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Building knowledge of university campus population dynamics to enhance near-to-source sewage surveillance for SARS-CoV-2 detection.

ABSTRACT: Wastewater surveillance has been widely implemented for monitoring of SARS-CoV-2 during the global COVID-19 pandemic, and near-to-source monitoring is of particular interest for outbreak management in discrete populations. However, variation in population size poses a challenge to the triggering of public health interventions using wastewater SARS-CoV-2 concentrations. This is especially important for near-to-source sites that are subject to significant daily variability in upstream populations. Focusing on a university campus in England, this study investigates methods to account for variation in upstream populations at a site with highly transient footfall and provides a better understanding of the impact of variable populations on the SARS-CoV-2 trends provided by wastewater-based epidemiology. The potential for complementary data to help direct response activities within the near-to-source population is also explored, and potential concerns arising due to the presence of heavily diluted samples during wet weather are addressed. Using wastewater biomarkers, it is demonstrated that population normalisation can reveal significant differences between days where SARS-CoV-2 concentrations are very similar. Confidence in the trends identified is strongest when samples are collected during dry weather periods; however, wet weather samples can still provide valuable information. It is also shown that building-level occupancy estimates based on complementary data aid identification of potential sources of SARS-CoV-2 and can enable targeted actions to be taken to identify and manage potential sources of pathogen transmission in localised communities.

SUBMITTER: Sweetapple C

PROVIDER: S-EPMC8450208 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Targeted wastewater surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been proposed by the United States Centers for Disease Control and Prevention's National Wastewater Surveillance System as a complementary approach to clinical surveillance to detect the presence of Coronavirus Disease 2019 (COVID-19) at high-density facilities and institutions such as university campuses, nursing homes, and correctional facilities. In this study we evaluated the efficacy of targeted wastewater surveillance of SARS-CoV-2 RNA together with individual-level testing for outbreak mitigation on a university campus during Fall 2020 semester. Wastewater samples (n = 117) were collected weekly from manholes or sewer cleanouts that receive wastewater inputs from dormitories, community-use buildings, and a COVID-19 isolation dormitory. Quantitative RT-PCR N1 and N2 assays were used to measure SARS-CoV-2 nucleocapsid genes in wastewater. Due to varying human waste input in different buildings, pepper mild mottle virus (PMMV) RNA was also measured in all samples and used to normalize SARS-CoV-2 N1 and N2 RNA wastewater concentrations. In this study, temporal trends of SARS-CoV-2 in wastewater samples mirrored trends in COVID-19 cases detected on campus. Normalizing SARS-CoV-2 RNA concentrations using human fecal indicator, PMMV enhanced the correlation between N1 and N2 gene abundances in wastewater with COVID-19 cases. N1 and N2 genes were significant predictors of COVID-19 cases in dormitories, and the N2 gene was significantly correlated with the number of detected COVID-19 cases in dormitories. By implementing several public health surveillance programs include targeted wastewater surveillance, individual-level testing, contact tracing, and quarantine/isolation facilities, university health administrators could act decisively during an outbreak on campus, resulting in rapid decline of newly detected COVID-19 cases. Wastewater surveillance of SARS-CoV-2 is a proactive outbreak monitoring tool for university campuses seeking to continue higher education practices in person during the COVID-19 pandemic.

Dataset Information

Building knowledge of university campus population dynamics to enhance near-to-source sewage surveillance for SARS-CoV-2 detection.

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