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Optimization of Infectious Disease Intervention Measures Based on Reinforcement Learning -- Empirical analysis based on UK COVID-19 epidemic data

arXiv 2505.04161 ↗First posted 7 May 2025cs.LG cs.CY cs.MAcited by 0

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Abstract (arXiv)

Globally, the outbreaks of infectious diseases have exerted an extremely profound and severe influence on health security and the economy. During the critical phases of epidemics, devising effective intervention measures poses a significant challenge to both the academic and practical arenas. There is numerous research based on reinforcement learning to optimize intervention measures of infectious diseases. Nevertheless, most of these efforts have been confined within the differential equation based on infectious disease models. Although a limited number of studies have incorporated reinforcement learning methodologies into individual-based infectious disease models, the models employed therein have entailed simplifications and limitations, rendering it incapable of modeling the complexity and dynamics inherent in infectious disease transmission. We establish a decision-making framework based on an individual agent-based transmission model, utilizing reinforcement learning to continuously explore and develop a strategy function. The framework's validity is verified through both experimental and theoretical approaches. Covasim, a detailed and widely used agent-based disease transmission model, was modified to support reinforcement learning research. We conduct an exhaustive exploration of the application efficacy of multiple algorithms across diverse action spaces. Furthermore, we conduct an innovative preliminary theoretical analysis concerning the issue of "time coverage". The results of the experiment robustly validate the effectiveness and feasibility of the methodological framework of this study. The coping strategies gleaned therefrom prove highly efficacious in suppressing the expansion of the epidemic scale and safeguarding the stability of the economic system, thereby providing crucial reference perspectives for the formulation of global public health security strategies.

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References · 13

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  12. 2020Reinforced Epidemic Control: Saving Both Lives and Economycited by 9
1 more
  1. 2023Planning Multiple Epidemic Interventions with Reinforcement Learningcited by 5

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  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. 2025On a Reinforcement Learning Methodology for Epidemic Control, with application to COVID-19
  5. 2026Neetyabhas: A Framework for Uncertainty-Aware Public Policy Optimization in Rational Agent-Based Models
  6. 2025H2-MARL: Multi-Agent Reinforcement Learning for Pareto Optimality in Hospital Capacity Strain and Human Mobility during Epidemic
  7. 2024SIR-RL: Reinforcement Learning for Optimized Policy Control during Epidemiological Outbreaks in Emerging Market and Developing Economies
  8. 2024Learning Epidemiological Dynamics via the Finite Expression Method