Predicting COVID-19 cases across a large university campus using complementary built environment and wastewater surveillance approaches

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. Samples were analyzed for the presence of SARS-CoV-2 using RT-qPCR. 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 regional wastewater. We used a mixed-effects Poisson regression analysis to model building-level cases using viral copies detected in floor samples as a predictor. A random intercepts logistic regression model tested whether floor samples collected in high-traffic areas were more likely to have SARS-CoV-2 present than low-traffic areas. 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 university buildings. Both floor swab positivity (Spearman r = 0.74, 95% CI: 0.53-0.87) and regional wastewater signal (Spearman r = 0.50, 95% CI: 0.18-0.73) were positively correlated with on-campus COVID-19 cases. In addition, built environment detection was a predictor of cases linked to individual university buildings (IR log10(copies) + 1 = 17, 95% CI: 7-44). There was no significant difference in detection between floors sampled in high-traffic versus low-traffic areas (OR = 1.3, 95% CI: 0.8-2.1). Conclusions Detection of SARS-CoV-2 RNA on floors and viral RNA levels found in 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. ### Competing Interest Statement ED works for DNA Genotek that provided sampling swabs in-kind for this study in an unrestricted fashion. DNA Genotek had no control over the findings, interpretations, or conclusions published in this paper. MF is a consultant for ProofDx, a start-up company that has created a point of care testing device for adults with COVID-19 using CRISPR. All other authors have no relevant conflicts to disclose. ### Funding Statement This study was funded by grants to RK from the University of Ottawa's COVID-19 Recovery Task Force and the NSERC Alliance and Discovery Grants programs. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Office of Research Ethics and Integrity at the University of Ottawa waived ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors. Code and data will be made available from our GitHub repository upon publication.