Implementing an adaptive, two-tiered SARS-CoV-2 wastewater surveillance program on a university campus using passive sampling

This dataset is part/subset of https://doi.org/10.1016/j.scitotenv.2023.168998

Wastewater-based surveillance (WBS) has been increasingly applied at sites upstream of the wastewater treatment plant (WWTP) to monitoring SARS-CoV-2 RNA at the building-scale. The dataset described is the result of an eight-month long passive sampling campaign on the University of Waterloo (Waterloo, ON) campus. The goal was to determine if passive sampling could support institution-level decision-making. In brief, the method involved the 24-hour deployment of cotton gauze passive samplers enclosed in a 3D printed housing vessel. Upon collection, passive sampling materials were washed and the resulting solids were concentrated. RNA extraction from solids was performed using a kit-based extraction method. Two regions of the SARS-CoV-2 nucleocapsid gene (e.g., N1, N2) as well as the endogenous indicator pepper mild mottle virus (PMMoV) were quantified using RT-qPCR. Our study demonstrated that cotton gauze was able to identify trends in SARS-CoV-2 burdens on campus and that viral concentrations on passive samplers were significantly correlated with known clinical cases in the catchment. The dataset also includes monitoring data collected at the nearby municipal WWTP which offers insight to transmission dynamics at both sampling scales. Further developing affordable WBS methodologies will benefit future monitoring efforts under novel public health scenarios.

Haskell, B., Dhiyebi, H., Servos, M. (2023). Implementing an adaptive, two-tiered SARS-CoV-2 wastewater surveillance program on a university campus using passive sampling. Federated Research Data Repository. https://doi.org/10.20383/103.0856

University of Waterloo, Waterloo, ON, Canada

These data are available under a CC BY 4.0 license < https://creativecommons.org/licenses/by/4.0

https://doi.org/10.20383/103.0856