(Poster) Bayesian sequential approach to monitor COVID-19 variants through test positivity rate from wastewater

Abstract

We present a statistical model to enhance the monitoring of COVID-19 outbreaks by correlating SARS-CoV-2 RNA concentrations in wastewater with the test positivity rate (TPR). To capture the non-autonomous nature of the prolonged pandemic, we introduce an adaptive scheme that can effectively model changes in viral transmission dynamics over time. The TPR is modeled through a sequential Bayesian approach with a Beta regression model using SARS-CoV-2 RNA concentrations measured in WW as covariable. This approach allows us to compute the TPR based on wastewater measurements and to incorporate changes in viral transmission dynamics through the adaptive scheme. Our results demonstrate that the proposed model provides a more comprehensive understanding of COVID-19 transmission dynamics compared to relying solely on clinical case detection. The model can inform public health interventions and serve as a powerful tool for monitoring COVID-19 outbreaks.