COVID-19 case prediction via wastewater surveillance in a low-prevalence urban community: a modeling approach

Abstract

Estimating and predicting the epidemic size from wastewater surveillance results remains challenging for the practical implementation of wastewater-based epidemiology (WBE). In this study, by employing a highly sensitive detection method, we documented the time series of SARS-CoV-2 RNA occurrence in the wastewater influent from an urban community with a 360,000 population in Japan, from August 2020 to February 2021. The detection frequency of the viral RNA increased during the outbreak events of COVID-19 and the highest viral RNA concentration was recorded at the beginning of January 2021, amid the most serious outbreak event during the study period. We found that: (1) direct back-calculation still suffers from great uncertainty dominated by inconsistent detection and the varying gap between the observed wastewater viral load and the estimated patient viral load, and (2) the detection frequency correlated well with reported cases and the prediction of the latter can be carried out via data-driven modeling methods. Our results indicate that wastewater virus occurrence can contribute to epidemic surveillance in ways more than back-calculation, which may spawn future wastewater surveillance implementations. HIGHLIGHTS Time series of SARS-CoV-2 wastewater presence in a low-prevalence community.; SARS-CoV-2 detection frequency correlates with reported cases.; Reported cases can be predicted by detection frequency within a period.; Data-driven methods may facilitate wastewater-based epidemic modeling.;

Keywords

• covid-19 surveillance
• data-driven modeling
• epidemic prediction
• wastewater-based epidemiology