[Frontiers in Public Health, 17 November 2022]

In Canada, the first confirmed case of “Coronavirus Disease 2019” (COVID-19), the disease caused by the virus known as “Severe Acute Respiratory Syndrome-related Coronavirus type 2” (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.

[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.

[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.

[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.

[PLOS ONE, 31 March 2022]

Background: The “Coronavirus Disease 2019” (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.

[Journal of Medical Virology, 13 September 2022]

Monkeypox, a milder disease compared to smallpox, is caused by a virus initially discovered and described in 1958 by the prominent Danish virologist von Magnus, who was investigating an infectious outbreak affecting monkey colonies. Currently, officially starting from May 2022, an outbreak of monkeypox is ongoing, with 51 000 cases being notified as of September 1, 2022—51 408 confirmed, 28 suspected, and 12 fatalities, for a grand total of 51 448 cases. More than 100 countries and territories are affected, from all the six World Health Organization regions. There are some striking features, that make this outbreak rather unusual when compared with previous outbreaks, including a shift on average age and the most affected age group, affected sex/gender, risk factors, clinical course, presentation, and the transmission route. Initially predominantly zoonotic, with an animal-to-human transmission, throughout the last decades, human-to-human transmission has become more and more sustained and effective. In particular, clusters of monkeypox have been described among men having sex with men, some of which have been epidemiologically linked to international travel to nonendemic countries and participation in mass gathering events/festivals, like the “Maspalomas (Gran Canaria) 2022 pride.” This review will specifically focus on the “emerging” transmission route of the monkeypox virus, that is to say, the sexual transmission route, which, although not confirmed yet, seems highly likely in the diffusion of the infectious agent.

[Journal of Medical Virology, 12 June 2022]

An emerging outbreak of monkeypox infection is quickly spreading worldwide, being currently reported in more than 30 countries, with slightly less than 1000 cases. In the present preliminary report, we collected and synthesized early data concerning epidemiological trends and clinical features of the ongoing outbreak and we compared them with those of previous outbreaks. Data were pooled from six clusters in Italy, Australia, the Czech Republic, Portugal, and the United Kingdom, totaling 124 cases (for 35 of which it was possible to retrieve detailed information). The ongoing epidemic differs from previous outbreaks in terms of age (54.29% of individuals in their thirties), sex/gender (most cases being males), risk factors, and transmission route, with sexual transmission being highly likely. Also, the clinical presentation is atypical and unusual, being characterized by anogenital lesions and rashes that relatively spare the face and extremities. The most prevalent sign/symptom reported was fever (in 54.29% of cases) followed by inguinal lymphadenopathy (45.71%) and exanthema (40.00%). Asthenia, fatigue, and headache were described in 22.86% and 25.71% of the subjects, respectively. Myalgia was present in 17.14% of the cases. Both genital and anal lesions (ulcers and vesicles) were reported in 31.43% of the cases. Finally, cervical lymphadenopathy was described in 11.43% of the sample, while the least commonly reported symptoms were diarrhea and axillary lymphadenopathy (5.71% of the case series for both symptoms). Some preliminary risk factors can be identified (being a young male, having sex with other men, engaging in risky behaviors and activities, including condomless sex, human immunodeficiency virus positivity (54.29% of the sample analyzed), and a story of previous sexually transmitted infections, including syphilis). On the other hand, being fully virally suppressed and undetectable may protect against a more severe infectious course. However, further research in the field is urgently needed.

[Frontiers in Microbiology, 23 September 2022]

Monkeypox is an emerging zoonotic disease caused by the monkeypox virus, which is an infectious agent belonging to the genus Orthopoxvirus. Currently, commencing from the end of April 2022, an outbreak of monkeypox is ongoing, with more than 43,000 cases reported as of 23 August 2022, involving 99 countries and territories across all the six World Health Organization (WHO) regions. On 23 July 2022, the Director-General of the WHO declared monkeypox a global public health emergency of international concern (PHEIC), since the outbreak represents an extraordinary, unusual, and unexpected event that poses a significant risk for international spread, requiring an immediate, coordinated international response. However, the real magnitude of the burden of disease could be masked by failures in ascertainment and under-detection. As such, underestimation affects the efficiency and reliability of surveillance and notification systems and compromises the possibility of making informed and evidence-based policy decisions in terms of the adoption and implementation of ad hoc adequate preventive measures. In this review, synthesizing 53 papers, we summarize the determinants of the underestimation of sexually transmitted diseases, in general, and, in particular, monkeypox, in terms of all their various components and dimensions (under-ascertainment, underreporting, under-detection, under-diagnosis, misdiagnosis/misclassification, and under-notification).

