Abstract
Wastewater surveillance, which regularly measures pathogen biomarkers in wastewater samples, is a valuable tool for monitoring infectious diseases circulating in communities. Yet, most wastewater-based epidemiology methods that use wastewater surveillance results to infer disease trends implicitly assume that individuals excrete only at their residential locations and that the populations contributing to wastewater samples are static. These simplifying assumptions ignore daily mobility, social interactions, and heterogeneous toilet-use patterns, which can bias the interpretation of wastewater results, especially at upstream sampling locations such as neighborhoods, institutions, or buildings. Here, we introduce an agent-based geospatial simulation framework. Building on an established Patterns of Life model, we simulate daily human activities within a realistic urban environment and extend the framework with a physiologically motivated defecation cycle and toilet-use patterns. We couple this behavioral model with an infectious disease model to simulate transmission through spatial and social interactions. When an infected agent defecates, a pathogen-shedding model determines the amount of pathogen released in the feces. By integrating population mobility, disease transmission, toilet-use behavior, and pathogen shedding, the framework can simulate the spatiotemporal dynamics of wastewater pathogen loads. Using a case study of 10,000 simulated agents in Fulton County, Georgia, we examine how varying infection rates alter epidemic trajectories, wastewater pathogen loads, and the spatial distribution of pathogen shedding over time. Our results show that mobility and toilet use can substantially decouple residential disease prevalence from wastewater pathogen loads and demonstrate how behaviorally grounded simulations can support interpretation, scenario analysis, and wastewater surveillance