Synthetic Wastewater Epidemiology Data Generation using Patterns-of-Life Simulation

Abstract

Wastewater contains rich biological signals that can be used to monitor population health, track infectious diseases, and detect emerging outbreaks. Pathogens and other biomarkers in sewage provide a unique, noninvasive view of disease prevalence at the community level. However, extracting these signals requires extensive field sampling, laboratory analysis, and expert interpretation. Consequently, wastewater-based epidemiology (WBE) datasets are scarce, geographically fragmented, and rarely released as open data. Even when available, existing datasets typically cover short time periods and limited geographic regions, restricting their usefulness for method development, benchmarking, and large-scale modeling. To address this gap, we present an application of an existing patterns-of-life simulation framework for generating synthetic infectious disease and wastewater pathogen datasets for wastewater-based epidemiology. Our approach extends a patterns-of-life simulation framework by using it as a model of human mobility and behavior while incorporating disease transmission and pathogen shedding dynamics. The resulting framework generates high-resolution spatial and temporal datasets capturing infection dynamics, mobility behavior, and wastewater-associated pathogen signals. We release a fully simulated dataset containing check-in records, social network links, infection states, pathogen loads, and ground-truth disease transmission information. These data support controlled experimentation for outbreak detection, source localization, resource allocation, surveillance strategy design, mobility-aware wastewater analysis, and targeted public health interventions. We provide datasets for Fulton County and demonstrate that the framework generalizes to other regions by generating data for any city with available OpenStreetMap information.