Coordinated Pandemic Control with Large Language Model Agents as Policymaking Assistants

2026-01-14

AI Summary

This study addresses the challenges of fragmented cross-regional policies, delayed responses, and insufficient coordination in pandemic control by proposing the first large language model–based multi-agent framework for collaborative policy-making. The framework deploys AI agents across regions, each integrating local epidemic dynamics and real-world human mobility data, and enables inter-regional policy coordination through a structured communication protocol. Coupled with an epidemiological simulator, the system performs counterfactual reasoning and closed-loop optimization to refine interventions. Experiments on U.S. state-level data from 2020 demonstrate that the approach can reduce cumulative infections and deaths in individual states by up to 63.7% and 40.1%, respectively, and achieve nationwide reductions of 39.0% and 27.0%, significantly enhancing the foresight and coordination of global pandemic response efforts.

Technology Category

• Multiagent Systems: Coordination and Collaboration
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Effective pandemic control requires timely and coordinated policymaking across administrative regions that are intrinsically interdependent. However, human-driven responses are often fragmented and reactive, with policies formulated in isolation and adjusted only after outbreaks escalate, undermining proactive intervention and global pandemic mitigation. To address this challenge, here we propose a large language model (LLM) multi-agent policymaking framework that supports coordinated and proactive pandemic control across regions. Within our framework, each administrative region is assigned an LLM agent as an AI policymaking assistant. The agent reasons over region-specific epidemiological dynamics while communicating with other agents to account for cross-regional interdependencies. By integrating real-world data, a pandemic evolution simulator, and structured inter-agent communication, our framework enables agents to jointly explore counterfactual intervention scenarios and synthesize coordinated policy decisions through a closed-loop simulation process. We validate the proposed framework using state-level COVID-19 data from the United States between April and December 2020, together with real-world mobility records and observed policy interventions. Compared with real-world pandemic outcomes, our approach reduces cumulative infections and deaths by up to 63.7% and 40.1%, respectively, at the individual state level, and by 39.0% and 27.0%, respectively, when aggregated across states. These results demonstrate that LLM multi-agent systems can enable more effective pandemic control with coordinated policymaking...

Problem

• pandemic control
• coordinated policymaking
• interregional interdependence
• proactive intervention
• policy fragmentation

Innovation

• large language model
• multi-agent system
• coordinated policymaking
• pandemic control
• counterfactual simulation