A mixed-effects model to predict COVID-19 hospitalizations using wastewater surveillance

Abstract

During the COVID-19 pandemic, many countries and regions investigated the potential use of wastewater-based disease surveillance as an early warning system. Initially, methods were created to detect the presence of SARS-CoV-2 RNA in wastewater. Investigators have since conducted extensive studies to examine the link between viral concentration in wastewater and COVID-19 cases in areas served by sewage treatment plants over time. However, only a few reports have attempted to create predictive models for hospitalizations at a county-level based on SARS-CoV-2 RNA concentrations in wastewater. This study implemented a linear mixed-effects model that evaluates the association between levels of virus in wastewater and county-level hospitalizations. The model was then utilized to predict short-term county-level hospitalization trends in 21 counties in California based on data from March 21, 2022, to May 21, 2023. The modeling framework proposed here permits repeated measurements, as well as fixed and random effects. The model that incorporated wastewater data as an input variable rather than cases or test positivity rate exhibited robust performance and effectively captured discernible trends in hospitalizations. Additionally, the model allows for the prediction of SARS CoV-2 hospitalizations two weeks ahead. Forecasts of COVID-19 hospitalizations could provide crucial information for hospitals to better allocate resources and prepare for potential surges in patient numbers.