[2311.00855] A Multi-Agent Reinforcement Learning Framework for Public Health Decision Analysis

Computer Science > Artificial Intelligence arXiv:2311.00855 (cs) [Submitted on 1 Nov 2023 (v1), last revised 4 Apr 2026 (this version, v3)] Title:A Multi-Agent Reinforcement Learning Framework for Public Health Decision Analysis Authors:Dinesh Sharma, Ankit Shah, Chaitra Gopalappa View a PDF of the paper titled A Multi-Agent Reinforcement Learning Framework for Public Health Decision Analysis, by Dinesh Sharma and 2 other authors View PDF HTML (experimental) 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. We develop intelligent decision-support systems to 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-jurisdi...