droplets. |
Link[197] Analyzing the dominant SARS-CoV-2 transmission routes toward an ab initio disease spread model
Author: Swetaprovo Chaudhuri, Saptarshi Basu, Abhishek Saha Publication date: 4 December 2020 Publication info: Physics of Fluids 32, 123306 (2020) Cited by: David Price 7:38 PM 24 January 2024 GMT Citerank: (2) 701037MfPH â Publications144B5ACA0, 717032Swetaprovo ChaudhuriSwetaprovo is an Associate Professor in the Institute for Aerospace Studies in the Faculty of Applied Science and Engineering at the University of Toronto.10019D3ABAB URL: DOI: https://doi.org/10.1063/5.0034032
| Excerpt / Summary [Physics of Fluids, 4 December 2020]
Identifying the relative importance of the different transmission routes of the SARS-CoV-2 virus is an urgent research priority. To that end, the different transmission routes and their role in determining the evolution of the Covid-19 pandemic are analyzed in this work. The probability of infection caused by inhaling virus-laden droplets (initial ejection diameters between 0.5 ”m and 750 ”m, therefore including both airborne and ballistic droplets) and the corresponding desiccated nuclei that mostly encapsulate the virions post droplet evaporation are individually calculated. At typical, air-conditioned yet quiescent indoor space, for average viral loading, cough droplets of initial diameter between 10 ”m and 50 ”m are found to have the highest infection probability. However, by the time they are inhaled, the diameters reduce to about 1/6th of their initial diameters. While the initially near unity infection probability due to droplets rapidly decays within the first 25 s, the small yet persistent infection probability of desiccated nuclei decays appreciably only by O (1000s) â , assuming that the virus sustains equally well within the dried droplet nuclei as in the droplets. Combined with molecular collision theory adapted to calculate the frequency of contact between the susceptible population and the droplet/nuclei cloud, infection rate constants are derived ab initio, leading to a susceptible-exposed-infectious-recovered-deceased model applicable for any respiratory eventâvector combination. The viral load, minimum infectious dose, sensitivity of the virus half-life to the phase of its vector, and dilution of the respiratory jet/puff by the entraining air are shown to mechanistically determine specific physical modes of transmission and variation in the basic reproduction number from first-principles calculations. |
Link[198] Effect of wetness on penetration dynamics of droplets impacted on facemasks
Author: Abhishek Saha, Sombuddha Bagchi, Saptarshi Basu, Swetaprovo Chaudhuri Publication date: 21 November 2021 Publication info: 74th Annual Meeting of the APS Division of Fluid Dynamics, Volume 66, Number 17 Cited by: David Price 7:55 PM 24 January 2024 GMT Citerank: (4) 701037MfPH â Publications144B5ACA0, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6, 715952Measles859FDEF6, 717032Swetaprovo ChaudhuriSwetaprovo is an Associate Professor in the Institute for Aerospace Studies in the Faculty of Applied Science and Engineering at the University of Toronto.10019D3ABAB URL:
| Excerpt / Summary [APS Division of Fluid Dynamics, 21 November 2021]
Properly designed facemasks can limit the spread of ballistic droplets and aerosol particles coming out of oral and nasal cavities during respiratory events, such as sneezing, coughing, singing, talking etc. Furthermore, it can also protect the user from inhaling small droplets, droplet nuclei, or aerosol particles. Thus, proper usage of facemasks can prevent the transmission of many diseases, including Covid19, influenza, measles, and the common cold. Although N95 masks are particularly designed to provide the best protection, various types of facemask became popular during the Covid19 pandemic due to a shortage of supply and high demand. In our recent study (Sharma et al. Sc. Adv. (2021) 7, eabf0452), we reported the fate of a respiratory droplet impacting on a dry facemask to show that larger droplets can penetrate the mask layers and undergo secondary atomizations leading to multiple smaller droplets. In this work, we focus on the effect of the wetness of the mask matrix on this atomization process. Indeed, due to the condensation process, longtime use renders the masks wet, and hence, its influence on the efficacy in blocking the droplet is worth investigating. We will present a regime map to show the penetration probability with impact velocity and wetness for two different types of masks. We will also present a scaling argument to explain the observed effects of wetness on penetration. |
Link[199] An exposition of facemask efficacy against large size cough droplets
Author: Shubham Sharma, Roven Pinto, Abhishek Saha, Swetaprovo Chaudhuri, Saptarshi Basu Publication date: 21 November 2021 Publication info: 74th Annual Meeting of the APS Division of Fluid Dynamics, Volume 66, Number 17, SundayâTuesday, November 21â23, 2021 Cited by: David Price 5:01 PM 25 January 2024 GMT Citerank: (3) 701037MfPH â Publications144B5ACA0, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6, 717032Swetaprovo ChaudhuriSwetaprovo is an Associate Professor in the Institute for Aerospace Studies in the Faculty of Applied Science and Engineering at the University of Toronto.10019D3ABAB URL: | Excerpt / Summary [74th Annual Meeting of the APS Division of Fluid Dynamics, 21 November 2021]
The usage of facemasks has been ubiquitously recommended worldwide as a physical barrier to the ejected droplet during respiratory events. This is an effective strategy for restricting various droplet-based disease transmission, as in the case of COVID-19. Although the N95 facemask has high efficacy against respiratory droplets, its accessibility/affordability for the general population is still deprived. As a possible solution, using a makeshift facemask (surgical or cotton facemasks) is generally advised by policymakers. Although such endorsement could be economical and accessible, quantitative analysis on the effectiveness of such facemasks is still lacking. Using a large-sized surrogate cough droplet, we identified an additional route of disease transmission, which involves atomization of large-sized cough droplets into numerous daughter droplets. It is shown that most of such atomized droplets are of sizes which is critical for aerosolization1. This suggested that the amount of aerosol generated (thereby the risk of infection) through this mechanism is higher than the earlier predictions based on mask filtration efficiencies alone. A scaling argument based on the energy balance of impact dynamics was obtained and verified using experiments to identify a criterion for droplet penetration through a mask layer. The parametric analysis was also carried, which involves droplet impact velocities (corresponding to different respiratory events), impact angles (corresponding to different mask orientations), mask fabrics (surgical and cotton facemasks), and different washing cycles. The obtained results are discussed in detail, and a recommendation of the most suitable fabric for making homemade facemasks is presented. |
Link[200] On secondary atomization and blockage of surrogate cough droplets in single- and multilayer face masks
Author: Shubham Sharma, Roven Pinto, Abhishek Saha, Swetaprovo Chaudhuri, Saptarshi Basu Publication date: 5 March 2021 Publication info: Science Advances, 7 (10), eabf0452 Cited by: David Price 8:44 PM 25 January 2024 GMT Citerank: (3) 701037MfPH â Publications144B5ACA0, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6, 717032Swetaprovo ChaudhuriSwetaprovo is an Associate Professor in the Institute for Aerospace Studies in the Faculty of Applied Science and Engineering at the University of Toronto.10019D3ABAB URL: DOI: https://doi.org/10.1126/sciadv.abf0452
| Excerpt / Summary [Science Advances, 5 March 2021]
Face masks prevent transmission of infectious respiratory diseases by blocking large droplets and aerosols during exhalation or inhalation. While three-layer masks are generally advised, many commonly available or makeshift masks contain single or double layers. Using carefully designed experiments involving high-speed imaging along with physics-based analysis, we show that high-momentum, large-sized (>250 micrometer) surrogate cough droplets can penetrate single- or double-layer mask material to a significant extent. The penetrated droplets can atomize into numerous much smaller (<100 micrometer) droplets, which could remain airborne for a significant time. The possibility of secondary atomization of high-momentum cough droplets by hydrodynamic focusing and extrusion through the microscale pores in the fibrous network of the single/double-layer mask material needs to be considered in determining mask efficacy. Three-layer masks can effectively block these droplets and thus could be ubiquitously used as a key tool against COVID-19 or similar respiratory diseases. |
Link[201] An opinion on the multiscale nature of Covid-19 type disease spread
Author: Swetaprovo Chaudhuri, Abhishek Saha, Saptarshi Basu Publication date: 1 May 2021 Publication info: Current Opinion in Colloid & Interface Science, 01 May 2021, 54:101462, PMID: 33967585 PMCID: PMC8088079 Cited by: David Price 11:30 PM 25 January 2024 GMT Citerank: (3) 701037MfPH â Publications144B5ACA0, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6, 717032Swetaprovo ChaudhuriSwetaprovo is an Associate Professor in the Institute for Aerospace Studies in the Faculty of Applied Science and Engineering at the University of Toronto.10019D3ABAB URL: DOI: https://doi.org/10.1016/j.cocis.2021.101462
| Excerpt / Summary [Current Opinion in Colloid & Interface Science, 1 May 2021]
Recognizing the multiscale, interdisciplinary nature of the Covid-19 transmission dynamics, we discuss some recent developments concerning an attempt to construct a disease spread model from the flow physics of infectious droplets and aerosols and the frequency of contact between susceptible individuals with the infectious aerosol cloud. Such an approach begins with the exhalation eventâspecific, respiratory droplet size distribution (both airborne/aerosolized and ballistic droplets), followed by tracking its evolution in the exhaled air to estimate the probability of infection and the rate constants of the disease spread model. The basic formulations and structure of submodels, experiments involved to validate those submodels, are discussed. Finally, in the context of preventive measures, respiratory dropletâface mask interactions are described. |
Link[202] Two-dimensional mathematical framework for evaporation dynamics of respiratory droplets
Author: Sreeparna Majee, Abhishek Saha, Swetaprovo Chaudhuri, Dipshikha Chakravortty, Saptarshi Basu Publication date: 1 October 2021 Publication info: Physics of Fluids, 33, 103302 (2021) Cited by: David Price 11:41 PM 25 January 2024 GMT Citerank: (2) 701037MfPH â Publications144B5ACA0, 717032Swetaprovo ChaudhuriSwetaprovo is an Associate Professor in the Institute for Aerospace Studies in the Faculty of Applied Science and Engineering at the University of Toronto.10019D3ABAB URL: DOI: https://doi.org/10.1063/5.0064635
| Excerpt / Summary [Physics of Fluids, 1 October 2021]
In majority of pandemics in human history, respiratory bio-aerosol is the most common route of transmission of diseases. These tiny droplets ejected through mouth and nose from an infected person during exhalation process like coughing, sneezing, speaking, and breathing consist of pathogens and a complex mixture of volatile and nonvolatile substances. A cloud of droplets ejected in such an event gets transmitted in the air, causing a series of coupled thermo-physical processes. Contemplating an individual airborne droplet in the cloud, boundary layers and wakes develop due to relative motion between the droplet and the ambient air. The complex phenomenon of the droplet's dynamics, such as shear-driven internal circulation of the liquid phase and Stefan flow due to vaporization or condensation, comes into effect. In this study, we present a mathematical description of the coupled subprocesses, including droplet aerodynamics, heat, and mass transfer, which were identified and subsequently solved. The presented two-dimensional model gives a complete analysis encompassing the gas phase coupled with the liquid phase responsible for the airborne droplet kinetics in the ambient environment. The transient inhomogeneity of temperature and concentration distribution in the liquid phase caused due to the convective and diffusive transports are captured in the 2D model. The evaporation time and distance traveled by droplets prior to nuclei or aerosol formation are computed for major geographical locations around the globe for nominal-windy conditions. The model presented can be used for determining the evaporation timescale of any viral or bacterial laden respiratory droplets across any geographical location. |
