Wastewater surveillance, which regularly examines the pathogen biomarkers in wastewater samples, is a valuable tool for monitoring infectious diseases circulating in communities. Yet, most wastewater-based epidemiology methods, which use wastewater surveillance results for disease inferences, implicitly assume that individuals excrete only at their residential locations and that the population contribute to wastewater samples are static. These simplifying assumptions ignore daily mobility, social interactions, and heterogeneous toilet use behavior patterns, which can lead to biased 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, mobility, and social contacts within a realistic urban environment and extend this agent-based framework with a physiologically motivated defecation cycle and toilet usage patterns. We couple this behavioral model with an infectious disease model to simulate transmissions through spatial and social interactions. When a defecation occurs for an infected agent, we use a pathogen shedding model to determine the amount of pathogen shed in the feces. Such a framework, integrating population mobility, disease transmission, toilet use behavior, and pathogen shedding models, is capable to simulate the Spatial-temporal dynamics of wastewater signals for a city. Using a case study of 10,000 simulated agents in Fulton County, Georgia, we examine how varying infection rates alter epidemic trajectories, pathogen loads in wastewater, and the spatial distribution of contamination across time.

翻译：废水监测通过定期检测废水样本中的病原体生物标志物，成为监测社区传染病传播的重要工具。然而，大多数基于废水监测结果进行疾病推论的废水流行病学方法，都隐含假设个体仅在其居住地排泄，且对废水样本有贡献的人群是静态的。这些简化假设忽略了日常流动性、社会互动以及异质性的厕所使用行为模式，可能导致对废水监测结果的偏误解读，尤其是在社区、机构或建筑物等上游采样点。本研究提出一种基于智能体的地理空间模拟框架：在成熟的"生活模式"模型基础上，我们在真实城市环境中模拟人类的日常活动、流动性与社会接触，并将该智能体框架扩展至具有生理学动机的排便周期与厕所使用模式。我们将此行为模型与传染病模型耦合，通过空间和社会互动模拟疾病传播。当受感染智能体发生排便行为时，我们采用病原体排放模型确定粪便中的病原体释放量。这种整合了人口流动性、疾病传播、厕所使用行为和病原体排放模型的框架，能够模拟城市废水监测信号的时空动态特征。通过对佐治亚州富尔顿县10,000个模拟智能体的案例研究，我们分析了不同感染率如何改变流行病传播轨迹、废水中的病原体负荷以及污染物的时空分布特征。