Coupled Epidemiological and Wastewater Modeling at the Urban Scale: A Case Study for Munich

Disease surveillance and epidemiological modeling are critical to guide public health interventions, but model performance depends on data availability and quality. While clinical reports often suffer from under-ascertainment and delays, wastewater-based surveillance (WBS) can rapidly capture community infection dynamics by detecting viral RNA from both symptomatic and asymptomatic cases. However, WBS data can be difficult to interpret. Here, we present a coupled model of infectious disease and wastewater dynamics designed for scalability to large cities. We calibrate the model to the first COVID-19 wave in the city of Munich and quantify how sampling protocols, precipitation infiltration, viral decay, normalization strategies, and intervention timing can shape the relationship between wastewater measurements and disease prevalence, thereby improving the interpretability and practical value of wastewater data for epidemiological decision-making. We find that when appropriate sampling, normalization, and analysis strategies are utilized, wastewater data can reveal changes in prevalence earlier than clinical reports and provide advance warning of upcoming increases in disease burden. Our results guide WBS design and integration into predictive early-warning systems. Our modeling framework is generalizable to other COVID-19-like pathogens to help enable robust, cost-efficient disease monitoring.