A Mixed-Effects Model to Predict COVID-19 Hospitalizations Using Wastewater Surveillance

Abstract

The potential utility of wastewater-based epidemiology as an early warning tool has been explored widely across the globe during the COVID-19 pandemic. Early in the pandemic, methods were developed to detect the presence of SARS-CoV-2 RNA in wastewater. Since then, extensive research has been conducted to study the relationship between viral concentration in wastewater and COVID-19 cases in catchment areas of sewage treatment plants over time. However, few reports, to date, have attempted to develop predictive models for hospitalizations using SARS-CoV-2 RNA concentrations in wastewater. This study uses wastewater data to forecast hospitalizations using a linear mixed-effects model that allows for repeated measures and fixed and random effects. We use wastewater data from various treatment plants in California to predict hospitalizations at the county level assuming data from March 14, 2022, to May 21, 2023. The results suggest that wastewater data can serve as a dependable substitute for clinical data in creating robust models to predict hospitalizations. This approach can enhance our understanding of community-level transmission and its impact on hospital capacity.