Speaker
Description
In this paper, 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) generated 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, thereby linking individual decision-making to population-level transmission outcomes. We further embed the same behavioral mechanism into empirically measured temporal contact networks from six real-world scenarios (conference, hospital, workplace, high school, primary school, and college campus).