Link[203] Analysis of overdispersion in airborne transmission of COVID-19
Author: Swetaprovo Chaudhuri, Prasad Kasibhatla, Arnab Mukherjee, William Pan, Glenn Morrison, Sharmistha Mishra, Vijaya Kumar Murty Publication date: 31 May 2022 Publication info: Physics of Fluids 34, 051914 (2022) Cited by: David Price 0:06 AM 26 January 2024 GMT Citerank: (3) 679893Kumar MurtyProfessor Kumar Murty is in the Department of Mathematics at the University of Toronto. His research fields are Analytic Number Theory, Algebraic Number Theory, Arithmetic Algebraic Geometry and Information Security. He is the founder of the GANITA lab, co-founder of Prata Technologies and PerfectCloud. His interest in mathematics ranges from the pure study of the subject to its applications in data and information security.10019D3ABAB, 701037MfPH â Publications144B5ACA0, 717032Swetaprovo ChaudhuriSwetaprovo is an Associate Professor in the Institute for Aerospace Studies in the Faculty of Applied Science and Engineering at the University of Toronto.10019D3ABAB URL: DOI: https://doi.org/10.1063/5.0089347
| Excerpt / Summary [Physics of Fluids, 31 May 2022]
Superspreading events and overdispersion are hallmarks of the COVID-19 pandemic. However, the specific roles and influence of established viral and physical factors related to the mechanisms of transmission, on overdispersion, remain unresolved. We, therefore, conducted mechanistic modeling of SARS-CoV-2 point-source transmission by infectious aerosols using real-world occupancy data from more than 100â000 social contact settings in ten US metropolises. We found that 80% of secondary infections are predicted to arise from approximately 4% of index cases, which show up as a stretched tail in the probability density function of secondary infections per infectious case. Individual-level variability in viral load emerges as the dominant driver of overdispersion, followed by occupancy. We then derived an analytical function, which replicates the simulated overdispersion, and with which we demonstrate the effectiveness of potential mitigation strategies. Our analysis, connecting the mechanistic understanding of SARS-CoV-2 transmission by aerosols with observed large-scale epidemiological characteristics of COVID-19 outbreaks, adds an important dimension to the mounting body of evidence with regard to airborne transmission of SARS-CoV-2 and thereby emerges as a powerful tool toward assessing the probability of outbreaks and the potential impact of mitigation strategies on large scale disease dynamics. |
Link[204] Analysing the distribution of SARS-CoV-2 infections in schools: integrating model predictions with real world observations
Author: Arnab Mukherjee, Sharmistha Mishra, Vijay Kumar Murty, Swetaprovo Chaudhuri Publication date: 21 December 2023 Publication info: bioRxiv, 21 December 2023 Cited by: David Price 0:17 AM 26 January 2024 GMT Citerank: (6) 679880Sharmistha MishraSharmistha Mishra is an infectious disease physician and mathematical modeler and holds a Tier 2 Canadian Research Chair in Mathematical Modeling and Program Science.10019D3ABAB, 679893Kumar MurtyProfessor Kumar Murty is in the Department of Mathematics at the University of Toronto. His research fields are Analytic Number Theory, Algebraic Number Theory, Arithmetic Algebraic Geometry and Information Security. He is the founder of the GANITA lab, co-founder of Prata Technologies and PerfectCloud. His interest in mathematics ranges from the pure study of the subject to its applications in data and information security.10019D3ABAB, 701037MfPH â Publications144B5ACA0, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6, 715617Schools859FDEF6, 717032Swetaprovo ChaudhuriSwetaprovo is an Associate Professor in the Institute for Aerospace Studies in the Faculty of Applied Science and Engineering at the University of Toronto.10019D3ABAB URL: DOI: https://doi.org/10.1101/2023.12.21.572736; t
| Excerpt / Summary [bioRxiv, 21 December 2023]
School closures were used as strategies to mitigate transmission in the COVID-19 pandemic. Understanding the nature of SARS-CoV-2 outbreaks and the distribution of infections in classrooms could help inform targeted or âprecisionâ preventive measures and outbreak management in schools, in response to future pandemics. In this work, we derive an analytical model of Probability Density Function (PDF) of SARS-CoV-2 secondary infections and compare the model with infection data from all public schools in Ontario, Canada between September-December, 2021. The model accounts for major sources of variability in airborne transmission like viral load and dose-response (i.e., the human bodyâs response to pathogen exposure), air change rate, room dimension, and classroom occupancy. Comparisons between reported cases and the modeled PDF demonstrated the intrinsic overdispersed nature of the real-world and modeled distributions, but uncovered deviations stemming from an assumption of homogeneous spread within a classroom. The inclusion of near-field transmission effects resolved the discrepancy with improved quantitative agreement between the data and modeled distributions. This study provides a practical tool for predicting the size of outbreaks from one index infection, in closed spaces such as schools, and could be applied to inform more focused mitigation measures. |
Link[205] Impact of community mask mandates on SARS-CoV-2 transmission in Ontario after adjustment for differential testing by age and sex
Author: Amy Peng, Savana Bosco, Alison E Simmons, Ashleigh R Tuite, David N Fisman Publication date: 12 February 2024 Publication info: PNAS Nexus, Volume 3, Issue 2, February 2024, pgae065 Cited by: David Price 1:02 AM 28 February 2024 GMT Citerank: (4) 679755Ashleigh TuiteAshleigh Tuite is an Assistant Professor in the Epidemiology Division at the Dalla Lana School of Public Health at the University of Toronto.10019D3ABAB, 679777David FismanI am a Professor in the Division of Epidemiology at Division of Epidemiology, Dalla Lana School of Public Health at the University of Toronto. I am a Full Member of the School of Graduate Studies. I also have cross-appointments at the Institute of Health Policy, Management and Evaluation and the Department of Medicine, Faculty of Medicine. I serve as a Consultant in Infectious Diseases at the University Health Network.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6 URL: DOI: https://doi.org/10.1093/pnasnexus/pgae065
| Excerpt / Summary [PNAS Nexus, 12 February 2024]
Mask use for prevention of respiratory infectious disease transmission is not new but has proven controversial during the SARS-CoV-2 pandemic. In Ontario, Canada, irregular regional introduction of community mask mandates in 2020 created a quasi-experiment useful for evaluating the impact of such mandates; however, Ontario SARS-CoV-2 case counts were likely biased by testing focused on long-term care facilities and healthcare workers. We developed a regression-based method that allowed us to adjust cases for under-testing by age and gender. We evaluated mask mandate effects using count-based regression models with either unadjusted cases, or testing-adjusted case counts, as dependent variables. Models were used to estimate mask mandate effectiveness, and the fraction of SARS-CoV-2 cases, severe outcomes, and costs, averted by mask mandates. Models using unadjusted cases as dependent variables identified modest protective effects of mask mandates (range 31â42%), with variable statistical significance. Mask mandate effectiveness in models predicting test-adjusted case counts was higher, ranging from 49% (95% CI 44â53%) to 76% (95% CI 57â86%). The prevented fraction associated with mask mandates was 46% (95% CI 41â51%), with 290,000 clinical cases, 3,008 deaths, and loss of 29,038 quality-adjusted life years averted from 2020 June to December, representing $CDN 610 million in economic wealth. Under-testing in younger individuals biases estimates of SARS-CoV-2 infection risk and obscures the impact of public health preventive measures. After adjustment for under-testing, mask mandates emerged as highly effective. Community masking saved substantial numbers of lives, and prevented economic costs, during the SARS-CoV-2 pandemic in Ontario, Canada. |
Link[206] Examining the Influence of Imbalanced Social Contact Matrices in Epidemic Models
Author: Mackenzie A Hamilton, Jesse Knight, Sharmistha Mishra Publication date: 15 September 2023 Publication info: American Journal of Epidemiology, Volume 193, Issue 2, February 2024, Pages 339â347 Cited by: David Price 4:34 PM 28 February 2024 GMT Citerank: (3) 679880Sharmistha MishraSharmistha Mishra is an infectious disease physician and mathematical modeler and holds a Tier 2 Canadian Research Chair in Mathematical Modeling and Program Science.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6 URL: DOI: https://doi.org/10.1093/aje/kwad185
| Excerpt / Summary [American Journal of Epidemiology, February 2024]
Transmissible infections such as those caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread according to who contacts whom. Therefore, many epidemic models incorporate contact patterns through contact matrices. Contact matrices can be generated from social contact survey data. However, the resulting matrices are often imbalanced, such that the total number of contacts reported by group A with group B do not match those reported by group B with group A. We examined the theoretical influence of imbalanced contact matrices on the estimated basic reproduction number (R0). We then explored how imbalanced matrices may bias model-based epidemic projections using an illustrative simulation model of SARS-CoV-2 with 2 age groups (<15 and â„15 years). Models with imbalanced matrices underestimated the initial spread of SARS-CoV-2, had later time to peak incidence, and had smaller peak incidence. Imbalanced matrices also influenced cumulative infections observed per age group, as well as the estimated impact of an age-specific vaccination strategy. Stratified transmission models that do not consider contact balancing may generate biased projections of epidemic trajectory and the impact of targeted public health interventions. Therefore, modeling studies should implement and report methods used to balance contact matrices for stratified transmission models. |
Link[207] Enhancing detection of SARS-CoV-2 re-infections using longitudinal sero-monitoring: demonstration of a methodology in a cohort of people experiencing homelessness in Toronto, Canada
Author: Lucie Richard, Rosane Nisenbaum, Karen Colwill, Sharmistha Mishra, Roya M. Dayam, Michael Liu, Cheryl Pedersen, Anne-Claude Gingras, Stephen W. Hwang Publication date: 2 February 2024 Publication info: BMC Infectious Diseases, Volume 24, Article number: 125 (2024) Cited by: David Price 4:48 PM 28 February 2024 GMT Citerank: (3) 679880Sharmistha MishraSharmistha Mishra is an infectious disease physician and mathematical modeler and holds a Tier 2 Canadian Research Chair in Mathematical Modeling and Program Science.10019D3ABAB, 708809Homelessness859FDEF6, 715831Diagnostic testing859FDEF6 URL: DOI: https://doi.org/10.1186/s12879-024-09013-9
| Excerpt / Summary [BMC Infectious Diseases, 2 February 2024]
Background: Accurate estimation of SARS-CoV-2 re-infection is crucial to understanding the connection between infection burden and adverse outcomes. However, relying solely on PCR testing results in underreporting. We present a novel approach that includes longitudinal serologic data, and compared it against testing alone among people experiencing homelessness.
