Real-time estimation of pathogen transmission dynamics from wastewater

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Abstract Wastewater monitoring proved 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 EpiSewer, a Bayesian semi-mechanistic wastewater model that jointly accounts for infection dynamics, pathogen shedding, and measurement noise, including outliers and non-detects. This enables direct inference of the effective reproduction number ( R t ) and epidemic growth rate ( r t ) from raw concentration and flow data, eliminating the need for prior smoothing, imputation, or outlier removal. We assessed EpiSewer across three seasons of multi-pathogen wastewater surveillance (Nov 2022 – May 2025) at 6–14 treatment plants in Switzerland, tracking SARS-CoV-2, influenza A virus (IAV), and respiratory syncytial virus (RSV) transmission 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. The model provided well-calibrated fourteen-day concentration forecasts, with minimal bias across epidemic phases. Under reduced sampling frequencies, EpiSewer maintained unbiased forecasts while accurately reflecting 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.

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