Speaker
Description
Norovirus is a leading cause of acute gastroenteritis globally, yet community infection burden remains poorly quantified due to asymptomatic transmission and clinical underreporting. Wastewater-based surveillance provides a community-level signal of infection prevalence, but translating viral RNA concentrations into epidemiological estimates is nontrivial, and requires explicit modeling of the noisy, nonlinear relationship between shedding dynamics and measured wastewater concentrations.
We develop a mechanistic framework that couples a compartmental transmission model to a wastewater observation process driven by symptom-stratified shedding profiles. This structure allows us to infer transmission parameters and reconstruct infection burden directly from wastewater time series, bypassing the underreporting inherent in clinical surveillance. Applied to data from multiple treatment plants in El Paso, Texas, the model estimates up to 13.5% of the population infectious at epidemic peak. Sensitivity analysis identifies the transmission rate and infectious period as the dominant sources of uncertainty in burden estimates.
More broadly, this work illustrates the potential and the challenges of wastewater as a novel data stream. The framework generalizes to other pathogens with high asymptomatic transmission and limited clinical reporting, and points toward open problems in identifiability and data integration for wastewater-based epidemiology.