Methods: We recruited 736 individuals experiencing homelessness in Toronto, Canada, between June and September 2021. Participants completed surveys and provided saliva and blood serology samples every three months over 12 months of follow-up. Re-infections were defined as: positive PCR or rapid antigen test (RAT) resultsâ>â90 days after initial infection; new serologic evidence of infection among individuals with previous infection who sero-reverted; or increases in anti-nucleocapsid in seropositive individuals whose levels had begun to decrease.
Results: Among 381 participants at risk, we detected 37 re-infections through PCR/RAT and 98 re-infections through longitudinal serology. The comprehensive method identified 37.4 re-infection events per 100 person-years, more than four-fold more than the rate detected through PCR/RAT alone (9.0 events/100 person-years). Almost all test-confirmed re-infections (85%) were also detectable by longitudinal serology.
Conclusions: Longitudinal serology significantly enhances the detection of SARS-CoV-2 re-infections. Our findings underscore the importance and value of combining data sources for effective research and public health surveillance. |
Link[208] Social network risk factors and COVID-19 vaccination: A cross-sectional survey study
Author: Ally Memedovich, Taylor Orr, Aidan Hollis, Charleen Salmon, Jia Hu, Kate Zinszer, Tyler Williamson, Reed F. Beall Publication date: 6 February 2024 Publication info: Vaccine, Volume 42, Issue 4, 2024, Pages 891-911, ISSN 0264-410X Cited by: David Price 6:20 PM 29 February 2024 GMT Citerank: (3) 679891Tyler WilliamsonTyler Williamson is the Director of the Centre for Health Informatics, formerly the Associate Director. In addition, he is an Associate Professor of Biostatistics in the Department of Community Health Sciences as well as the Director of the Health Data Science and Biostatistics Diploma Program at the University of Calgary.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1016/j.vaccine.2024.01.012
| Excerpt / Summary [Vaccine, 6 February 2024]
Background: Social networks have an important impact on our health behaviours, including vaccination. Peopleâs vaccination beliefs tend to mirror those of their social network. As social networks are homogenous in many ways, we sought to determine in the context of COVID-19 which factors were most predictive of belonging to a mostly vaccinated or unvaccinated social group.
Methods: We conducted a cross-sectional survey among Canadian residents in November and December 2021. Participants were asked about the vaccination status of their social networks their beliefs relating to COVID-19, and various sociodemographic factors. Respondents were split into three groups based on social network vaccination: low-, medium-, and high-risk. Chi-squared tests tested associations between factors and risk groups, and an ordinal logistic model was created to determine their direction and strength.
Results: Most respondents (81.1 %) were classified as low risk (i.e., a mostly vaccinated social network) and few respondents (3.7 %) were classified as high-risk (i.e., an unvaccinated social group). Both the chi-square test (29.2 % difference between the low- and high- risk groups [1.8 % vs. 31.0 %], p < 0.001) and the ordinal logistic model (odds ratio between the low- and high-risk groups: 14.45, p < 0.01) found that respondentsâ perceptions of COVID-19 as a ânot at all seriousâ risk to Canadians was the most powerful predictor of belonging to a predominantly unvaccinated social circle. The model also found that those in mostly unvaccinated social circles also more often reported severe COVID-19 symptoms (odds ratio between the low- and high-risk groups: 2.26, p < 0.05).
Conclusion: Perception of COVID-19 as a threat to others may signal communities with lower vaccination coverage and higher risk of severe outcomes. This may have implications for strategies to improve public outreach, messaging, and planning for downstream consequences of low intervention uptake. |
Link[209] Factors associated with SARS-CoV-2 infection in unvaccinated children and young adults
Author: Sarah L. Silverberg, Hennady P. Shulha, Brynn McMillan, Guanyuhui He, Amy Lee, Ana Citlali MĂĄrquez, Sofia R. Bartlett, Vivek Gill, Bahaa Abu-Raya, Julie A. Bettinger, Adriana Cabrera, Daniel Coombs, Soren Gantt, David M. Goldfarb, Laura SauvĂ©, Mel Krajden, Muhammad Morshed, Inna Sekirov, Agatha N. Jassem, Manish Sadarangani Publication date: 15 January 2024 Publication info: BMC Infectious Diseases, Volume 24, Article number: 91 (2024) Cited by: David Price 6:41 PM 29 February 2024 GMT Citerank: (3) 679773Daniel CoombsProfessor and Head of the Mathematics Department in the Institute of Applied Mathematics at the University of British Columbia.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1186/s12879-023-08950-1
| Excerpt / Summary [BMC Infectious Diseases, 15 January 2024]
Background and objectives: Pediatric COVID-19 cases are often mild or asymptomatic, which has complicated estimations of disease burden using existing testing practices. We aimed to determine the age-specific population seropositivity and risk factors of SARS-CoV-2 seropositivity among children and young adults during the pandemic in British Columbia (BC).
Methods: We conducted two cross-sectional serosurveys: phase 1 enrolled children and adultsâ<â25 years between November 2020-May 2021 and phase 2 enrolled childrenâ<â10 years between June 2021-May 2022 in BC. Participants completed electronic surveys and self-collected finger-prick dried blood spot (DBS) samples. Samples were tested for immunoglobulin G antibodies against ancestral spike protein (S). Descriptive statistics from survey data were reported and two multivariable analyses were conducted to evaluate factors associated with seropositivity.
Results: A total of 2864 participants were enrolled, of which 95/2167 (4.4%) participants were S-seropositive in phase 1 across all ages, and 61/697 (8.8%) unvaccinated children aged under ten years were S-seropositive in phase 2. Overall, South Asian participants had a higher seropositivity than other ethnicities (13.5% vs. 5.2%). Of 156 seropositive participants in both phases, 120 had no prior positive SARS-CoV-2 test. Young infants and young adults had the highest reported seropositivity rates (7.0% and 7.2% respectively vs. 3.0-5.6% across other age groups).
