Human behaviour during epidemics affects infectious disease dynamics, but quantifying this remains deeply challenging. Here we introduce the Epi-LLM framework: a novel integration of agent-based modelling, real-life epigames, and large language models (LLMs) in which a synthetic society of agents reasons and adapts dynamically over an outbreak contact network. Comparing synthetic agent behaviour against a no-intervention SEIR baseline and human participant data from the AUIB epigame study, we find that LLM agents across four different architectures reduced peak active infections, with quarantine compliance peaking at 58-65% on day six of the 15-day simulation. A binomial generalised linear model showed that perceived health severity was the strongest predictor of quarantine behaviour ($β= 0.33, p = 0.002$), yielding a pseudo-$R^2$ of 0.055, comparable to the 0.072 observed in the human trial. LLM architecture is a key determinant of epidemic dynamics: low-variance architectures offer greater internal validity for testing behavioural rules, while high-variance models may better represent real-world decision-making. Geographic labels alone do not induce culturally differentiated behaviour; explicit attitudinal parameterisation is required. This proof-of-principle work lays the groundwork for deploying the Epi-LLM framework as a scalable, risk-free simulation environment for pandemic preparedness research.

翻译：流行期间的人类行为会影响传染病动态，但量化该影响仍极具挑战性。本文提出Epi-LLM框架：一种融合基于主体建模、现实流行病游戏与大型语言模型（LLM）的创新方法，其中合成主体社会在暴发接触网络上进行动态推理与适应。通过将合成主体行为与无干预SEIR基线及AUIB流行病游戏研究中人类参与者数据进行对比，我们发现四种不同架构的LLM主体均减少了峰值活跃感染人数，其中第6天（共15天模拟）的隔离依从性达到58-65%的峰值。二项式广义线性模型显示，感知健康严重程度是隔离行为的最强预测因子（β=0.33, p=0.002），其伪R²值为0.055，与人体试验中观测到的0.072相当。LLM架构是流行病动态的关键决定因素：低方差架构在测试行为规则时提供更高的内部效度，而高方差模型可能更真实地反映现实决策。仅凭地域标签无法诱导文化分化行为，需显式参数化态度。这项原理验证工作为将Epi-LLM框架部署为可扩展、无风险的大流行准备研究模拟环境奠定了基础。