GABLE用大语言模型模拟人类行为变化，比传统移动数据预测更准，还能提前评估政策效果。
研究人员提出GABLE框架，利用大语言模型模拟疫情中的人类行为变化。该模型在法国COVID-19案例中成功复现了人口混合和年龄特定接触结构。短期预测中，LLM生成的接触矩阵优于基于真实移动数据的矩阵，且预测时间越长优势越明显。GABLE还能评估候选政策实施前后的疫情和人类行为响应。
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 population mixing and age-specific contact structures that remained epidemiologically informative. In short-term forecasting, LLM-generated contact matrices outperformed mobility-driven matrices derived from real-world mobility data, with the largest gains at longer horizons. GABLE also extends beyond forecasting to prospective policy evaluation by projecting behavioral and epidemic responses to candidate interventions before implementation. When supplied with subsequently implemented policies, GABLE reproduced epidemic trajectories and generated distinct responses to alternative policy timing and composition. By leveraging LLMs as a flexible behavioral layer, GABLE provides a framework for coupling context-sensitive behavioral generation with epidemic dynamics.