Conclusions: SARS-CoV-2 seropositivity among unvaccinated children and young adults was low in May 2022, and South Asians were disproportionately infected. This work demonstrates the need for improved diagnostics and reporting strategies that account for age-specific differences in pandemic dynamics and acceptability of testing mechanisms. |
Link[210] A Bayesian framework for modeling COVID-19 case numbers through longitudinal monitoring of SARS-CoV-2 RNA in wastewater
Author: Xiaotian Dai, Nicole Acosta, Xuewen Lu, Casey R. J. Hubert, Jangwoo Lee, Kevin Frankowski, Maria A. Bautista, Barbara J. Waddell, Kristine Du, Janine McCalder, Jon Meddings, Norma Ruecker, Tyler Williamson, Danielle A. Southern, Jordan Hollman, Gopal Achari, M. Cathryn Ryan, Steve E. Hrudey, Bonita E. Lee, Xiaoli Pang, Rhonda G. Clark, Michael D. Parkins, Thierry Chekouo Publication date: 14 January 2024 Publication info: Statistics in Medicine, Volume 43, Issue 6 p. 1153-1169 Cited by: David Price 4:23 PM 1 March 2024 GMT Citerank: (4) 679891Tyler WilliamsonTyler Williamson is the Director of the Centre for Health Informatics, formerly the Associate Director. In addition, he is an Associate Professor of Biostatistics in the Department of Community Health Sciences as well as the Director of the Health Data Science and Biostatistics Diploma Program at the University of Calgary.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704022Surveillance859FDEF6, 708744Wastewater-based surveillance (WBS) 859FDEF6 URL: DOI: https://doi.org/10.1002/sim.10009
| Excerpt / Summary [Statistics in Medicine, 14 January 2024]
Wastewater-based surveillance has become an important tool for research groups and public health agencies investigating and monitoring the COVID-19 pandemic and other public health emergencies including other pathogens and drug abuse. While there is an emerging body of evidence exploring the possibility of predicting COVID-19 infections from wastewater signals, there remain significant challenges for statistical modeling. Longitudinal observations of viral copies in municipal wastewater can be influenced by noisy datasets and missing values with irregular and sparse samplings. We propose an integrative Bayesian framework to predict daily positive cases from weekly wastewater observations with missing values via functional data analysis techniques. In a unified procedure, the proposed analysis models severe acute respiratory syndrome coronavirus-2 RNA wastewater signals as a realization of a smooth process with error and combines the smooth process with COVID-19 cases to evaluate the prediction of positive cases. We demonstrate that the proposed framework can achieve these objectives with high predictive accuracies through simulated and observed real data. |
Link[211] Risk factors for recognized and unrecognized SARS-CoV-2 infection: a seroepidemiologic analysis of the Prospective Urban Rural Epidemiology (PURE) study
Author: Darryl P. Leong, Mark Loeb, Prem K. Mony, Sumathy Rangarajan, Maha Mushtaha, Matthew S. Miller, Mary Dias, Sergey Yegorov, Mamatha V, Ozge Telci Caklili, Ahmet Temizhan, Andrzej Szuba, Marc Evans M. Abat, Nafiza Mat-Nasir, Maria Luz Diaz, Hamda Khansaheb, Patricio Lopez-Jaramillo, MyLinh Duong, Koon K. Teo, Paul Poirier, Gustavo Oliveira, Ălvaro Avezum, Salim Yusuf Publication date: 12 January 2024 Publication info: Epidemiology, 12 January 2024 Cited by: David Price 4:30 PM 1 March 2024 GMT Citerank: (2) 679843Mark LoebProfessor at Pathology and Molecular Medicine (primary), Clinical Epidemiology and Biostatistics in the Department of Pathology and Molecular Medicine at McMaster University. Associate Member, Medicine and Michael G. DeGroote Chair in Infectious Diseases.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0 URL: DOI: https://doi.org/10.1128/spectrum.01492-23
| Excerpt / Summary [Epidemiology, 12 January 2024]
There are limited data on individual risk factors for SARS-CoV-2 infection (including unrecognized infection). In this seroepidemiologic substudy of an ongoing prospective cohort study of community-dwelling adults, participants were thoroughly characterized pre-pandemic. The SARS-CoV-2 infection was ascertained by serology. Among 8,719 participants from 11 high-, middle-, and low-income countries, 3,009 (35%) were seropositive for SARS-CoV-2. Characteristics independently associated with seropositivity were younger age (odds ratio, OR; 95% confidence interval, CI, per five-year increase: 0.95; 0.91â0.98) and body mass index >25 kg/m2 (OR, 95% CI: 1.16, 1.01â1.34). Smoking (as compared with never smoking, OR, 95% CI: 0.83, 0.70â0.97) and COVID-19 vaccination (OR, 95% CI: 0.70, 0.60â0.82) were associated with a reduced risk of seropositivity. Among seropositive participants, 83% were unaware of having been infected with SARS-CoV-2. Seropositivity and a lack of awareness of infection were more common in lower-income countries. The COVID-19 vaccination reduces the risk of SARS-CoV-2 infection (including recognized and unrecognized infections). Overweight or obesity is an independent risk factor for SARS-CoV-2 infection. Infection and lack of infection awareness are more common in lower-income countries. |
Link[212] Long-Term Dynamics of COVID-19 in a Multi-strain Model
Author: Elisha B. Are, Jessica Stockdale, Caroline Colijn Publication date: 7 August 2023 Publication info: In: David, J., Wu, J. (eds) Mathematics of Public Health. Fields Institute Communications, vol 88. Springer, Cham. Cited by: David Price 0:10 AM 4 March 2024 GMT Citerank: (2) 679761Caroline ColijnDr. Caroline Colijn works at the interface of mathematics, evolution, infection and public health, and leads the MAGPIE research group. She joined SFU's Mathematics Department in 2018 as a Canada 150 Research Chair in Mathematics for Infection, Evolution and Public Health. She has broad interests in applications of mathematics to questions in evolution and public health, and was a founding member of Imperial College London's Centre for the Mathematics of Precision Healthcare.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0 URL: DOI: https://doi.org/10.1007/978-3-031-40805-2_11
| Excerpt / Summary [Mathematics of Public Health, 7 August 2023]
The continuous emergence and spread of new variants of SARS-CoV-2 has added an extra layer of complexity in the effort to effectively control the pandemic. The long-term impact of the new variants, and how they will interplay with population immunity and other factors to shape future resurgence of infection, is not fully understood. To provide some insight on this, we simulate future SARS-CoV-2 variants assuming Poisson process arrival times in British Columbia, Canada, sampling their transmissibility and immune escape capacity from a multivariate log-normal distribution. Using a two-strain deterministic model that incorporates waning of immunity and breakthrough infection, we explore the long-term dynamics of COVID-19 in British Columbia. Our model predicts multiple waves of resurgence of SARS-CoV-2 infection modulated by transmissibility, immune escape capacity and variantsâ arrival rates, without achieving stable endemicity within the next 3 years. The peak and rate of resurgence of infection waves can be reduced by continuous boosting of immunity with efficacious vaccines, while proactive measures are employed to encourage booster uptake. |
Link[213] Impact of immune evasion, waning and boosting on dynamics of population mixing between a vaccinated majority and unvaccinated minority
Author: David N. Fisman, Afia Amoako, Alison Simmons, Ashleigh R. Tuite Publication date: 4 April 2024 Publication info: PLoS ONE 19(4): e0297093 Cited by: David Price 11:50 PM 14 April 2024 GMT Citerank: (4) 679755Ashleigh TuiteAshleigh Tuite is an Assistant Professor in the Epidemiology Division at the Dalla Lana School of Public Health at the University of Toronto.10019D3ABAB, 679777David FismanI am a Professor in the Division of Epidemiology at Division of Epidemiology, Dalla Lana School of Public Health at the University of Toronto. I am a Full Member of the School of Graduate Studies. I also have cross-appointments at the Institute of Health Policy, Management and Evaluation and the Department of Medicine, Faculty of Medicine. I serve as a Consultant in Infectious Diseases at the University Health Network.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1371/journal.pone.0297093
| Excerpt / Summary [PLoS ONE, 4 April 2024]
Background: We previously demonstrated that when vaccines prevent infection, the dynamics of mixing between vaccinated and unvaccinated sub-populations is such that use of imperfect vaccines markedly decreases risk for vaccinated people, and for the population overall. Risks to vaccinated people accrue disproportionately from contact with unvaccinated people. In the context of the emergence of Omicron SARS-CoV-2 and evolving understanding of SARS-CoV-2 epidemiology, we updated our analysis to evaluate whether our earlier conclusions remained valid.
Methods: We modified a previously published Susceptible-Infectious-Recovered (SIR) compartmental model of SARS-CoV-2 with two connected sub-populations: vaccinated and unvaccinated, with non-random mixing between groups. Our expanded model incorporates diminished vaccine efficacy for preventing infection with the emergence of Omicron SARS-CoV-2 variants, waning immunity, the impact of prior immune experience on infectivity, âhybridâ effects of infection in previously vaccinated individuals, and booster vaccination. We evaluated the dynamics of an epidemic within each subgroup and in the overall population over a 10-year time horizon.
Results: Even with vaccine efficacy as low as 20%, and in the presence of waning immunity, the incidence of COVID-19 in the vaccinated subpopulation was lower than that among the unvaccinated population across the full 10-year time horizon. The cumulative risk of infection was 3â4 fold higher among unvaccinated people than among vaccinated people, and unvaccinated people contributed to infection risk among vaccinated individuals at twice the rate that would have been expected based on the frequency of contacts. These findings were robust across a range of assumptions around the rate of waning immunity, the impact of âhybrid immunityâ, frequency of boosting, and the impact of prior infection on infectivity in unvaccinated people.
Interpretation: Although the emergence of the Omicron variants of SARS-CoV-2 has diminished the protective effects of vaccination against infection with SARS-CoV-2, updating our earlier model to incorporate loss of immunity, diminished vaccine efficacy and a longer time horizon, does not qualitatively change our earlier conclusions. Vaccination against SARS-CoV-2 continues to diminish the risk of infection among vaccinated people and in the population as a whole. By contrast, the risk of infection among vaccinated people accrues disproportionately from contact with unvaccinated people. |
Link[214] Nasopharyngeal angiotensin converting enzyme 2 (ACE2) expression as a risk-factor for SARS-CoV-2 transmission in concurrent hospital associated outbreaks
Author: Aidan M. Nikiforuk, Kevin S. Kuchinski, Katy Short, Susan Roman, Mike A. Irvine, Natalie Prystajecky, Agatha N. Jassem, David M. Patrick, Inna Sekirov Publication date: 26 February 2024 Publication info: BMC Infectious Diseases, Volume 24, Article number: 262 (2024) Cited by: David Price 0:00 AM 15 April 2024 GMT Citerank: (3) 679854Natalie Anne PrystajeckyNatalie Prystajecky is the program head for the Environmental Microbiology program at the BCCDC Public Health Laboratory. She is also a clinical associate professor in the Department of Pathology & Laboratory Medicine at UBC.10019D3ABAB, 685420Hospitals16289D5D4, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0 URL: DOI: https://doi.org/10.1186/s12879-024-09067-9
| Excerpt / Summary [BMC Infectious Diseases, 26 February 2024]
Background: Widespread human-to-human transmission of the severe acute respiratory syndrome coronavirus two (SARS-CoV-2) stems from a strong affinity for the cellular receptor angiotensin converting enzyme two (ACE2). We investigate the relationship between a patientâs nasopharyngeal ACE2 transcription and secondary transmission within a series of concurrent hospital associated SARS-CoV-2 outbreaks in British Columbia, Canada.
