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Nationwide Trends in COVID-19 Cases and SARS-CoV-2 RNA Wastewater Concentrations in the United States.

Claire DuvalletFuqing WuKyle A McElroyMaxim ImakaevNoriko EndoAmy XiaoJianbo ZhangRóisín Floyd-O'SullivanMorgan M PowellSamuel MendolaShane T WilsonFrancis CruzTamar MelmanChaithra Lakshmi SathyanarayanaScott W OlesenTimothy B EricksonNewsha GhaeliPeter ChaiEric J AlmMariana Matus
Published in: ACS ES&T water (2022)
Wastewater-based epidemiology has emerged as a promising technology for population-level surveillance of COVID-19. In this study, we present results of a large nationwide SARS-CoV-2 wastewater monitoring system in the United States. We profile 55 locations with at least six months of sampling from April 2020 to May 2021. These locations represent more than 12 million individuals across 19 states. Samples were collected approximately weekly by wastewater treatment utilities as part of a regular wastewater surveillance service and analyzed for SARS-CoV-2 RNA concentrations. SARS-CoV-2 RNA concentrations were normalized to pepper mild mottle virus, an indicator of fecal matter in wastewater. We show that wastewater data reflect temporal and geographic trends in clinical COVID-19 cases and investigate the impact of normalization on correlations with case data within and across locations. We also provide key lessons learned from our broad-scale implementation of wastewater-based epidemiology, which can be used to inform wastewater-based epidemiology approaches for future emerging diseases. This work demonstrates that wastewater surveillance is a feasible approach for nationwide population-level monitoring of COVID-19 disease. With an evolving epidemic and effective vaccines against SARS-CoV-2, wastewater-based epidemiology can serve as a passive surveillance approach for detecting changing dynamics or resurgences of the virus.
Keyphrases
  • sars cov
  • wastewater treatment
  • respiratory syndrome coronavirus
  • anaerobic digestion
  • antibiotic resistance genes
  • coronavirus disease
  • public health
  • risk factors
  • cross sectional
  • microbial community
  • machine learning