A groundbreaking study from The Hong Kong University of Science and Technology and Zhejiang University introduces a large language model (LLM)-driven multi-agent framework aimed at enhancing coordinated pandemic control across interdependent regions. Researchers propose assigning an LLM to each administrative area, allowing localized reasoning on epidemiological data while ensuring cross-regional communication. This innovative system effectively synthesizes real-world data and employs a pandemic evolution simulator, enabling proactive policymaking and negotiations. Using state-level COVID-19 data, the framework demonstrates remarkable potential, achieving reductions of up to 63.7% in cumulative infections and 40.1% in deaths. By translating complex epidemiological information into actionable policies, this LLM framework marks a significant advancement in public health decision-making. Its adaptability paves the way for better preparedness against future pandemics and other intricate societal challenges, highlighting the importance of integrated approaches in global health strategies. The study underscores the value of LLM-assisted policymaking for efficient epidemic management and improved public health outcomes.
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