[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 0–11 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.

[Frontiers in Public Health, 10 February 2023]

In late December 2019, a novel, emerging coronavirus, termed as “Severe Acute Respiratory Syndrome-related Coronavirus Type 2” (SARS-CoV-2) was identified as the infectious agent responsible for the generally mild, but sometimes life-threatening and even fatal “Coronavirus Disease 2019” (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.

[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 “off-the-shelf” 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 “vaccine apartheid” 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.

[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 cluster’s severity, progression and whether it can be defined as a hot-spot.

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 – Publications144B5ACA0, 703957Economics859FDEF6, 704045Covid-19859FDEF6, 715666Social networks859FDEF6, 715767Woldegebriel Assefa WoldegerimaDr. Woldegerima, knows as "Assefa", is an Assistant Professor at the Department of Mathematics and Statistics at York University.10019D3ABAB

[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.

[Frontiers in Public Health, 2 December 2022]

The global economy has been hard hit by the COVID-19 pandemic. Many countries are experiencing a severe and destructive recession. A significant number of firms and businesses have gone bankrupt or been scaled down, and many individuals have lost their jobs. The main goal of this study is to support policy- and decision-makers with additional and real-time information about the labor market flow using Twitter data. We leverage the data to trace and nowcast the unemployment rate of South Africa during the COVID-19 pandemic. First, we create a dataset of unemployment-related tweets using certain keywords. Principal Component Regression (PCR) is then applied to nowcast the unemployment rate using the gathered tweets and their sentiment scores. Numerical results indicate that the volume of the tweets has a positive correlation, and the sentiments of the tweets have a negative correlation with the unemployment rate during and before the COVID-19 pandemic. Moreover, the now-casted unemployment rate using PCR has an outstanding evaluation result with a low Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Symmetric MAPE (SMAPE) of 0.921, 0.018, 0.018, respectively and a high R2-score of 0.929.

[Data in Brief, 6 June 2023]

After struggling with COVID-19 pandemic for two years, the world is finally recovering from this crisis. Nonetheless, another virus, Monkeypox, is quickly spreading throughout the world and in non-endemic regions and continents, threatening the world to a new pandemic. Twitter as a popular social media has successfully been used for predicting and controlling outbreaks. Much research previously has been done for building early warning systems, trend prediction, and misinformation and fake news detection. Since tweets are not accessible to all researchers, in this work, a publicly available dataset containing 2400202 tweets gathered from May first to December twenty-fifth, 2022 is presented. Twitter developers academic researcher API which returns all the tweets matching a given query was used to gather the dataset. To this end, the full archive search and keywords related to Monkeypox and its equivalents in other languages, i.e. Monkeypox or “monkey pox” or “viruela dei mono” or “variole du singe” or “variola do macoco” were used. The retweets were excluded using the negation operator, and the tweet ids and user ids were extracted and shared with public. Approximately, 1.79 percent (43047 number) of tweets were geotagged. To visualize the geotagged tweets, the longitude and latitude of the bounding box coordinates were averaged. This work will help researchers shed light on the news, patterns, and on-going discussions of Monkeypox on social media, identify hotspots, and help contain the Monkeypox virus.

[Journal of Medical Virology, 13 September 2022]

Monkeypox, a milder disease compared to smallpox, is caused by a virus initially discovered and described in 1958 by the prominent Danish virologist von Magnus, who was investigating an infectious outbreak affecting monkey colonies. Currently, officially starting from May 2022, an outbreak of monkeypox is ongoing, with 51 000 cases being notified as of September 1, 2022—51 408 confirmed, 28 suspected, and 12 fatalities, for a grand total of 51 448 cases. More than 100 countries and territories are affected, from all the six World Health Organization regions. There are some striking features, that make this outbreak rather unusual when compared with previous outbreaks, including a shift on average age and the most affected age group, affected sex/gender, risk factors, clinical course, presentation, and the transmission route. Initially predominantly zoonotic, with an animal-to-human transmission, throughout the last decades, human-to-human transmission has become more and more sustained and effective. In particular, clusters of monkeypox have been described among men having sex with men, some of which have been epidemiologically linked to international travel to nonendemic countries and participation in mass gathering events/festivals, like the “Maspalomas (Gran Canaria) 2022 pride.” This review will specifically focus on the “emerging” transmission route of the monkeypox virus, that is to say, the sexual transmission route, which, although not confirmed yet, seems highly likely in the diffusion of the infectious agent.

