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Wastewater-based surveillance (WBS) Interest1 #708744
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+Citations (2) - CitationsAdd new citationList by: CiterankMapLink[1] 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 D’Aoust, 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 11:06 PM 29 November 2023 GMT Citerank: (4) 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 – Publications144B5ACA0, 704022Surveillance859FDEF6, 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[2] 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 11:55 PM 29 November 2023 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, 701020CANMOD – PublicationsPublications by CANMOD Members144B5ACA0, 704022Surveillance859FDEF6 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. |
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