ArticlePhilosophical transactions of the Royal Society of London. Series B, Biological sciences2019
Context matters: using reinforcement learning to develop human-readable, state-dependent outbreak response policies.
Article in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Scenario-aware control of multipathway spread processes: Application to biological invasions.PNAS nexus · 2026Article
- A Scoping Review of Machine Learning Applications Across Epidemiological Stages of Zoonotic Disease.Transboundary and emerging diseases · 2026Article
- Potential benefits of adaptive control strategies are outweighed by costs of infrequent, but dramatically larger disease outbreaks.Royal Society open science · 2025Article
- Reinforcement learning for healthcare operations management: methodological framework, recent developments, and future research directions.Health care management science · 2025Review
- Optimal control prevents itself from eradicating stochastic disease epidemics.PLoS computational biology · 2025Article
- Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges.Nature communications · 2025Article
- Article
- A deep reinforcement learning based decision-making approach for avoiding crowd situation within the case of Covid'19 pandemic.Computational intelligence · 2022Article
- Dynamic resource allocation for controlling pathogen spread on a large metapopulation network.Journal of the Royal Society, Interface · 2022Article
- Research perspectives on animal health in the era of artificial intelligence.Veterinary research · 2021Review
- Transient disease dynamics across ecological scales.Theoretical ecology · 2021Article
- Anticipating future learning affects current control decisions: A comparison between passive and active adaptive management in an epidemiological setting.Journal of theoretical biology · 2020Article
- Effects of regional differences and demography in modelling foot-and-mouth disease in cattle at the national scale.Interface focus · 2020Article
- Preface to theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019Article
- Detection, forecasting and control of infectious disease epidemics: modelling outbreaks in humans, animals and plants.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019Article
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7 authors.
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Abstract
The number of all possible epidemics of a given infectious disease that could occur on a given landscape is large for systems of real-world complexity. Furthermore, there is no guarantee that the control actions that are optimal, on average, over all possible epidemics are also best for each possible epidemic. Reinforcement learning (RL) and Monte Carlo control have been used to develop machine-readable context-dependent solutions for complex problems with many possible realizations ranging from video-games to the game of Go. RL could be a valuable tool to generate context-dependent policies for outbreak response, though translating the resulting policies into simple rules that can be read and interpreted by human decision-makers remains a challenge. Here we illustrate the application of RL to the development of context-dependent outbreak response policies to minimize outbreaks of foot-and-mouth disease. We show that control based on the resulting context-dependent policies, which adapt interventions to the specific outbreak, result in smaller outbreaks than static policies. We further illustrate two approaches for translating the complex machine-readable policies into simple heuristics that can be evaluated by human decision-makers. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.
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