[Journal of Medical Virology, 11 February 2023]

Monkeypox, a zoonotic disease, is emerging as a potential sexually transmitted infection/disease, with underlying transmission mechanisms still unclear. We devised a risk-structured, compartmental model, incorporating sexual behavior dynamics. We compared different strategies targeting the high-risk population: a scenario of control policies geared toward the use of condoms and/or sexual abstinence (robust control strategy) with risk compensation behavior change, and a scenario of control strategies with behavior change in response to the doubling rate (adaptive control strategy). Monkeypox's basic reproduction number is 1.464, 0.0066, and 1.461 in the high-risk, low-risk, and total populations, respectively, with the high-risk group being the major driver of monkeypox spread. Policies imposing condom use or sexual abstinence need to achieve a 35% minimum compliance rate to stop further transmission, while a combination of both can curb the spread with 10% compliance to abstinence and 25% to condom use. With risk compensation, the only option is to impose sexual abstinence by at least 35%. Adaptive control is more effective than robust control where the daily sexual contact number is reduced proportionally and remains constant thereafter, shortening the time to epidemic peak, lowering its size, facilitating disease attenuation, and playing a key role in controlling the current outbreak.

[Frontiers in Physiology, 9 November 2022]

The still ongoing COVID-19 pandemic has dramatically impacted athletes, and, in particular, para-athletes and athletes with disabilities. However, there is no scholarly appraisal on this topic. Therefore, a critical scoping review of the literature was conducted. We were able to retrieve sixteen relevant studies. The sample size ranged from 4 to 183. Most studies were observational, cross-sectional, and questionnaire-based surveys, two studies were interventional, and two were longitudinal. One study was a technical feasibility study. Almost all studies were conducted as single-country studies, with the exception of one multi-country investigation. Five major topics/themes could be identified: namely, 1) impact of COVID-19-induced confinement on training and lifestyles in athletes with disabilities/para-athletes; 2) impact of COVID-19-induced confinement on mental health in athletes with disabilities/para-athletes; 3) impact of COVID-19-induced confinement on performance outcomes in athletes with disabilities/para-athletes; 4) risk of contracting COVID-19 among athletes with disabilities/para-athletes; and, finally, 5) impact of COVID-19 infection on athletes with disabilities/para-athletes. The scholarly literature assessed was highly heterogeneous, with contrasting findings, and various methodological limitations. Based on our considerations, we recommend that standardized, reliable tools should be utilized and new, specific questionnaires should be created, tested for reliability, and validated. High-quality, multi-center, cross-countries, longitudinal surveys should be conducted to overcome current shortcomings. Involving all relevant actors and stakeholders, including various national and international Paralympic Committees, as a few studies have done, is fundamental: community-led, participatory research can help identify gaps in the current knowledge about sports-related practices among the population of athletes with disabilities during an unprecedented period of measures undertaken that have significantly affected everyday life. Moreover, this could advance the field, by capturing the needs of para-athletes and athletes with disabilities and enabling the design of a truly “disability-inclusive response” to COVID-19 and similar future conditions/situations. Furthermore, follow-up studies on COVID-19-infected para-athletes and athletes with disabilities should be conducted. Evidence of long-term effects of COVID-19 is available only for able-bodied athletes, for whom cardiorespiratory residual alterations and mental health issues a long time after COVID-19 have been described.

[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.

[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 “smart” interventions, which are as efficacious as stringent ones.

Citerank: (10) 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, 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-RÉUNIS). 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, 679817Julien ArinoProfessor and Faculty of Science Research Chair in Fundamental Science with the Department of Mathematics at the University of Manitoba.10019D3ABAB, 701222OMNI – Publications144B5ACA0, 714608Charting a FutureCharting a Future for Emerging Infectious Disease Modelling in Canada – 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

Background: Globally, nonpharmaceutical interventions for COVID-19, including stay-at-home policies, limitations on gatherings and closure of public spaces, are being lifted. We explored the effect of lifting a stay-at-home policy on virus resurgence under different conditions.

