Coupled Epidemiological and Wastewater Modeling at the Urban Scale: A Case Study for Munich
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Accurately modeling infection spread is crucial for evaluating public health interventions and informing policy makers. However, model performance depends on data availability and quality. Epidemiological models typically use reported case counts for model calibration which often suffer from under-ascertainment and reporting delays. Wastewater-based surveillance is a promising tool to rapidly capture trends in infection dynamics by detecting viral RNA from fecal shedding. We developed an integrative modeling framework that couples an agent-based infectious disease model with an established wastewater dynamics model. The infection dynamics model provides time- and spatially-resolved information on disease prevalence which are linked to wastewater processes via viral shedding curves. Incorporating extensive heterogenous data sources, the integrative model is calibrated to closely reflect the city of Munich, Germany during the first wave of the COVID-19, starting in the beginning of March to the beginning of June 2020. In a simulation study, we quantify the impact of sampling locations, precipitation events, viral decay and intervention timings on true infection prevalence and wastewater measurements. Additionally, we examine how community structures and mobility behavior influence disease spread and how they shape local differences in prevalence. Our findings indicate that under appropriate normalization and analysis techniques, wastewater data can predict outbreaks and epidemic trends thereby underscoring its value as an early-warning system. The study further revealed that nonpharmaceutical interventions can temporarily alter the relationship between wastewater measurements and disease prevalence. Concluding, our results highlight the value of wastewater-based surveillance on robust disease monitoring while advancing the development of predictive epidemiological models informed by wastewater data.
