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Researchers introduce HALE, a hybrid agent-based and language-driven epidemic modeling framework that uses LLMs to predict human decision-making within large-scale agent-based simulations. Traditional ABMs rely on static priors that cannot adapt to real-time changes, whereas LLM-powered reasoning enables more dynamic behavioral modeling. As a proof-of-concept, the authors simulate COVID-19's spread and effects in Salt Lake County, UT, demonstrating the framework's scalability and policy-relevance.

• •HALE framework combines LLM reasoning with agent-based modeling for epidemic simulation
• •Addresses static-prior limitation of traditional ABMs by using LLMs to predict human decision-making
• •Proof-of-concept applies the framework to COVID-19 modeling in Salt Lake County, UT

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