A Wastewater-based Modeling Framework for Inferring Norovirus Transmission Dynamics.

Norovirus is a leading cause of gastroenteritis globally, yet its true infection burden in the community remains poorly understood because of widespread asymptomatic transmission and underreporting. Wastewater-based surveillance is a promising approach for tracking infection trends, but methods for mechanistically linking wastewater viral concentrations to infection dynamics remain limited. Here, we developed a mechanistic SEIR-V framework to infer community-level norovirus transmission dynamics from wastewater data by jointly modeling disease transmission, viral-shedding kinetics, and wastewater viral load. We leveraged temporal fecal shedding data from a controlled human challenge study and systematic literature review to parametrize symptom-specific shedding profiles and integrate them into the framework. Applied to wastewater data from four treatment plants in El Paso, Texas, the model showed consistent agreement between out-of-sample predictions and observed wastewater viral load across sewersheds while generating model-inferred temporal infection dynamics across disease compartments. Sensitivity analysis further showed that wastewater viral load was influenced by transmission, shedding, and viral loss parameters, whereas inferred infection dynamics were primarily influenced by the transmission rate and infectious duration. This work provides a mechanistic wastewater modeling framework to infer norovirus transmission dynamics and could support proactive public health surveillance, particularly for pathogens with high asymptomatic transmission and limited clinical reporting.