基于生活模式模拟的合成废水流行病学数据生成
Synthetic Wastewater Epidemiology Data Generation using Patterns-of-Life Simulation
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中文总结 AI 辅助
该研究将生活模式模拟框架扩展为人类移动与行为模型，生成合成废水流行病学数据集，可支持多种公共卫生相关的受控实验，且框架可推广至具备OpenStreetMap信息的其他地区。
中文摘要 AI 辅助
废水中含有丰富的生物信号，可用于监测人群健康、追踪传染病并检测新发疫情。污水中的病原体及其他生物标志物提供了社区层面疾病流行率的独特非侵入性视角。然而，提取这些信号需要大量实地采样、实验室分析及专业解读。因此，基于废水的流行病学（WBE）数据集稀缺、地理分散，且极少作为开放数据发布。即便有可用数据，现有数据集通常覆盖的时间段短、地理区域有限，限制了其在方法开发、基准测试及大规模建模中的实用性。为解决这一缺口，我们将现有生活模式模拟框架应用于生成用于废水流行病学的合成传染病及废水病原体数据集。我们的方法扩展了生活模式模拟框架，将其用作人类移动与行为模型，同时纳入疾病传播及病原体排泄动态。该框架生成高分辨率时空数据集，涵盖感染动态、移动行为及废水相关病原体信号。我们发布了包含签到记录、社交网络链接、感染状态、病原体载量及真实疾病传播信息的完全模拟数据集。这些数据支持疫情检测、源定位、资源分配、监测策略设计、移动感知废水分析及针对性公共卫生干预的受控实验。我们提供富尔顿县的数据集，并通过为任何具备OpenStreetMap信息的城市生成数据，证明该框架可推广至其他地区。
英文摘要
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.
