发表机构
University of Calgary; University of Virginia（卡尔加里大学; 弗吉尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究人员提出结合大语言模型（LLMs）的流行病生成式自适应行为层（GABLE），将自适应人类行为整合进流行病模型，其生成的接触矩阵预测效果优于流动性驱动矩阵，还可用于前瞻性政策评估。
AI 中文摘要
传染病的传播受人类互动模式的影响，而这种模式会随流行病状况的变化而调整。捕捉这些依赖于情境的行为仍是流行病模型面临的核心挑战。在此，我们通过使用大语言模型（LLMs）在机制性流行病模型中表征自适应人类行为，重新定义了这一挑战。我们通过流行病生成式自适应行为层（GABLE）来实现这一思路，该层调整LLMs以推断对流行病及政策状况的行为反应，并将其转化为与机制性流行病模型耦合的年龄结构接触矩阵。将其应用于法国的新冠疫情时，GABLE重现了人群混合及年龄特异性接触结构的反应，这些反应仍具有流行病学意义。在短期预测中，LLM生成的接触矩阵优于来自真实世界流动性数据的流动性驱动矩阵，且在较长时间范围内的提升最为显著。GABLE还超越了预测范畴，可在候选干预措施实施前通过预测行为和流行病反应来进行前瞻性政策评估。当提供后续实施的政策时，GABLE重现了流行病轨迹，并对不同政策的时机和组成产生了不同的反应。通过利用LLMs作为灵活的行为层，GABLE提供了一个将情境敏感的行为生成与流行病动力学耦合的框架。
英文摘要
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.
