Abstract / Summary
Abstract Wastewater-based epidemiology provides a low-cost, scalable view of community infection dynamics, but converting these signals into actionable epidemiological insights remains difficult. Mechanistic models offer interpretability, yet assumptions such as a constant transmission rate limit realism over long simulation horizons and heterogeneous settings. We present a susceptible–exposed–infectious–recovered (SEIR) universal differential equation (UDE) with time-varying transmission and reporting rates represented by neural networks to map wastewater viral loads to case data. Parameter and prediction uncertainties are quantified using an ensemble method. We assessed the method using newly collected SARS-CoV-2 data for Bonn, Germany, as well as published SARS-CoV-2 data for five cities in Rhineland-Palatinate, Germany. The proposed approach produces realistic out-of-sample estimates of case counts over an up to 50-week evaluation horizon, and it learns city-specific mappings to prevalence that generalise within each location. Compared to SEIR models with fixed transmission rates, the UDE captures non-stationary drivers (policy, behaviour, seasonality) without sacrificing epidemiological structure, while propagating observation and model uncertainty. Accordingly, the approach facilitates a scalable interpretation and exploitation of wastewater data for the monitoring of infectious diseases.