Methods: Epidemiological case data from the outbreak investigations was merged with public health laboratory records and viral lineage calls, from whole genome sequencing, to reconstruct the concurrent outbreaks using infection tracing transmission network analysis. ACE2 transcription and RNA viral load were measured by quantitative real-time polymerase chain reaction. The transmission network was resolved to calculate the number of potential secondary cases. Bivariate and multivariable analyses using Poisson and Negative Binomial regression models was performed to estimate the association between ACE2 transcription the number of SARS-CoV-2 secondary cases.
Results: The infection tracing transmission network provided nâ=â76 potential transmission events across nâ=â103 cases. Bivariate comparisons found that on average ACE2 transcription did not differ between patients and healthcare workers (Pâ=â0.86). High ACE2 transcription was observed in 98.6% of transmission events, either the primary or secondary case had above average ACE2. Multivariable analysis found that the association between ACE2 transcription (log2 fold-change) and the number of secondary transmission events differs between patients and healthcare workers. In health care workers Negative Binomial regression estimated that a one-unit change in ACE2 transcription decreases the number of secondary cases (ÎČ = -0.132 (95%CI: -0.255 to -0.0181) adjusting for RNA viral load. Conversely, in patients a one-unit change in ACE2 transcription increases the number of secondary cases (ÎČâ=â0.187 (95% CI: 0.0101 to 0.370) adjusting for RNA viral load. Sensitivity analysis found no significant relationship between ACE2 and secondary transmission in health care workers and confirmed the positive association among patients.
Conclusion: Our study suggests that ACE2 transcription has a positive association with SARS-CoV-2 secondary transmission in admitted inpatients, but not health care workers in concurrent hospital associated outbreaks, and it should be further investigated as a risk-factor for viral transmission. |
Link[215] Endemic does not mean constant as SARS-CoV-2 continues to evolve
Author: Sarah P Otto, Ailene MacPherson, Caroline Colijn Publication date: 9 March 2024 Publication info: Evolution, Volume 78, Issue 6, 1 June 2024, Pages 1092â1108, Cited by: David Price 2:55 PM 30 July 2024 GMT Citerank: (4) 679761Caroline ColijnDr. Caroline Colijn works at the interface of mathematics, evolution, infection and public health, and leads the MAGPIE research group. She joined SFU's Mathematics Department in 2018 as a Canada 150 Research Chair in Mathematics for Infection, Evolution and Public Health. She has broad interests in applications of mathematics to questions in evolution and public health, and was a founding member of Imperial College London's Centre for the Mathematics of Precision Healthcare.10019D3ABAB, 679875Sarah OttoProfessor in Zoology. Theoretical biologist, Canada Research Chair in Theoretical and Experimental Evolution, and Killam Professor at the University of British Columbia.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 722446Covid-19Covid-19 » Who. » Sarah Otto10000FFFACD URL: DOI: https://doi.org/10.1093/evolut/qpae041
| Excerpt / Summary [Evolution, 1 June 2024]
COVID-19 has become endemic, with dynamics that reflect the waning of immunity and re-exposure, by contrast to the epidemic phase driven by exposure in immunologically naĂŻve populations. Endemic does not, however, mean constant. Further evolution of SARS-CoV-2, as well as changes in behavior and public health policy, continue to play a major role in the endemic load of disease and mortality. In this article, we analyze evolutionary models to explore the impact that a newly arising variant can have on the short-term and longer-term endemic load, characterizing how these impacts depend on the transmission and immunological properties of the variants. We describe how evolutionary changes in the virus will increase the endemic load most for a persistently immune-escape variant, by an intermediate amount for a more transmissible variant, and least for a transiently immune-escape variant. Balancing the tendency for evolution to favor variants that increase the endemic load, we explore the impact of vaccination strategies and non-pharmaceutical interventions that can counter these increases in the impact of disease. We end with some open questions about the future of COVID-19 as an endemic disease. |
Link[216] A Joint Temporal Model for Hospitalizations and ICU Admissions Due to COVID-19 in Quebec
Author: Mariana Carmona-Baez, Alexandra M. Schmidt, Shirin Golchi, David Buckeridge Publication date: 6 September 2024 Publication info: Stat, Volume 13, Issue 3 e70000, 6 September 2024 Cited by: David Price 1:19 PM 2 December 2024 GMT Citerank: (4) 679775David BuckeridgeDavid is a Professor in the School of Population and Global Health at McGill University, where he directs the Surveillance Lab, an interdisciplinary group that develops, implements, and evaluates novel computational methods for population health surveillance. He is also the Chief Digital Health Officer at the McGill University Health Center where he directs strategy on digital transformation and analytics and he is an Associate Member with the Montreal Institute for Learning Algorithms (Mila).10019D3ABAB, 685420Hospitals16289D5D4, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 728389Covid-19Covid-19 » Who. » David Buckeridge10000FFFACD URL: DOI: https://doi.org/10.1002/sta4.70000
| Excerpt / Summary [Stat, 6 September 2024]
Infectious respiratory diseases have been of interest in recent years for the great burden they place on health systems, for instance, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that caused the global COVID-19 pandemic. As many of these diseases might require hospitalization and even intensive care unit (ICU) admission, understanding the joint dynamics of hospitalizations and ICU admissions across time and different groups of the population remains of great importance. We aim to understand the joint evolution of hospital and ICU admissions given COVID-19 test-positive cases in the province of Quebec, Canada. We obtain the daily counts, by age group, on the number of confirmed COVID-19 cases, the number of hospitalizations and the number of ICU admissions due to COVID-19, from March 2020 through October 2021 in Quebec. We propose a joint Bayesian generalized dynamic linear model for the number of hospitalizations and ICU admissions to study their temporal trends and possible associations with sex and age group. Additionally, we use transfer functions to investigate if there is a memory effect of the number of cases on hospitalizations across the different age groups. The results suggest that there is a clear distinction in the patterns of hospitalizations and ICU admissions across age groups and that the number of cases has a persistent effect on the rate of hospitalization. |
Link[217] Canadian health care providers' and education workers' hesitance to receive original and bivalent COVID-19 vaccines
Author: Brenda L. Coleman, Iris Gutmanis, Susan J. Bondy, Robyn Harrison, Joanne Langley, Kailey Fischer, Curtis Cooper, Louis Valiquette, Matthew P. Muller, Jeff Powis, Dawn Bowdish, Kevin Katz, Mark Loeb, Marek Smieja, Shelly A. McNeil, Samira Mubareka, Jeya Nadarajah, Saranya Arnoldo, Allison McGeer Publication date: 24 October 2024 Publication info: Vaccine, Volume 42, Issue 24, 24 October 2024, 126271, ISSN 0264-410X, Cited by: David Price 1:40 PM 2 December 2024 GMT Citerank: (5) 679843Mark LoebProfessor at Pathology and Molecular Medicine (primary), Clinical Epidemiology and Biostatistics in the Department of Pathology and Molecular Medicine at McMaster University. Associate Member, Medicine and Michael G. DeGroote Chair in Infectious Diseases.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6, 728390Covid-19Covid-19 » Who. » Mark Loeb10000FFFACD, 728391VaccinationVaccination » Who. » Mark Loeb10000FFFACD URL: DOI: https://doi.org/10.1016/j.vaccine.2024.126271
| Excerpt / Summary [Vaccine, 24 October 2024]
Background: The demand for COVID-19 vaccines has diminished as the pandemic lingers. Understanding vaccine hesitancy among essential workers is important in reducing the impact of future pandemics by providing effective immunization programs delivered expeditiously.
Method: Two surveys exploring COVID-19 vaccine acceptance in 2021 and 2022 were conducted in cohorts of health care providers (HCP) and education workers participating in prospective studies of COVID-19 illnesses and vaccine uptake. Demographic factors and opinions about vaccines (monovalent and bivalent) and public health measures were collected in these self-reported surveys. Modified multivariable Poisson regression was used to determine factors associated with hesitancy.
Results: In 2021, 3 % of 2061 HCP and 6 % of 3417 education workers reported hesitancy (p < 0.001). In December 2022, 21 % of 868 HCP and 24 % of 1457 education workers reported being hesitant to receive a bivalent vaccine (p = 0.09). Hesitance to be vaccinated with the monovalent vaccines was associated with earlier date of survey completion, later receipt of first COVID-19 vaccine dose, no influenza vaccination, and less worry about becoming ill with COVID-19. Factors associated with hesitance to be vaccinated with a bivalent vaccine that were common to both cohorts were receipt of two or fewer previous COVID-19 doses and lower certainty that the vaccines were safe and effective.
Conclusion: Education workers were somewhat more likely than HCP to report being hesitant to receive COVID-19 vaccines but reasons for hesitancy were similar. Hesitancy was associated with non-receipt of previous vaccines (i.e., previous behaviour), less concern about being infected with SARS-CoV-2, and concerns about the safety and effectiveness of vaccines for both cohorts. Maintaining inter-pandemic trust in vaccines, ensuring rapid data generation during pandemics regarding vaccine safety and effectiveness, and effective and transparent communication about these data are all needed to support pandemic vaccination programs. |
Link[218] Canada needs a national COVID-19 inquiry now
Author: David Fisman, Jillian Horton, Matthew Oliver, Mark Ungrin, Joseph Vipond, Julia M. Wright, Dick Zoutman Publication date: 15 November 2024 Publication info: BMC Medicine, Volume 22, Article number: 537 (2024) Cited by: David Price 2:01 PM 2 December 2024 GMT Citerank: (3) 679777David FismanI am a Professor in the Division of Epidemiology at Division of Epidemiology, Dalla Lana School of Public Health at the University of Toronto. I am a Full Member of the School of Graduate Studies. I also have cross-appointments at the Institute of Health Policy, Management and Evaluation and the Department of Medicine, Faculty of Medicine. I serve as a Consultant in Infectious Diseases at the University Health Network.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 728394Covid-19Covid-19 » Who. » David Fisman10000FFFACD URL: DOI: https://doi.org/10.1186/s12916-024-03756-7
| Excerpt / Summary [BMC Medicine, 15 November 2024]
Background: We are now in the fifth year of an ongoing pandemic, and Canada continues to experience significant surges of COVID-19 infections. In addition to the acute impacts of deaths and hospitalizations, there is growing awareness of an accumulation of organ damage and disability which is building a âhealth debtâ that will affect Canadians for decades to come. Calls in 2023 for an inquiry into the handling of the COVID-19 pandemic went unheeded, despite relevant precedent. Canada urgently needs a comprehensive review of its successes and failures to chart a better response in the near- and long-term.
