Emerging Infectious Diseases, Volume 29, Issue 8
- Anthology ID:
- G23-24
- Month:
- Year:
- 2023
- Address:
- Venue:
- GWF
- SIG:
- Publisher:
- Centers for Disease Control and Prevention (CDC)
- URL:
- https://gwf-uwaterloo.github.io/gwf-publications/G23-24
- DOI:
Omicron COVID-19 Case Estimates Based on Previous SARS-CoV-2 Wastewater Load, Regional Municipality of Peel, Ontario, Canada
Lydia Cheng
|
Hadi A. Dhiyebi
|
Monali Varia
|
Kyle Atanas
|
Nivetha Srikanthan
|
Samina Hayat
|
Heather Ikert
|
Meghan Fuzzen
|
Carly Sing-Judge
|
Yash Badlani
|
Eli Zeeb
|
Leslie M. Bragg
|
Robert Delatolla
|
John P. Giesy
|
Elaine Gilliland
|
Mark R. Servos
We determined correlations between SARS-CoV-2 load in untreated water and COVID-19 cases and patient hospitalizations before the Omicron variant (September 2020-November 2021) at 2 wastewater treatment plants in the Regional Municipality of Peel, Ontario, Canada. Using pre-Omicron correlations, we estimated incident COVID-19 cases during Omicron outbreaks (November 2021-June 2022). The strongest correlation between wastewater SARS-CoV-2 load and COVID-19 cases occurred 1 day after sampling (r = 0.911). The strongest correlation between wastewater load and COVID-19 patient hospitalizations occurred 4 days after sampling (r = 0.819). At the peak of the Omicron BA.2 outbreak in April 2022, reported COVID-19 cases were underestimated 19-fold because of changes in clinical testing. Wastewater data provided information for local decision-making and are a useful component of COVID-19 surveillance systems.