Modelling individual decision-making during infectious disease outbreaks is crucial for understanding behavioural dynamics and informing effective public health interventions. Prior work has shown that large language models can simulate realistic human behaviour by generating agent decisions based on demographic prompts and situational context. We build on this foundation with a spatially grounded, agent-based simulation framework that integrates LLM-generated decisions about self-reported influenza-like illness into a census-based synthetic population of agents. Location is treated as a central feature: agents are assigned to spatial units within cities, capturing the spatial distributions of different demographic groups using real-world census data and enabling geographically diverse behavioural modelling. We implement and compare three decision scenarios, independent reasoning, household influence, and message framing, and simulate self-reporting outcomes in San Francisco and Atlanta. Results reveal that income and education are the dominant drivers of reporting rate variation, with smaller but consistent effects from geography, LLM model choice, and message framing. Our framework generates synthetic data that captures both social and geographic heterogeneity, supporting spatial epidemiological modelling and bias-aware behavioural analysis.

翻译：在传染病暴发期间对个体决策进行建模，对于理解行为动态并制定有效的公共卫生干预措施至关重要。已有研究表明，大语言模型能够通过基于人口统计提示和情境背景生成智能体决策，从而模拟真实的人类行为。我们在此基础上构建了一个基于空间地理的智能体仿真框架，该框架将大语言模型生成的关于自报流感样疾病的决策整合到基于人口普查的合成智能体群体中。地理位置被视为核心特征：智能体被分配至城市内的空间单元，利用真实世界的人口普查数据捕捉不同人口群体的空间分布，从而实现地理多样化的行为建模。我们实施并比较了三种决策场景——独立推理、家庭影响和信息框架——并在旧金山和亚特兰大模拟了自报结果。结果表明，收入和受教育程度是报告率差异的主要驱动因素，而地理位置、大语言模型选择和信息框架的影响虽较小但具有一致性。我们的框架生成兼顾社会异质性与地理异质性的合成数据，为空间流行病学建模和偏差感知行为分析提供了支持。