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Optimization of Mitigation Strategies During Epidemics Using Offline Reinforcement Learning

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Social, Cultural, and Behavioral Modeling (SBP-BRiMS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12720))

Abstract

Emerging infectious diseases affect a large number of people throughout the world. Preventing the spread of viruses and mitigating their adverse societal and economic effects are major challenges facing all institutions in society. When reacting to events occurring in real-time, approaches based on human decision-making systems usually encounter difficulties in sorting out the most efficient mitigation strategies. In this paper, we present the framework for a real-time data-driven decision support tool for policymakers. Our framework is based on a reinforcement learning algorithm meant to optimize governmental responses to the state of the epidemic at each time-step. This framework adapts to changes in epidemic-spread given the advances in disease treatment methods and public health interventions. The mitigation strategy is adjusted based on the government’s priorities in a specific region. Our model is validated based on the COVID-19 data collected from New York state, USA.

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Notes

  1. 1.

    https://www.who.int/emergencies/diseases/en/.

  2. 2.

    https://www.cdc.gov/flu/pandemic-resources/.

  3. 3.

    https://covid19tracker.health.ny.gov/.

  4. 4.

    https://www.census.gov/quickfacts/NY.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, USA

    Alina Vereshchaka & Nitin Kulkarni

Authors
  1. Alina Vereshchaka
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  2. Nitin Kulkarni
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Corresponding author

Correspondence to Alina Vereshchaka .

Editor information

Editors and Affiliations

  1. United States Military Academy, West Point, NY, USA

    Robert Thomson

  2. University of Arkansas at Little Rock, Little Rock, AR, USA

    Muhammad Nihal Hussain

  3. Bucknell University, Lewisburg, PA, USA

    Christopher Dancy

  4. United States Military Academy, West Point, NY, USA

    Aryn Pyke

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Vereshchaka, A., Kulkarni, N. (2021). Optimization of Mitigation Strategies During Epidemics Using Offline Reinforcement Learning. In: Thomson, R., Hussain, M.N., Dancy, C., Pyke, A. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2021. Lecture Notes in Computer Science(), vol 12720. Springer, Cham. https://doi.org/10.1007/978-3-030-80387-2_4

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  • DOI: https://doi.org/10.1007/978-3-030-80387-2_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-80386-5

  • Online ISBN: 978-3-030-80387-2

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