用大模型模拟流感期个人报告行为，揭示收入教育是关键影响因素
An Infectious Disease Spread Simulation Based on Large Language Model Decision Making
- 基于大模型生成个体决策，结合真实人口普查数据构建城市级仿真
- 收入与教育显著影响报告率，地理与消息框架也有小幅作用
- 适合做流行病学建模与行为偏差分析的研究者参考
在传染病爆发期间建模个体决策对理解行为动态和制定有效公共卫生干预至关重要。已有研究表明，大语言模型可通过基于人口统计提示和情境上下文生成代理决策来模拟真实人类行为。本文构建了一个基于空间的、基于代理的仿真框架，将大模型生成的自报流感样症状决策整合到基于人口普查的合成人群代理中。位置被作为核心特征：代理被分配到城市内的空间单元，利用真实人口普查数据捕捉不同人口群体的空间分布，支持地理多样性行为建模。我们实现了并比较了三种决策情景：独立推理、家庭影响和信息框架。在旧金山和亚特兰大进行了自报结果的模拟。结果表明，收入和教育是报告率差异的主要驱动因素，地理、大模型选择和信息框架也有较小但一致的影响。该框架生成的合成数据能够体现社会与地理异质性，支持空间流行病学建模和偏差感知的行为分析。
原文摘要 · Abstract (English)
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.
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