Predicting COVID-19 cases across a large university campus using built environment surveillance

Abstract

Background: Environmental surveillance of SARS-CoV-2 via wastewater has become an invaluable tool for population-level surveillance. Built environment sampling may provide complementary spatially refined detection for viral surveillance in congregate settings such as universities. Methods: We conducted a prospective environmental surveillance study at the University of Ottawa between September 2021 and April 2022. Floor surface samples were collected twice weekly from six university buildings and analyzed for the presence of SARS-CoV-2 using quantitative PCR. A Poisson regression was used to model the campus-wide COVID-19 cases predicted from the fraction of floor swabs positive for SARS-CoV-2 RNA, building CO2 levels, Wi-Fi usage, and SARS-CoV-2 RNA levels in city wastewater. Building-level cases were modelled using viral copies detected in floor samples as a predictor. Results: Over the 32-week study period, we collected 554 floor swabs at six university buildings. Overall, 13% of swabs were PCR positive for SARS-CoV-2, with positivity ranging between 4.8% and 32.7% among buildings. Both floor swab positivity (Spearman r = 0.74, 95% CI 0.53 to 0.87) and city wastewater signal (Spearman r = 0.50, 95% CI 0.18 to 0.73) positively correlated with on-campus COVID-19 cases. In addition, built environment detection was a predictor of cases linked to individual university buildings. Conclusions: Detection of SARS-CoV-2 RNA on floors and viral RNA levels in city-wide wastewater were strongly associated with the incidence of COVID-19 cases on a university campus. These data suggest a potential role for institutional built environment sampling, used together with wastewater surveillance, for predicting COVID-19 cases at both campus-wide and building-level scales.