Speaker
Description
Human behavioral responses play a central role in shaping epidemic dynamics, yet they remain difficult to model mechanistically due to their heterogeneity and context dependence. We develop a hybrid, networked SEIR framework that integrates generative AI-driven agents to capture individualized protective behavior. Each agent is characterized by demographic and socioeconomic attributes, and a large language model (LLM) generates daily willingness-to-comply scores from prompts that encode personal traits, occupation and income, local and global epidemic conditions, social influence, and policy strength. These AI-generated behavioral states modulate edge-level infection risk on dynamic physical contact networks, linking individual decision-making to population-level transmission outcomes. The framework offers a generalizable approach for integrating generative agent simulation into epidemic modeling and evaluating targeted, context-aware non-pharmaceutical interventions.