Main body: While Canada fared better than many comparators in the early years of the COVID-19 pandemic, it is clearly still in a public health crisis. Infections are not only affecting Canadiansâ daily lives but also eroding healthcare capacity. Post-COVID condition is having accumulating and profound individual, social, and economic consequences.
An inquiry is needed to understand the current evidence underlying policy choices, identify a better course of action on various fronts, and build resilience. More must be done to reduce transmission, including a serious public education campaign to better inform Canadians about COVID and effective mitigations, especially the benefits of respirator masks. We need a national standard for indoor air quality to make indoor public spaces safer, particularly schools. Data collection must be more robust, especially to understand and mitigate the disproportionate impacts on under-served communities and high-risk populations. General confidence in public health must be rebuilt, with a focus on communication and transparency. In particular, the wide variation in provincial policies has sown mistrust: evidence-based policy should be consistent. Finally, Canadaâs early success in vaccination has collapsed, and this development needs a careful post-mortem.
Conclusions: A complete investigation of Canadaâs response to the pandemic is not yet possible because that response is still ongoing and, while we have learned much, there remain areas of dispute and uncertainty. However, an inquiry is needed to conduct a rapid assessment of the current evidence and policies and provide recommendations on how to improve in 2025 and beyond as well as guidance for future pandemics. |
Link[219] The political economy of epidemic management
Author: David McAdams, Troy Day Publication date: 28 August 2024 Publication info: Review of Economic Design, 28 August 2024 Cited by: David Price 2:06 PM 2 December 2024 GMT Citerank: (4) 679890Troy DayTroy Day is a Professor and the Associate Head of the Department of Mathematics and Statistics at Queenâs University. He is an applied mathematician whose research focuses on dynamical systems, optimization, and game theory, applied to models of infectious disease dynamics and evolutionary biology.10019D3ABAB, 703957Economics859FDEF6, 728395Covid-19Covid-19 » Who. » Troy Day10000FFFACD, 728396EconomicsEconomics » Who. » Troy Day10000FFFACD URL: DOI: https://doi.org/10.1007/s10058-024-00357-x
| Excerpt / Summary [Review of Economic Design, 28 August 2024]
During an infectious-disease epidemic, a political leader imposes âstay-at-home ordersâ (limiting activity) or âgo-out ordersâ (mandating activity) whenever preferred by the majority of the citizenry over the no-intervention status quo. We characterize the resulting equilibrium epidemic trajectory in an economic-epidemiological model that allows for asymptomatic infection and social-economic complementarities of activity, assuming that citizens are myopic optimizers. We find that the qualitative features of equilibrium policy dynamics hinge critically on whether the pathogen is transmitted before or after infected people have developed symptoms. If transmission only occurs symptomatically, then the leader never imposes stay-at-home orders on the healthy but may impose go-out orders during some phases of the epidemic. However, if transmission occurs asymptomatically, the leader never imposes go-out orders on the healthy. |
Link[220] The Clinical Severity of COVID-19 Variants of Concern: Retrospective Population-Based Analysis
Author: Sean P Harrigan, HĂ©ctor A VelĂĄsquez GarcĂa, Younathan Abdia, James Wilton, Natalie Prystajecky, John Tyson, Chris Fjell, Linda Hoang, Jeffrey C Kwong, Sharmistha Mishra, Linwei Wang, Beate Sander, Naveed Z Janjua, Hind Sbihi Publication date: 27 August 2024 Publication info: JMIR Public Health Surveill 2024;10:e45513 Cited by: David Price 2:21 PM 5 December 2024 GMT Citerank: (5) 679757Beate SanderCanada Research Chair in Economics of Infectious Diseases and Director, Health Modeling & Health Economics and Population Health Economics Research at THETA (Toronto Health Economics and Technology Assessment Collaborative).10019D3ABAB, 679854Natalie Anne PrystajeckyNatalie Prystajecky is the program head for the Environmental Microbiology program at the BCCDC Public Health Laboratory. She is also a clinical associate professor in the Department of Pathology & Laboratory Medicine at UBC.10019D3ABAB, 679856Naveed Zafar JanjuaDr. Naveed Zafar Janjua is an epidemiologist and senior scientist at the BC Centre for Disease Control and Clinical Associate Professor at School of Population and Public Health, University of British Columbia. Dr. Janjua is a Medical Doctor (MBBS) with a Masters of Science (MSc) degree in Epidemiology & Biostatistics and Doctorate in Public Health (DrPH). 10019D3ABAB, 679880Sharmistha MishraSharmistha Mishra is an infectious disease physician and mathematical modeler and holds a Tier 2 Canadian Research Chair in Mathematical Modeling and Program Science.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0 URL: DOI: https://doi.org/10.2196/45513
| Excerpt / Summary [JMIR Public Health Surveillance, 27 August 2024]
Background: SARS-CoV-2 variants of concern (VOCs) emerged and rapidly replaced the original strain worldwide. The increased transmissibility of these new variants led to increases in infections, hospitalizations, and mortality. However, there is a scarcity of retrospective investigations examining the severity of all the main VOCs in presence of key public health measures and within various social determinants of health (SDOHs).
Objective: This study aims to provide a retrospective assessment of the clinical severity of COVID-19 VOCs in the context of heterogenous SDOHs and vaccination rollout.
Methods: We used a population-based retrospective cohort design with data from the British Columbia COVID-19 Cohort, a linked provincial surveillance platform. To assess the relative severity (hospitalizations, intensive care unit [ICU] admissions, and deaths) of Gamma, Delta, and Omicron infections during 2021 relative to Alpha, we used inverse probability treatment weighted Cox proportional hazard modeling. We also conducted a subanalysis among unvaccinated individuals, as assessed severity differed across VOCs and SDOHs.
Results: We included 91,964 individuals infected with a SARS-CoV-2 VOC (Alpha: n=20,487, 22.28%; Gamma: n=15,223, 16.55%; Delta: n=49,161, 53.46%; and Omicron: n=7093, 7.71%). Delta was associated with the most severe disease in terms of hospitalization, ICU admissions, and deaths (hospitalization: adjusted hazard ratio [aHR] 2.00, 95% CI 1.92-2.08; ICU: aHR 2.05, 95% CI 1.91-2.20; death: aHR 3.70, 95% CI 3.23-4.25 relative to Alpha), followed generally by Gamma and then Omicron and Alpha. The relative severity by VOC remained similar in the unvaccinated individual subanalysis, although the proportion of individuals infected with Delta and Omicron who were hospitalized was 2 times higher in those unvaccinated than in those fully vaccinated. Regarding SDOHs, the proportion of hospitalized individuals was higher in areas with lower income across all VOCs, whereas among Alpha and Gamma infections, 2 VOCs that cocirculated, differential distributions of hospitalizations were found among racially minoritized groups.
Conclusions: Our study provides robust severity estimates for all VOCs during the COVID-19 pandemic in British Columbia, Canada. Relative to Alpha, we found Delta to be the most severe, followed by Gamma and Omicron. This study highlights the importance of targeted testing and sequencing to ensure timely detection and accurate estimation of severity in emerging variants. It further sheds light on the importance of vaccination coverage and SDOHs in the context of pandemic preparedness to support the prioritization of allocation for resource-constrained or minoritized groups. |
Link[221] Seroprevalence of SARS-CoV-2 antibodies among children receiving primary care in Toronto, Ontario
Author: Mary Aglipay, Jeffrey C. Kwong, Karen Colwill, Anne-Claude Gringas, Ashleigh Tuite, Muhammad Mamdani, Charles Keown-Stoneman, Catherine Birken, Jonathon Maguire, TARGet Kids! Collaboration Publication date: 21 August 2024 Publication info: Canadian Journal of Public Health, 21 August 2024 Cited by: David Price 4:07 PM 5 December 2024 GMT Citerank: (3) 679755Ashleigh TuiteAshleigh Tuite is an Assistant Professor in the Epidemiology Division at the Dalla Lana School of Public Health at the University of Toronto.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 715376Serosurveillance859FDEF6 URL: DOI: https://doi.org/10.17269/s41997-024-00916-3
| Excerpt / Summary [Canadian Journal of Public Health, 21 August 2024]
Objective: Characterizing the seroprevalence of SARS-CoV-2 antibodies in children is needed to optimize the COVID-19 public health response. We quantified the seroprevalence of SARS-CoV-2 infection-acquired antibodies and vaccine-acquired antibodies among children receiving primary care in Toronto, Canada.
Methods: We conducted a longitudinal cohort study between January 2021 and November 2022 in healthy children aged 0â16 years receiving primary care in Toronto. The primary and secondary outcomes were seroprevalence of SARS-COV-2 infection-acquired antibodies and vaccine-acquired antibodies ascertained from finger-prick dried blood spots. Samples were tested using an enzyme-linked immunosorbent assay for antibodies to full-length spike trimer and nucleocapsid. We explored sociodemographic differences with Firthâs penalized generalized estimating equations.
