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

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.

Bayesian sequential approach to monitor COVID-19 variants through positivity rate from wastewate (preprint)

Trends in COVID-19 infection have changed throughout the pandemic due to myriad factors, including changes in transmission driven by social behavior, vaccine development and uptake, mutations in the virus genome, and public health policies. Mass testing was an essential control measure for curtailing the burden of COVID-19 and monitoring the magnitude of the pandemic during its multiple phases. However, as the pandemic progressed, new preventive and surveillance mechanisms emerged. Implementing vaccine programs, wastewater (WW) surveillance, and at-home COVID-19 tests reduced the demand for mass severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing. This paper proposes a sequential Bayesian approach to estimate the COVID-19 positivity rate (PR) using SARS-CoV-2 RNA concentrations measured in WW through an adaptive scheme incorporating changes in virus dynamics. PR estimates are used to compute thresholds for WW data using the CDC thresholds for low, substantial, and high transmission. The effective reproductive number estimates are calculated using PR estimates from the WW data. This approach provides insights into the dynamics of the virus evolution and an analytical framework that combines different data sources to continue monitoring the COVID-19 trends. These results can provide public health guidance to reduce the burden of future outbreaks as new variants continue to emerge. The proposed modeling framework was applied to the City of Davis and the campus of the University of California Davis.