Abstract
Using wastewater surveillance as a continuous pooled sampling technique has been in place in many countries since the early stages of the outbreak of COVID-19. Since the beginning of the outbreak, many research works have emerged, studying different aspects of viral SARS-CoV-2 DNA concentrations (viral load) in wastewater and its potential as an early warning method. However, one of the questions that has remained unanswered is the quantitative relation between viral load and clinical indicators such as daily cases, deaths, and hospitalizations. Few studies have tried to couple viral load data with an epidemiological model to relate the number of infections in the community to the viral burden. This paper proposes a stochastic wastewater-based SEIR model to showcase the importance of viral load in the early detection and prediction of an outbreak in a community. We built three models based on whether or not they use the case count and viral load data and compared their simulations and forecasting quality. Our results demonstrate that a simple SEIR model based on viral load data can reliably predict the number of infections in the future. Therefore, wastewater-based surveillance is a promising way of monitoring the spread of COVID-19 and can provide city officials with timely information about the circulation of COVID-19 in the community.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
The authors would like to thank the Ivy Foundation COVID-19 translational research fund for supporting this work.
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Footnotes
mf4yc{at}virginia.edu
sns2sf{at}virginia.edu
mdp2u{at}virginia.edu
bf4g{at}virginia.edu
hs9hd{at}virginia.edu
Paper in collection COVID-19 SARS-CoV-2 preprints from medRxiv and bioRxiv
The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, University of Washington, and Vrije Universiteit Amsterdam.