Results: Of the 475 participants, 50.1% were girls and mean age was 6.4 years (SDâ=â3.2). We identified 103 children seropositive for infection-acquired antibodies, with a crude seroprevalence that rose from 2.6% (95%CI 1.39â4.92) from January to July 2021 to 50.7% (95%CI 39.5â61.8) by July to November 2022. Seroprevalence of vaccine-acquired antibodies was 45.2% by July to November 2022 (95%CI 34.3â56.58). No differences in sociodemographic factors (age, sex, income, or ethnicity) were identified for infection-acquired antibodies; however, children with vaccine-acquired antibodies were more likely to be older, have mothers with university education, and have mothers who had also been vaccinated.
Conclusion: Our results provide a benchmark for seroprevalence of SARS-CoV-2 antibodies in children in Toronto. Ongoing monitoring of the serological status of children is important, particularly with the emergence of new variants of concern, low vaccine coverage, and discontinuation of PCR testing. |
Link[222] An early warning indicator trained on stochastic disease-spreading models with different noises
Author: Amit K. Chakraborty, Shan Gao, Reza Miry, Pouria Ramazi, Russell Greiner, Mark A. Lewis, Hao Wang Publication date: 9 August 2024 Publication info: J. R. Soc. Interface.212024019920240199, August 2024, Volume 21, Issue 217 Cited by: David Price 10:54 PM 8 December 2024 GMT Citerank: (5) 679842Mark LewisProfessor Mark Lewis, Kennedy Chair in Mathematical Biology at the University of Victoria and Emeritus Professor at the University of Alberta.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 70113403 Early Warning Systems of Infectious DiseasesAnalysis of early warning signals is a crucial component of a coordinated response to emerging infectious diseases (EIDs). The goal of OMNI-RĂUNIsâ projects is to provide both an analysis of these signals and of the possibility of disease establishment, and collectively these could be used to inform public health regarding the level of disease threat.859FDEF6, 701140Mining and Summarization of Early Warning Pandemic SignalsMining and Summarization of Early Warning Pandemic Signals for vector-borne diseases (Lyme and Chikungunya, etc.).859FDEF6, 701222OMNI â Publications144B5ACA0 URL: DOI: https://doi.org/10.1098/rsif.2024.0199
| Excerpt / Summary [Journal of the Royal Society Interface, 9 August 2024]
The timely detection of disease outbreaks through reliable early warning signals (EWSs) is indispensable for effective public health mitigation strategies. Nevertheless, the intricate dynamics of real-world disease spread, often influenced by diverse sources of noise and limited data in the early stages of outbreaks, pose a significant challenge in developing reliable EWSs, as the performance of existing indicators varies with extrinsic and intrinsic noises. Here, we address the challenge of modelling disease when the measurements are corrupted by additive white noise, multiplicative environmental noise and demographic noise into a standard epidemic mathematical model. To navigate the complexities introduced by these noise sources, we employ a deep learning algorithm that provides EWS in infectious disease outbreaks by training on noise-induced disease-spreading models. The indicatorâs effectiveness is demonstrated through its application to real-world COVID-19 cases in Edmonton and simulated time series derived from diverse disease spread models affected by noise. Notably, the indicator captures an impending transition in a time series of disease outbreaks and outperforms existing indicators. This study contributes to advancing early warning capabilities by addressing the intricate dynamics inherent in real-world disease spread, presenting a promising avenue for enhancing public health preparedness and response efforts. |
Link[223] Impact of waning immunity against SARS-CoV-2 severity exacerbated by vaccine hesitancy
Author: Chadi M. Saad-Roy, Sinead E. Morris, Mike Boots, Rachel E. Baker, Bryan L. Lewis, Jeremy Farrar, Madhav V. Marathe, Andrea L. Graham, Simon A. Levin, Caroline E. Wagner, C. Jessica E. Metcalf, Bryan T. Grenfell Publication date: 5 August 2024 Publication info: PLoS Comput Biol 20(8): e1012211 Cited by: David Price 11:17 PM 8 December 2024 GMT Citerank: (3) 679762Caroline E WagnerCaroline Wagner is an Assistant Professor in the Department of the Bioengineering at McGill University.10019D3ABAB, 704036Immunology859FDEF6, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1371/journal.pcbi.1012211
| Excerpt / Summary [PLoS Computational Biololgy, 5 August 2024]
The SARS-CoV-2 pandemic has generated a considerable number of infections and associated morbidity and mortality across the world. Recovery from these infections, combined with the onset of large-scale vaccination, have led to rapidly-changing population-level immunological landscapes. In turn, these complexities have highlighted a number of important unknowns related to the breadth and strength of immunity following recovery or vaccination. Using simple mathematical models, we investigate the medium-term impacts of waning immunity against severe disease on immuno-epidemiological dynamics. We find that uncertainties in the duration of severity-blocking immunity (imparted by either infection or vaccination) can lead to a large range of medium-term population-level outcomes (i.e. infection characteristics and immune landscapes). Furthermore, we show that epidemiological dynamics are sensitive to the strength and duration of underlying host immune responses; this implies that determining infection levels from hospitalizations requires accurate estimates of these immune parameters. More durable vaccines both reduce these uncertainties and alleviate the burden of SARS-CoV-2 in pessimistic outcomes. However, heterogeneity in vaccine uptake drastically changes immune landscapes toward larger fractions of individuals with waned severity-blocking immunity. In particular, if hesitancy is substantial, more robust vaccines have almost no effects on population-level immuno-epidemiology, even if vaccination rates are compensatorily high among vaccine-adopters. This pessimistic scenario for vaccination heterogeneity arises because those few individuals that are vaccine-adopters are so readily re-vaccinated that the duration of vaccinal immunity has no appreciable consequences on their immune status. Furthermore, we find that this effect is heightened if vaccine-hesitants have increased transmissibility (e.g. due to riskier behavior). Overall, our results illustrate the necessity to characterize both transmission-blocking and severity-blocking immune time scales. Our findings also underline the importance of developing robust next-generation vaccines with equitable mass vaccine deployment. |
Link[224] Canadaâs provincial COVID-19 pandemic modelling efforts: A review of mathematical models and their impacts on the responses
Author: Yiqing Xia, Jorge Luis Flores Anato, Caroline Colijn, Naveed Janjua, Mike Irvine, Tyler Williamson, Marie B. Varughese, Michael Li, Nathaniel Osgood, David J. D. Earn, Beate Sander, Lauren E. Cipriano, Kumar Murty, Fanyu Xiu, Arnaud Godin, David Buckeridge, Amy Hurford, Sharmistha Mishra, Mathieu Maheu-Giroux Publication date: 25 July 2024 Publication info: Canadian Journal of Public Health, Volume 115, pages 541â557, (2024) Cited by: David Price 0:22 AM 10 December 2024 GMT
Citerank: (15) 679712CANMOD â PeopleCANMOD is a national network, with members located across the country and associated with a broader Emerging Infectious Disease Modelling (EIDM) initiative. We are a community of modellers, statisticians, epidemiologists, public health decision-makers, and those implementing and delivering interventions.10019D3ABAB, 679752Amy HurfordAmy Hurford is an Associate Professor jointly appointed in the Department of Biology and the Department of Mathematics and Statistics at Memorial University of Newfoundland and Labrador. 10019D3ABAB, 679757Beate SanderCanada Research Chair in Economics of Infectious Diseases and Director, Health Modeling & Health Economics and Population Health Economics Research at THETA (Toronto Health Economics and Technology Assessment Collaborative).10019D3ABAB, 679761Caroline ColijnDr. Caroline Colijn works at the interface of mathematics, evolution, infection and public health, and leads the MAGPIE research group. She joined SFU's Mathematics Department in 2018 as a Canada 150 Research Chair in Mathematics for Infection, Evolution and Public Health. She has broad interests in applications of mathematics to questions in evolution and public health, and was a founding member of Imperial College London's Centre for the Mathematics of Precision Healthcare.10019D3ABAB, 679775David BuckeridgeDavid is a Professor in the School of Population and Global Health at McGill University, where he directs the Surveillance Lab, an interdisciplinary group that develops, implements, and evaluates novel computational methods for population health surveillance. He is also the Chief Digital Health Officer at the McGill University Health Center where he directs strategy on digital transformation and analytics and he is an Associate Member with the Montreal Institute for Learning Algorithms (Mila).10019D3ABAB, 679776David EarnProfessor of Mathematics and Faculty of Science Research Chair in Mathematical Epidemiology at McMaster University.10019D3ABAB, 679844Mathieu Maheu-GirouxCanada Research Chair (Tier 2) in Population Health Modeling and Associate Professor, McGill University.10019D3ABAB, 679855Nathaniel OsgoodNathaniel D. Osgood is a Professor in the Department of Computer Science and Associate Faculty in the Department of Community Health & Epidemiology at the University of Saskatchewan.10019D3ABAB, 679856Naveed Zafar JanjuaDr. Naveed Zafar Janjua is an epidemiologist and senior scientist at the BC Centre for Disease Control and Clinical Associate Professor at School of Population and Public Health, University of British Columbia. Dr. Janjua is a Medical Doctor (MBBS) with a Masters of Science (MSc) degree in Epidemiology & Biostatistics and Doctorate in Public Health (DrPH). 10019D3ABAB, 679880Sharmistha MishraSharmistha Mishra is an infectious disease physician and mathematical modeler and holds a Tier 2 Canadian Research Chair in Mathematical Modeling and Program Science.10019D3ABAB, 679893Kumar MurtyProfessor Kumar Murty is in the Department of Mathematics at the University of Toronto. His research fields are Analytic Number Theory, Algebraic Number Theory, Arithmetic Algebraic Geometry and Information Security. He is the founder of the GANITA lab, co-founder of Prata Technologies and PerfectCloud. His interest in mathematics ranges from the pure study of the subject to its applications in data and information security.10019D3ABAB, 685387Michael Y LiProfessor of Mathematics in the Department of Mathematical and Statistical Sciences at the University of Alberta, and Director of the Information Research Lab (IRL).10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 701037MfPH â Publications144B5ACA0, 728553Pandemic modeling questions?140D5CB99 URL: DOI: https://doi.org/10.17269/s41997-024-00910-9
| Excerpt / Summary [Canadian Journal of Public Health, 25 July 2024]
Setting: Mathematical modelling played an important role in the public health response to COVID-19 in Canada. Variability in epidemic trajectories, modelling approaches, and data infrastructure across provinces provides a unique opportunity to understand the factors that shaped modelling strategies.
