Work

A multi-agent reinforcement learning framework for public health decision analysis

Year: 2025

Type: article

Abstract: Human immunodeficiency virus (HIV) is a major public health concern in the United States (U.S.), with about 1.2 million people living with it and about 35,000 newly infected each year. There are considerable geographical disparities in HIV burden and care access across the U.S. The ’Ending the HIV Epidemic (EHE)’ initiative by the U.S. Department of Health and Human Services aims to reduce new infections by 90% by 2030, by improving coverage of diagnoses, treatment, and prevention interventions and prioritizing jurisdictions with high HIV prevalence. One of the approaches towards achieving this objective includes developing intelligent decision-support systems that can help optimize resource allocation and intervention strategies. Existing decision analytic models either focus on individual cities or aggregate national data, failing to capture jurisdictional interactions critical for optimizing intervention strategies. To address this, we propose a multi-agent reinforcement learning (MARL) framework that enables jurisdiction-specific decision-making while accounting for cross-jurisdictional epidemiological interactions. Our framework functions as an intelligent resource optimization system, helping policymakers strategically allocate interventions based on dynamic, data-driven insights. Experimental results across jurisdictions in California and Florida demonstrate that MARL-driven policies outperform traditional single-agent reinforcement learning approaches by reducing new infections under fixed budget constraints. Our study highlights the importance of incorporating jurisdictional dependencies in decision-making frameworks for large-scale public initiatives. By integrating multi-agent intelligent systems, decision analytics, and reinforcement learning, this study advances expert systems for government resource planning and public health management, offering a scalable framework for broader applications in healthcare policy and epidemic management. • Propose a decision framework using multi-agent learning to optimize epidemic interventions. • Evaluate dynamic policies for testing, treatment, and prevention across regions and risk groups. • Demonstrate improved epidemic control under constrained budgets using adaptive interventions. • Highlight the impact of jurisdictional interactions on optimal health resource planning. • Support government agencies in prioritizing healthcare actions with intelligent analytics.

Source: Healthcare Analytics

DOI: 10.1016/j.health.2025.100436

Authors: Dinesh Sharma, Ankit Shah, Chaitra Gopalappa

Institutions: University of South Florida, Indiana University Bloomington, Indiana University, University of Massachusetts Amherst

Language: English

Topic: HIV/AIDS Research and Interventions

Subfield: Infectious Diseases

Field: Medicine

Domain: Health Sciences

SDGs: Good health and well-being, Reduced inequalities

Open Access status: gold

Estimated APC paid: $1,790