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Article Abstract

Building-level wastewater-based surveillance (WBS) has been increasingly applied upstream from wastewater treatment plants to conduct targeted monitoring for SARS-CoV-2. In this study, a two-tiered, trigger-based wastewater surveillance program was developed on a university campus to monitor dormitory wastewater. The objective was to determine if passive sampling with cotton gauze as a sampling medium could be used to support institution-level public health action. Two nucleocapsid gene targets (N1 and N2) of SARS-CoV-2 as well as the endogenous fecal indicator pepper mild mottle virus (PMMoV) were quantified using RT-qPCR. >500 samples were analyzed during two contrasting surveillance periods. In the Fall of 2021 community viral burden was low and a tiered sampling network was able to isolate individual clinical cases at the building-scale. In the Winter of 2022 wastewater signals were quickly elevated by the emergence of the highly transmissible SARS-CoV-2 Omicron (B.1.1.529) variant. Prevalence of SARS-CoV-2 shifted surveillance objectives from isolating cases to monitoring trends, revealing both the benefits and limitations of a tiered surveillance design under different public health situations. Normalization of SARS-CoV-2 by PMMoV was not reflective of upstream population differences, suggesting saturation of the material occurred during the exposure period. The passive sampling method detected nearly all known clinical cases and in one instance was able to identify one pre-symptomatic individual days prior to confirmation by clinical test. Comparisons between campus samplers and municipal wastewater influent suggests that the spread of COVID-19 on the campus was similar to that of the broader community. The results demonstrate that passive sampling is an effective tool that can produce semi-quantitative data capable of tracking temporal trends to guide targeted public health decision-making at an institutional level. Practitioners of WBS can utilize these results to inform surveillance program designs that prioritize efficient resource use and rapid reporting.

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http://dx.doi.org/10.1016/j.scitotenv.2023.168998DOI Listing

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