Methods: Using confirmed case data from Toronto, Canada, between Feb. 24 and June 24, 2020, we ran a compartmental model with household structure to simulate the impact of the stay-at-home policy considering different levels of compliance. We estimated threshold values for the maximum number of contacts, probability of transmission and testing rates required for the safe reopening of the community.

Results: After the implementation of the stay-at-home policy, the contact rate outside the household fell by 39% (from 11.58 daily contacts to 7.11). The effective reproductive number decreased from 3.56 (95% confidence interval [CI] 3.02–4.14) on Mar. 12 to 0.84 (95% CI 0.79–0.89) on May 6. Strong adherence to stay-at-home policies appeared to prevent SARS-CoV-2 resurgence, but extending the duration of stay-at-home policies beyond 2 months had little added effect on cumulative cases (25 958 for 65 days of a stay-at-home policy and 23 461 for 95 days, by July 2, 2020) and deaths (1404 for 65 days and 1353 for 95 days). To avoid a resurgence, the average number of contacts per person per day should be kept below 9, with strict nonpharmaceutical interventions in place.

Interpretation: Our study demonstrates that the stay-at-home policy implemented in Toronto in March 2020 had a substantial impact on mitigating the spread of SARS-CoV-2. In the context of the early pandemic, before the emergence of variants of concern, reopening schools and workplaces was possible only with other nonpharmaceutical interventions in place.

Nonpharmaceutical interventions for COVID-19, including stay-at-home policies, isolation of cases and contact tracing, as well as physical distancing, handwashing and use of protective equipment such as face masks, are effective mitigation strategies for preventing virus spread.1–4 Many studies investigating SARS-CoV-2 transmission and nonpharmaceutical interventions point to the importance of within- and between-household transmission. 5–8 Although stay-at-home policies can help curb spread of SARS-CoV-2 in the community by reducing contacts outside the household,8 they can increase contacts among family members, leading to higher risk within the household, 9 with secondary infection rates in households shown to be as high as 30%–52.7%.5,10 Furthermore, prolonged periods of stay-at-home policies may not be practical because of the essential operations of society, and may directly or indirectly harm the economy and the physical and mental health of individuals.11,12 Therefore, it is important to assess the optimal length of policy implementation for preventing virus resurgence.

During the epidemic, stay-at-home policies have been used to mitigate virus spread. The proportion of people staying at home is a paramount factor for evaluating the effectiveness of this policy implementation. For example, symptomatic individuals, those who tested positive for SARS-CoV-2 infection, and traced contacts are more likely to remain in the home through self-isolation or quarantine than uninfected or asymptomatic individuals. 13 Hence, rates of testing, diagnosis, isolation of cases, contact tracing and quarantine of contacts, as well as public compliance with stay-at-home policies, are essential factors for determining virus transmission and the likelihood of epidemic resurgence after the lifting of restrictive closures.1 To allow for this level of complexity, we developed a household-based transmission model to capture differences in policy uptake behaviour using confirmed case data from Toronto, Canada.

Throughout the pandemic, Canadian provinces and territories have implemented restrictive closures of businesses, schools, workplaces and public spaces to reduce the number of contacts in the population and prevent further virus spread, with these restrictions lifted and reinstituted at various times.14 On Mar. 17, 2020, Ontario declared a state of emergency, with directives including stay-at-home policies.15

We aimed to evaluate the effect of the stay-at-home policy issued in March 2020 on the transmission of SARS-CoV-2 in Toronto, accounting for average household size, the degree of adherence to the stay-at-home policy, and the length of policy implementation. Additionally, on the basis of the average family size and local epidemic data, we estimated the basic reproduction number (R0) and effective reproduction number (Rt) and investigated potential thresholds for the number of contacts, testing rates and use of nonpharmaceutical interventions that would be optimal for mitigating the epidemic. Hence, we conducted simulations of dynamic population behaviour under different reopening and adherence scenarios, to compare different public health strategies in hopes of adding those evaluations to the scientific literature.

[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.

[Diversity and Distributions, 19 December 2022]

Aim: Many freshwater fishes are migrating poleward to more thermally suitable habitats in response to warming climates. In this study, we aimed to identify which freshwater fishes are most sensitive to climatic changes and asked: (i) how fast are lakes warming? (ii) how fast are fishes moving? and (iii) are freshwater fishes tracking climate?

