Coupled epidemiological and wastewater modeling at the urban scale: A case study for Munich.

Epidemiological modeling is critical to guide public health interventions, but model performance depends on data availability and quality. While clinical reports 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 Munich and quantify how sampling protocols, precipitation, viral decay, normalization strategies, and intervention timing shape the relationship between wastewater measurements and disease prevalence. We find that under appropriate normalization and analysis strategies, wastewater data can provide advance warning of increases in disease burden. Our results guide WBS design and integration into predictive early-warning systems, and our framework is generalizable to other COVID-19-like pathogens, thereby enabling robust disease monitoring.