Applications of Near Source Wastewater Based Epidemiology to Monitor SARS-CoV-2 Prevalence in Sub-Catchment Populations

The etiologic agent of the COVID-19 pandemic is SARS-CoV-2. SARS-CoV-2 is commonly shed, primarily via stool, by both symptomatic and asymptomatic carriers. SARS- CoV-2 stability and partitioning characteristics make SARS-CoV-2 is a viable candidate for wastewater based epidemiology. During the COVID-19 pandemic, educational institutions have been responsible for enacting mitigation strategies for in person learning. Public health decision makers use test-based viral detection strategies to assess the risk of viral exposure. Near source monitoring (NSM) wastewater based epidemiology (WBE) can be used as a complementary testing method to clinical testing. Two studies were conducted to explore the feasibility and reliability of using NSM WBE in college residence halls and public schools. Results suggest that both grab and composite NSM WBE samples can reliably identify qualitative trends in SARS-CoV-2 carrier prevalence. Grab samples are sufficient for qualitative detection in the presence of elevated background community case rates. If background community case rates are below 10%, composite sampling is warranted to improve the probability of viral detection. Also, sample collection time and building function may have an impact on the reliability of results, especially for grab samples. Sample type, sample frequency, and site feasibility influence the detection capability of NSM WBE monitoring programs. NSM WBE with bi-weekly time weighted composite samples produces similar results to clinical testing. Modeled case rates, from NSM WBE grab sample data in college residence halls, were in agreement with background case rates. Overall, NSM WBE is an effective and reliable strategy to assess risk of viral exposure in residence halls and public schools. Clinical testing and wastewater testing can be employed simultaneously to further improve the reliability of test based risk assessments in the presence of low background case rates. Advisor: Shannon Bartelt-Hun