Integrating adaptive human behavior into epidemic models with large language models
Infectious disease transmission is shaped by patterns of human interaction, which adapt as epidemic conditions change. Capturing these context-dependent behaviors remains a fundamental challenge for epidemic models. Here, we recast this challenge by using large language models (LLMs) to represent adaptive human behavior within mechanistic epidemic models. We operationalize this idea through Generative Adaptive Behavioral Layer for Epidemics (GABLE), which adapts LLMs to infer behavioral responses to epidemic and policy conditions and translates them into age-structured contact matrices coupled to a mechanistic epidemic model. Applied to COVID-19 in France, GABLE reproduced responses in po...
| Authors | Yicheng Mao, Haoyang Li, Rob Deardon et al. |
|---|---|
| Primary category | physics.soc-ph |
| Published | 30 August 2026 |
| Last updated at source | 30 August 2026 |
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