A multicentre study to predict COVID-19 outbreaks in long-term care homes using wastewater surveillance and environmental surface sampling for SARS-CoV-2

BackgroundFloor swabs can be an effective environmental sampling method for proactive SARS-CoV-2 surveillance in congregate settings like long-term care homes (LTCHs). Concurrent assessment of additional variables such as wastewater surveillance data and weather data have the potential to improve the predictive performance of this approach. MethodsWe analyzed existing data from 5,095 floor swabs collected between August 2021 and January 2023 from 10 LTCHs across three cities in Ontario, Canada: Ottawa, Toronto, and Sault Ste. Marie. Floors were swabbed weekly at each LTCH. Swabs were analyzed using RT-qPCR. Wastewater data was obtained from the Ontario Wastewater Surveillance Consortiums repository; we included one treatment plant for each city. Weather data was sourced from Environment Canada, with one station selected from each city. Logistic regression, LASSO-penalized logistic regression, Random Forest, and XGBoost were used for COVID-19 outbreak predictions using different subsets of predictors with leave-one-LTCH-out cross-validation. SHAP values were computed for model explainability. Our outcome of interest was a COVID-19 outbreak within an LTCH. ResultsOver the study period, 25 COVID-19 outbreaks occurred in the participating LTCHs, with a median duration of 30 days and a median of 39 cases per outbreak (range 2 to 196). LASSO generally out-performed logistic regression, Random Forest, and XGBoost. The two variables with the highest SHAP values were log transformed 7-day mean wastewater and log viral copies from floor swabs. ConclusionsIncorporating wastewater data and weather data enhanced the ability of floor swab results to predict an outbreak of COVID-19 in an LTCH. Future studies are needed to evaluate how well the model performs when implemented into practice.