mlsanityData to 14 Oct 2026

SIR-RL: Reinforcement Learning for Optimized Policy Control during Epidemiological Outbreaks in Emerging Market and Developing Economies

arXiv 2404.08423 ↗First posted 12 Apr 2024cs.LG physics.soc-ph q-bio.PEcited by 1

On the map: Deep reinforcement learning methods → RL for clinical treatment and surgery

Abstract (arXiv)

The outbreak of COVID-19 has highlighted the intricate interplay between public health and economic stability on a global scale. This study proposes a novel reinforcement learning framework designed to optimize health and economic outcomes during pandemics. The framework leverages the SIR model, integrating both lockdown measures (via a stringency index) and vaccination strategies to simulate disease dynamics. The stringency index, indicative of the severity of lockdown measures, influences both the spread of the disease and the economic health of a country. Developing nations, which bear a disproportionate economic burden under stringent lockdowns, are the primary focus of our study. By implementing reinforcement learning, we aim to optimize governmental responses and strike a balance between the competing costs associated with public health and economic stability. This approach also enhances transparency in governmental decision-making by establishing a well-defined reward function for the reinforcement learning agent. In essence, this study introduces an innovative and ethical strategy to navigate the challenge of balancing public health and economic stability amidst infectious disease outbreaks.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for clinical treatment and surgery, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 63 references that are in the map. First posted on arXiv 12 Apr 2024.

Builds on

4 of 63 references.

References · 4

  1. 2017Deep Reinforcement Learning that Matterscited by 754
  2. 2017A Brief Survey of Deep Reinforcement Learningcited by 483
  3. 2018An Introduction to Deep Reinforcement Learningcited by 206
  4. 2022A general framework for optimising cost-effectiveness of pandemic response under partial intervention measurescited by 2

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Controlling dynamics of stochastic systems with deep reinforcement learningcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2026Empowering Epidemic Response: The Role of Reinforcement Learning in Infectious Disease Control
  2. 2024Modeling and Optimization of Epidemiological Control Policies Through Reinforcement Learning
  3. 2025Learning Pareto-Optimal Pandemic Intervention Policies with MORL
  4. 2026Neetyabhas: A Framework for Uncertainty-Aware Public Policy Optimization in Rational Agent-Based Models
  5. 2024Spatio-Temporal SIR Model of Pandemic Spread During Warfare with Optimal Dual-use Healthcare System Administration using Deep Reinforcement Learning
  6. 2025Optimization of Infectious Disease Intervention Measures Based on Reinforcement Learning -- Empirical analysis based on UK COVID-19 epidemic data
  7. 2025On a Reinforcement Learning Methodology for Epidemic Control, with application to COVID-19
  8. 2025H2-MARL: Multi-Agent Reinforcement Learning for Pareto Optimality in Hospital Capacity Strain and Human Mobility during Epidemic