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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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    1. Jovanovic, Boyan & Rosenthal, Robert W., 1988. "Anonymous sequential games," Journal of Mathematical Economics, Elsevier, vol. 17(1), pages 77-87, February.
    2. McKelvey Richard D. & Palfrey Thomas R., 1995. "Quantal Response Equilibria for Normal Form Games," Games and Economic Behavior, Elsevier, vol. 10(1), pages 6-38, July.
    3. Emma Hubert & Thibaut Mastrolia & Dylan Possamai & Xavier Warin, 2020. "Incentives, lockdown, and testing: from Thucydides's analysis to the COVID-19 pandemic," Papers 2009.00484, arXiv.org, revised Apr 2022.
    4. Ashish R. Hota & Shreyas Sundaram, 2017. "Game-Theoretic Vaccination Against Networked SIS Epidemics and Impacts of Human Decision-Making," Papers 1703.08750, arXiv.org, revised Mar 2019.
    5. Adlakha, Sachin & Johari, Ramesh & Weintraub, Gabriel Y., 2015. "Equilibria of dynamic games with many players: Existence, approximation, and market structure," Journal of Economic Theory, Elsevier, vol. 156(C), pages 269-316.
    6. Eitan Altman & Mandar Datar & Francesco Pellegrini & Samir Perlaza & Daniel Sadoc Menasché, 2022. "The Mask Game with Multiple Populations," Dynamic Games and Applications, Springer, vol. 12(1), pages 147-167, March.
    7. Shutian Liu & Yuhan Zhao & Quanyan Zhu, 2022. "Herd Behaviors in Epidemics: A Dynamics-Coupled Evolutionary Games Approach," Dynamic Games and Applications, Springer, vol. 12(1), pages 183-213, March.
    8. Ioannis Kordonis & Athanasios-Rafail Lagos & George P. Papavassilopoulos, 2022. "Dynamic Games of Social Distancing During an Epidemic: Analysis of Asymmetric Solutions," Dynamic Games and Applications, Springer, vol. 12(1), pages 214-236, March.
    9. Berenice Anne Neumann, 2020. "Stationary Equilibria of Mean Field Games with Finite State and Action Space," Dynamic Games and Applications, Springer, vol. 10(4), pages 845-871, December.
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    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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