Location: Ontario, Canada.

Methods: We assembled a database containing time series data on climate and species occurrence data from 10,732 lakes between 1986 and 2017. We calculated the rate of lake warming and climate velocity for these lakes. Climate velocities were compared with biotic velocities, specifically the rate at which the northernmost extent of each species shifted north.

Results: Lakes in Ontario warmed by 0.2°C decade−1 on average, at a climate velocity of 9.4 km decade−1 between 1986 and 2017. In response, some freshwater fishes have shifted their northern range boundaries with considerable interspecific variation ranging from species moving southwards at a rate of −58.9 km decade−1 to species ranges moving northwards at a rate of 83.6 km decade−1 over the same time period. More freshwater fish species are moving into northern lakes in Ontario than those being lost. Generally, predators are moving their range edges northwards, whereas prey fishes are being lost from northern lakes.

Main Conclusions: The concurrent loss of cooler refugia, combined with antagonistic competitive and predatory interactions with the range expanding species, has resulted in many commercially important predators moving their range edges northwards, whereas prey species have contracted their northern range edge boundaries. Trophic partitioning of range shifts highlights a previously undocumented observation of the loss of freshwater fishes from lower trophic levels in response to climate-driven migrations.

[Journal of Medical Internet Research, 1 May 2023]

Background: The global Mpox (formerly, Monkeypox) outbreak is disproportionately affecting the gay and bisexual men having sex with men community.

Objective: The aim of this study is to use social media to study country-level variations in topics and sentiments toward Mpox and Two-Spirit, Lesbian, Gay, Bisexual, Transgender, Queer or Questioning, Intersex, Asexual (2SLGBTQIAP+)–related topics. Previous infectious outbreaks have shown that stigma intensifies an outbreak. This work helps health officials control fear and stop discrimination.

Methods: In total, 125,424 Twitter and Facebook posts related to Mpox and the 2SLGBTQIAP+ community were extracted from May 1 to December 25, 2022, using Twitter application programming interface academic accounts and Facebook-scraper tools. The tweets’ main topics were discovered using Latent Dirichlet Allocation in the sklearn library. The pysentimiento package was used to find the sentiments of English and Spanish posts, and the CamemBERT package was used to recognize the sentiments of French posts. The tweets’ and Facebook posts’ languages were understood using the Twitter application programming interface platform and pycld3 library, respectively. Using ArcGis Online, the hot spots of the geotagged tweets were identified. Mann-Whitney U, ANOVA, and Dunn tests were used to compare the sentiment polarity of different topics and countries.

Results: The number of Mpox posts and the number of posts with Mpox and 2SLGBTQIAP+ keywords were 85% correlated (P<.001). Interestingly, the number of posts with Mpox and 2SLGBTQIAP+ keywords had a higher correlation with the number of Mpox cases (correlation=0.36, P<.001) than the number of posts on Mpox (correlation=0.24, P<.001). Of the 10 topics, 8 were aimed at stigmatizing the 2SLGBTQIAP+ community, 3 of which had a significantly lower sentiment score than other topics (ANOVA P<.001). The Mann-Whitney U test shows that negative sentiments have a lower intensity than neutral and positive sentiments (P<.001) and neutral sentiments have a lower intensity than positive sentiments (P<.001). In addition, English sentiments have a higher negative and lower neutral and positive intensities than Spanish and French sentiments (P<.001), and Spanish sentiments have a higher negative and lower positive intensities than French sentiments (P<.001). The hot spots of the tweets with Mpox and 2SLGBTQIAP+ keywords were recognized as the United States, the United Kingdom, Canada, Spain, Portugal, India, Ireland, and Italy. Canada was identified as having more tweets with negative polarity and a lower sentiment score (P<.04).

Conclusions: The 2SLGBTQIAP+ community is being widely stigmatized for spreading the Mpox virus on social media. This turns the community into a highly vulnerable population, widens the disparities, increases discrimination, and accelerates the spread of the virus. By identifying the hot spots and key topics of the related tweets, this work helps decision makers and health officials inform more targeted policies.

[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.

[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.

[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: 3.31–4.12). The estimates provided in this paper will help to inform COVID-19 modeling in the respective countries/regions.

[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.