Coupling Wastewater-Based Epidemiological Surveillance and Modelling of SARS-COV-2/COVID-19: Practical Applications at the Public Health Agency of Canada
Joung, M. J.; Mangat, C. S.; Mejia, E.; Nagasawa, A.; Nichani, A.; Peres-Iracheta, C.; Peterson, S. W.; Champredon, D.
Show 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.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- COVID-19 wastewater surveillance in rural communities: Comparison of lagoon and pumping station samples 98%
- Catching a resurgence: Increase in SARS-CoV-2 viral RNA identified in wastewater 48 hours before COVID-19 clinical tests and 96 hours before hospitalizations 97%
- Wastewater to clinical case (WC) ratio of COVID-19 identifies insufficient clinical testing, onset of new variants of concern and population immunity in urban communities 97%
Similar papers in this journal
- Sensitivity of wastewater-based epidemiology for detection of SARS-CoV-2 RNA in a low prevalence setting 97%
- When Case Reporting Becomes Untenable: Can Sewer Networks Tell Us Where COVID-19 Transmission Occurs? 97%
- Epidemiological model can forecast COVID-19 outbreaks from wastewater-based surveillance in rural communities. 97%
Similar papers in this journal
- Quantitative Trend Analysis of SARS-CoV-2 RNA in Municipal Wastewater Exemplified with Sewershed-Specific COVID-19 Clinical Case Counts 97%
- Wastewater-based surveillance of respiratory syncytial virus epidemic at the national level in Finland 97%
- Nationwide trends in COVID-19 cases and SARS-CoV-2 wastewater concentrations in the United States 97%
Similar papers in this journal
- SARS-CoV-2 Surveillance in US Wastewater: Leading Indicators and Data Variability Analysis in the 2023-2024 Season 96%
- Monitoring occurrence of SARS-CoV-2 in school populations: a wastewater-based approach 95%
- A systematic review on the incidence of influenza viruses in wastewater matrices: Implications for Public Health 95%
Similar papers in this journal
- Real-time outlier detection in digital PCR data for wastewater-based pathogen surveillance 96%
- Unravelling the early warning capability of wastewater surveillance for COVID-19: A temporal study on SARS-CoV-2 RNA detection and need for the escalation 95%
- Predictive power of wastewater for nowcasting infectious disease transmission: a retrospective case study of five sewershed areas in Louisville, Kentucky 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.