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A Multi-Agent Reinforcement Learning Framework for Public Health Decision Analysis.

Dinesh Sharma1, Ankit Shah2, Chaitra Gopalappa3

  • 1University of South Florida, Department of Industrial and Management Systems Engineering, 4202 E Fowler Ave, Tampa, 33620, FL, USA.

Healthcare Analytics (New York, N.Y.)
|February 2, 2026
PubMed
Summary
This summary is machine-generated.

A new multi-agent reinforcement learning framework optimizes resource allocation for the Ending the HIV Epidemic initiative. This approach accounts for cross-jurisdictional interactions, outperforming traditional methods in reducing new HIV infections.

Keywords:
Decision Support SystemsEpidemic Resource AllocationHealth Intervention ModelingIntelligent Policy DesignPublic Health AnalyticsReinforcement Learning

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Area of Science:

  • Public Health
  • Artificial Intelligence
  • Epidemiology

Background:

  • Human Immunodeficiency Virus (HIV) presents a significant public health challenge in the U.S., characterized by substantial geographical disparities in disease burden and access to care.
  • The "Ending the HIV Epidemic" (EHE) initiative aims to decrease new infections by 90% by 2030 through improved diagnostics, treatment, and prevention, focusing on high-prevalence areas.
  • Current decision-analytic models for HIV intervention are limited, either focusing on single locations or aggregated national data, thus missing crucial cross-jurisdictional dynamics.

Purpose of the Study:

  • To develop an intelligent decision-support system for optimizing resource allocation and intervention strategies within the EHE initiative.
  • To propose a novel multi-agent reinforcement learning (MARL) framework that facilitates jurisdiction-specific decision-making while considering epidemiological interactions between different areas.
  • To create a system that aids policymakers in strategically allocating resources for HIV prevention and treatment based on dynamic, data-driven insights.

Main Methods:

  • Development of a multi-agent reinforcement learning (MARL) framework designed for intelligent resource optimization.
  • Implementation of jurisdiction-specific decision-making capabilities within the MARL framework to capture cross-jurisdictional epidemiological interactions.
  • Experimental evaluation of the MARL framework using data from California and Florida jurisdictions.

Main Results:

  • The MARL-driven policies demonstrated superior performance compared to traditional single-agent reinforcement learning approaches.
  • The proposed framework effectively reduced new HIV infections under fixed budget constraints.
  • The study confirmed the significance of incorporating jurisdictional dependencies into decision-making frameworks for large-scale public health initiatives.

Conclusions:

  • Multi-agent reinforcement learning offers a powerful approach for optimizing resource allocation in public health initiatives like EHE.
  • Accounting for inter-jurisdictional epidemiological interactions is crucial for effective large-scale epidemic management.
  • This study advances expert systems for government resource planning and public health management, providing a scalable framework for epidemic control.