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Learning to Mitigate Epidemic Risks: A Dynamic Population Game Approach

Author

Listed:
  • Ashish R. Hota

    (IIT Kharagpur)

  • Urmee Maitra

    (IIT Kharagpur)

  • Ezzat Elokda

    (ETH Zürich)

  • Saverio Bolognani

    (ETH Zürich)

Abstract

We present a dynamic population game model to capture the behavior of a large population of individuals in presence of an infectious disease or epidemic. Individuals can be in one of five possible infection states at any given time: susceptible, asymptomatic, symptomatic, recovered and unknowingly recovered, and choose whether to opt for vaccination, testing or social activity with a certain degree. We define the evolution of the proportion of agents in each epidemic state, and the notion of best response for agents that maximize long-run discounted expected reward as a function of the current state and policy. We further show the existence of a stationary Nash equilibrium and explore the transient evolution of the disease states and individual behavior under a class of evolutionary learning dynamics. Our results provide compelling insights into how individuals evaluate the trade-off among vaccination, testing and social activity under different parameter regimes, and the impact of different intervention strategies (such as restrictions on social activity) on vaccination and infection prevalence.

Suggested Citation

  • Ashish R. Hota & Urmee Maitra & Ezzat Elokda & Saverio Bolognani, 2023. "Learning to Mitigate Epidemic Risks: A Dynamic Population Game Approach," Dynamic Games and Applications, Springer, vol. 13(4), pages 1106-1129, December.
    • Handle: RePEc:spr:dyngam:v:13:y:2023:i:4:d:10.1007_s13235-023-00529-4
    DOI: 10.1007/s13235-023-00529-4
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    References listed on IDEAS

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    Cited by:

    1. Christian Hilbe & Maria Kleshnina & Kateřina Staňková, 2023. "Evolutionary Games and Applications: Fifty Years of ‘The Logic of Animal Conflict’," Dynamic Games and Applications, Springer, vol. 13(4), pages 1035-1048, December.

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