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:
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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.