Robust real-time estimation of pathogen transmission dynamics from wastewater

Wastewater monitoring has proven effective for tracking SARS-CoV-2 transmission during the COVID-19 pandemic. However, estimating transmission parameters for other pathogens remains difficult due to lower concentrations in sewage, uncertain shedding kinetics, and limited clinical validation data. Here we present a Bayesian semi-mechanistic wastewater model, called EpiSewer, which jointly accounts for uncertainty in infection dynamics, pathogen shedding, and measurement noise, including outliers and non-detects. This framework enables direct inference of transmission dynamics from raw concentration and flow data, eliminating the need for preprocessing such as smoothing, imputation, or outlier removal. It also provides short-term concentration forecasts for out-of-sample validation. We assessed EpiSewer across three seasons of multi-pathogen wastewater surveillance at 6–14 treatment plants in Switzerland, estimating the effective reproduction number ( R _t ) for SARS-CoV-2, influenza A virus (IAV), and respiratory syncytial virus (RSV) in real time. R _t estimates were consistent and robust to measurement noise, even with IAV and RSV concentrations 10–50 times lower than SARS-CoV-2. Fourteen-day concentration forecasts were well-calibrated, with minimal bias during both epidemic growth and decline. Under reduced sampling frequencies, EpiSewer maintained unbiased forecasts while accurately reflecting increased uncertainty. Our approach enables robust inference of transmission dynamics for lower-abundance pathogens with limited clinical surveillance, using only a few wastewater samples per week.