Postdoctoral Scholars Association

Background: Wastewater-based disease surveillance emerged as a promising tool during the COVID-19 pandemic for early detection of SARS-CoV-2 outbreaks. While initial efforts focused on detecting viral RNA in wastewater, recent studies have explored its potential to predict COVID-19 trends. However, few studies have developed predictive models for county-level hospitalizations based on wastewater data. Methods: This study implemented a linear mixed-effects model to analyse the relationship between SARS-CoV-2 RNA concentrations in wastewater and county-level COVID-19 hospitalizations, using data from 21 counties in California (March 21, 2022, to May 21, 2023). Wastewater data were used as the primary input variable, replacing traditional metrics such as case counts or test positivity rates. Results: The model demonstrated strong performance in capturing hospitalization trends and enabled accurate two-week-ahead predictions of COVID-19 hospitalizations. By leveraging wastewater data, the framework provided a reliable early warning system for potential surges in hospitalizations. Conclusion: This study highlights the utility of wastewater-based surveillance as a predictive tool for COVID-19 hospitalizations at the county level. The proposed model offers actionable insights for hospitals to optimize resource allocation and prepare for patient influxes, underscoring the value of wastewater monitoring in public health decision-making.