Intervention: Provinces implemented stringent pandemic interventions to mitigate SARS-CoV-2 transmission, considering evidence from epidemic models. This study aimed to summarize provincial COVID-19 modelling efforts. We identified modelling teams working with provincial decision-makers, through referrals and membership in Canadian modelling networks. Information on models, data sources, and knowledge translation were abstracted using standardized instruments.
Outcomes: We obtained information from six provinces. For provinces with sustained community transmission, initial modelling efforts focused on projecting epidemic trajectories and healthcare demands, and evaluating impacts of proposed interventions. In provinces with low community transmission, models emphasized quantifying importation risks. Most of the models were compartmental and deterministic, with projection horizons of a few weeks. Models were updated regularly or replaced by new ones, adapting to changing local epidemic dynamics, pathogen characteristics, vaccines, and requests from public health. Surveillance datasets for cases, hospitalizations and deaths, and serological studies were the main data sources for model calibration. Access to data for modelling and the structure for knowledge translation differed markedly between provinces.
Implication: Provincial modelling efforts during the COVID-19 pandemic were tailored to local contexts and modulated by available resources. Strengthening Canadian modelling capacity, developing and sustaining collaborations between modellers and governments, and ensuring earlier access to linked and timely surveillance data could help improve pandemic preparedness. |
Link[225] COVID-19 data and modeling: We need to learn from and act on our experiences
Author: Michael Wolfson Publication date: 26 July 2024 Publication info: Canadian Journal of Public Health, 26 July 2024, Volume 115 , pages 535â540. Cited by: David Price 11:27 AM 12 December 2024 GMT Citerank: (3) 679851Michael WolfsonAdjunct Professor in the School of Epidemiology and Public Health at the University of Ottawa.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 728553Pandemic modeling questions?140D5CB99 URL: DOI: https://doi.org/10.17269/s41997-024-00917-2
| Excerpt / Summary [Canadian Journal of Public Health, Editorial, 26 July 2024]
This issue of the Journal includes an important paper by Xia and colleagues (Xia et al., 2024 ) describing recent pandemic experiments modeling the disease. Throughout the pandemic, health officials and government leaders showed graphs of projected epidemic curves, and alternative curves depending on whether a particular intervention like physical distancing or school closures was implemented.
Underlying these projections are computer simulation models. Xia et al. ( 2024 ) surveyed and characterized the variety of these models for six provinces where information was available. There was some informal information sharing among these modelers and regular cross-country virtual conversations agreed by the Public Health Agency of Canada (PHAC). Still, important lessons merit being more widely shared, for which this CJPH article provides an important starting point.
As in all models, the veracity of the projections depends critically on the quality of the data on which they are based, and the methodologies applied. Canada is well endowed with infectious disease modeling expertise, primarily based in universities. However, the data needed to support these modeling efforts were too often limited. In many cases, the data does not exist; for example, at the start of the pandemic, accurate counts of new infections, hospitalizations, and deaths due to COVID, as well as the availability of ventilators and personal protective equipment (PPE) were unavailable.
In other cases, the data existed digitally but were not shared, even within provinces and the same or closely affiliated agencies. For example, data in one agency on infected individuals could have been linked to data in another agency that was capturing the virus' genotype at the individual level, but were not sharedâthus depriving modelers of critical information on the virulence of evolving mutations. Another example was the failure to link detailed individual-level data on infections, vaccinations, and hospital admissions within the same province. Doing so would have enabled inputs and analysis greatly improving mathematical modeling.
Notwithstanding repeated claims that health care is a provincial responsibility, the federal government constitutionally has important powers as well and plays an overarching and coordinating role as health is a shared jurisdiction. Specifically for health crises like the pandemic, the Constitution grants the federal government powers regarding quarantine.
The federal government played a major role in procuring vaccines and test kits, and providing large cash transfers to individuals and businesses to help them bear the economic hardships of lockdowns and other measures intended to stem pandemic spread. Statistics Canada's legislated authorities and flexible data collection capabilities were available, though underutilized.
The federally funded COVID-19 Immunity Task Force (COVID-19 Immunity Task Force, nd ) played a key role, including producing seroprevalence estimates, albeit mostly pulling data from various existing sources including blood donations, cohort studies, and lab tests, sources that were not designed to provide unbiased surveillance. The Canadian COVID-19 Antibody and Health Survey (CCAHS) (Statistics Canada, 2023 ) made extensive efforts to provide unbiased sampling, an area that merits major improvements.
PHAC further has important responsibilities regarding infectious disease outbreaks, including modeling. This and other information are required to brief the federal Minister of Health and Cabinet on how the pandemic was likely to evolve. However, even though the provinces signed agreements more than a decade ago to share key data, such as individual-level data on cases of infection, these data often did not flow, or were seriously incomplete⊠|
Link[226] The majority of Canadians likely behaved as myopic rationalists rather than success-based learners when deciding on their first dose of COVID-19 vaccine
Author: Azadeh Aghaeeyan, Pouria Ramazi, Mark A. Lewis Publication date: 24 July 2024 Publication info: Frontiers in Public Health, 24 July 2024 Cited by: David Price 10:07 PM 13 December 2024 GMT Citerank: (3) 679842Mark LewisProfessor Mark Lewis, Kennedy Chair in Mathematical Biology at the University of Victoria and Emeritus Professor at the University of Alberta.10019D3ABAB, 704041Vaccination859FDEF6, 708794Health economics859FDEF6 URL: DOI: https://doi.org/10.3389/fpubh.2024.1406911
| Excerpt / Summary [Frontiers in Public Health, 24 July 2024]
Introduction: Successful vaccine promotion communication strategies require knowing how eligible recipients will respond to the opportunity to get vaccinated. Two main classes of recipients are myopic rationalists, those who receive a dose of vaccine only if it maximizes their own instant benefit and if so, do it as soon as possible, and success-based learners, those who learn from others that they perceive to be most successful.
Methods: A recent study models these two decision-making types, and estimates the population proportion of myopic rationalists in each U.S. state. In this report, we fit a similar model to data on COVID-19 vaccine uptake across the Canadian provinces and territories.
Results: We estimated that 64% of Canadians behaved as myopic rationalists in taking the first dose of a COVID-19 vaccine, compared to an estimated 47% in the United States. Among the provinces, the lowest proportion of myopic rationalists was 0.51 in Saskatchewan, while the highest was 0.74 in Prince Edward Island. The correlation analysis suggested a positive correlation between the proportion of myopic rationalists and the average age across the Canadian provinces (Pearson-r = 0.71).
Discussion: Canadian health management may benefit from these results in tailoring the vaccine promotion communication strategies. |
Link[227] Quantitative observational evidence of indirect herd benefits from COVID-19 vaccination or prior infection on SARS-CoV-2 infections and COVID-19 deaths: a population-based retrospective cohort study in Ontario, Canada
Author: Linwei Wang, Sarah Swayze, Arjumand Siddiqi, Stefan D Baral, Beate Sander, Hind Sbihi, Jeffrey C Kwong, Sharmistha Mishra Publication date: 31 January 2025 Publication info: medRxiv 2025.01.30.25321209 Cited by: David Price 11:07 AM 2 February 2025 GMT Citerank: (5) 679757Beate SanderCanada Research Chair in Economics of Infectious Diseases and Director, Health Modeling & Health Economics and Population Health Economics Research at THETA (Toronto Health Economics and Technology Assessment Collaborative).10019D3ABAB, 679880Sharmistha MishraSharmistha Mishra is an infectious disease physician and mathematical modeler and holds a Tier 2 Canadian Research Chair in Mathematical Modeling and Program Science.10019D3ABAB, 701020CANMOD â PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6, 715390Mortality859FDEF6 URL: DOI: https://doi.org/10.1101/2025.01.30.25321209
| Excerpt / Summary [medRxiv, 31 January 2025]
Background: Empirical evidence on the indirect herd benefits of COVID-19 vaccination and/or prior infection is limited. We aimed to examine how area-level immunity interacts with individual-level immunity to affect COVID-19 diagnoses and deaths.
Methods: Ontario residents aged 18 years or older were followed from August-01-2021 to January-30-2022. Individual-level immunity was defined as received a primary series of COVID-19 vaccines or a positive SARS-CoV-2 test in the past 165 days. Area-level immunity was determined based on the proportion of immune individuals in an individuals residing area. We used logistic regression and cause-specific hazard models to examine the relationship between immunity and COVID-19 diagnosis, and between immunity and COVID-19 death, respectively. We included an interaction term between individual-level and area-level immunity in each model.
Results: Of 11,122,816 adults, 7,518,015 (67.6%) were immune at baseline. After accounting for individual-level demographics, baseline health, and area-level social determinants of health, area-level immunity (highest vs. lowest quintiles) was associated with lower odds of COVID-19 diagnosis; the association was larger among non-immune (odds ratios [95% confidence interval]: 0.72 [0.70, 0.75]) than immune individuals (0.93 [0.90, 0.96]). Higher area-level immunity (highest vs. lowest quintiles) was also associated with lower hazard of COVID-19 death among non-immune individuals (hazard ratio: 0.77 [0.60, 1.00]).
Conclusions: Our study provides observational evidence supporting the herd benefits of vaccination or prior infection on SARS-CoV-2 infections and COVID-19 deaths. Findings reinforce the need for high vaccination coverage to protect vaccinated and unvaccinated populations, while providing insights for interpreting vaccine effectiveness estimates in the context of herd immunity. |
|
|