The Impact of Heatwaves on Population Health: A Large Language Model-Enhanced Agent-Based Simulation

Abstract

Extreme heat events are increasing in frequency and intensity under climate change, but the socio-behavioral mechanisms that shape community resilience remain insufficiently understood. This study uses a Large Language Model-enhanced agent-based model to simulate responses to a prolonged heatwave in a virtual society. One hundred heterogeneous agents were assigned a Heat Vulnerability Index based on demographic risk factors and observed over 13 simulated days covering baseline, heatwave, and recovery periods. The simulation shows that heat-related impacts are primarily psychosocial and unequally distributed. Agents with higher vulnerability experienced larger declines in perceived safety and social connection than agents with lower vulnerability. Vulnerability also shaped adaptive capacity. More resilient agents maintained routine self-care and protective behaviors, whereas highly vulnerable agents showed behavioral constriction, marked by reduced engagement in protective actions. At the collective level, risk-information diffusion followed a pattern of complex contagion, with adoption driven more by repeated social reinforcement within cohesive networks than by broad exposure alone. These findings suggest that LLM-enhanced simulation can help identify behavioral and social mechanisms of climate resilience and inform heat-risk interventions that combine targeted support for vulnerable groups with community-based information pathways.