Granular insights: A wastewater-based machine learning approach for localized COVID-19 hospitalization forecasting

Wastewater-based epidemiology (WBE) is a valuable tool for monitoring emerging disease trends in a community. Specifically, early predictions of hospitalization in a community can help reduce the strain on healthcare services and facilitate better planning and preparation. This study examines the use of SARS-CoV-2 RNA concentrations in wastewater to predict COVID-19 hospitalizations in South Carolina. We analyzed SARS-CoV-2 RNA concentration collected from six wastewater treatment plants (WWTPs) across South Carolina from April 19, 2020 to February 2, 2021 to predict COVID-19-related hospitalizations across WWTPs and 43 associated ZIP codes. Poisson regression and random forest models were utilized to forecast 7-day, 14-day, and 21-day ahead COVID-19 hospitalizations. Model performance was validated against statewide hospitalization claims data. Model accuracy was strongest for 14-day ahead prediction, with the random forest models achieving a median percentage agreement (PA) of 91.16% (IQR = 86.49-91.84%) across WWTPs and 78.12% (IQR = 67.99-84.53%) across ZIP codes. These findings demonstrate that WBE offers a robust and timely approach for predicting hospitalizations at fine geographic scales. This modeling framework can be adapted to other infectious diseases to enhance surveillance and response efforts.