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Journal Article Ppublish 3 citations

PubMed: 39488168 DOI: 10.1016/j.watres.2024.122671

Epidemiological model can forecast COVID-19 outbreaks from wastewater-based surveillance in rural communities.

Water Res 268(Pt A):122671

Authors: Tyler Meadows, Erik R Coats, Solana Narum, Eva M Top, Benjamin J Ridenhour, Thibault Stalder

Affiliations

  • Department of Mathematics and Statistics, Queen's University, Kingston, Ontario, Canada; Institute for Modeling Collaboration and Innovation (IMCI), University of Idaho, Moscow, ID, USA.
  • Department of Civil and Environmental Engineering, University of Idaho, Moscow, ID, USA; Institute for Modeling Collaboration and Innovation (IMCI), University of Idaho, Moscow, ID, USA; Bioinformatics and Computational Biology Graduate Program (BCB), Moscow, ID, USA.
  • Department of Civil and Environmental Engineering, University of Idaho, Moscow, ID, USA; Bioinformatics and Computational Biology Graduate Program (BCB), Moscow, ID, USA.
  • Institute for Modeling Collaboration and Innovation (IMCI), University of Idaho, Moscow, ID, USA; Bioinformatics and Computational Biology Graduate Program (BCB), Moscow, ID, USA; Department of Biological Sciences, University of Idaho, Moscow, ID, USA; Institute for Interdisciplinary Data Sciences (IIDS), University of Idaho, Moscow, ID, USA.
  • Institute for Modeling Collaboration and Innovation (IMCI), University of Idaho, Moscow, ID, USA; Bioinformatics and Computational Biology Graduate Program (BCB), Moscow, ID, USA; Institute for Interdisciplinary Data Sciences (IIDS), University of Idaho, Moscow, ID, USA; Department of Mathematics and Statistical Science, University of Idaho, Moscow, ID, USA.
  • Institute for Modeling Collaboration and Innovation (IMCI), University of Idaho, Moscow, ID, USA; Department of Biological Sciences, University of Idaho, Moscow, ID, USA; Institute for Interdisciplinary Data Sciences (IIDS), University of Idaho, Moscow, ID, USA; INSERM, CHU Limoges, RESINFIT, U1092, Univ. Limoges, F-87000, Limoges, France.

Abstract

Wastewater has emerged as a crucial tool for infectious disease surveillance, offering a valuable means to bridge the equity gap between underserved communities and larger urban municipalities. However, using wastewater surveillance in a predictive manner remains a challenge. In this study, we tested if detecting SARS-CoV-2 in wastewater can forecast outbreaks in rural communities. Under the CDC National Wastewater Surveillance program, we monitored the SARS-CoV-2 in the wastewater of five rural communities and a small city in Idaho (USA). We then used a particle filter method coupled with a stochastic susceptible-exposed-infectious-recovered (SEIR) model to infer active case numbers using quantities of SARS-CoV-2 in wastewater. Our findings revealed that while high daily variations in wastewater viral load made real-time interpretation difficult, the SEIR model successfully factored out this noise, enabling accurate forecasts of the Omicron outbreak in five of the six towns shortly after initial increases in SARS-CoV-2 concentrations were detected in wastewater. The model predicted outbreaks with a lead time of 0 to 11 days (average of 6 days +/- 4) before the surge in reported clinical cases. This study not only underscores the viability of wastewater-based epidemiology (WBE) in rural communities-a demographic often overlooked in WBE research-but also demonstrates the potential of advanced epidemiological modeling to enhance the predictive power of wastewater data. Our work paves the way for more reliable and timely public health guidance, addressing a critical gap in the surveillance of infectious diseases in rural populations.

MeSH

Disease Outbreaks, Covid-19, Communicable Diseases

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