An Infectious Disease Spread Simulation Based on Large Language Model Decision Making
For epidemiologists and public health officials, this framework enables more realistic simulations of behavioral dynamics during outbreaks, but the contribution is incremental as it builds on prior work using LLMs for agent decision-making.
This paper presents a spatially grounded agent-based simulation framework that uses LLMs to model individual decisions about self-reporting influenza-like illness, integrating census-based synthetic populations. Results show income and education are the dominant drivers of reporting rate variation, with smaller effects from geography, LLM model choice, and message framing.
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.