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Coupling Wastewater-Based Epidemiological Surveillance and Modelling of SARS-COV-2/COVID-19: Practical Applications at the Public Health Agency of Canada (preprint)
medrxiv; 2022.
Preprint
in English
| medRxiv | ID: ppzbmed-10.1101.2022.06.26.22276912
ABSTRACT
Wastewater-based surveillance (WBS) of SARS-CoV-2 offers a complementary tool for clinical surveillance to detect and monitor Coronavirus Disease 2019 (COVID-19). Since both symptomatic and asymptomatic individuals infected with SARS-CoV-2 can shed the virus through the fecal route, WBS has the potential to measure community prevalence of COVID-19 without restrictions from healthcare-seeking behaviors and clinical testing capacity. During the Omicron wave, the limited capacity of clinical testing to identify COVID-19 cases in many jurisdictions highlighted the utility of WBS to estimate disease prevalence and inform public health strategies. However, there is a plethora of in-sewage, environmental and laboratory factors that can influence WBS outputs. The implementation of WBS therefore requires a comprehensive framework to outline an analysis pipeline that accounts for these complex and nuanced factors. This article reviews the framework of the national WBS conducted at the Public Health Agency of Canada to present WBS methods used in Canada to track and monitor SARS-CoV-2. In particular, we focus on five Canadian cities - Vancouver, Edmonton, Toronto, Montreal and Halifax - whose wastewater signals are analyzed by a mathematical model to provide case forecasts and reproduction number estimates. This work provides insights on approaches to implement WBS at the national scale in an accurate and efficient manner. Importantly, the national WBS system has implications beyond COVID-19, as a similar framework can be applied to monitor other infectious disease pathogens or antimicrobial resistance in the community.
Full text:
Available
Collection:
Preprints
Database:
medRxiv
Main subject:
Communicable Diseases
/
COVID-19
Language:
English
Year:
2022
Document Type:
Preprint
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