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. 2023;13(4):1106-1129.
doi: 10.1007/s13235-023-00529-4. Epub 2023 Oct 21.

Learning to Mitigate Epidemic Risks: A Dynamic Population Game Approach

Ashish R Hota  1 , Urmee Maitra  1 , Ezzat Elokda  2 , Saverio Bolognani  2
Affiliations

Learning to Mitigate Epidemic Risks: A Dynamic Population Game Approach

Ashish R Hota et al. Dyn Games Appl. 2023.

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.

Keywords: Dynamic population game; Epidemic mitigation; Perturbed best response dynamics; Testing; Vaccination.

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Conflict of interest statement

Conflict of interestNone of the authors have any competing interest of a personal or financial nature.

Figures

Fig. 1
Fig. 1
Evolution of states in the SAIRU epidemic model under activation, testing and vaccination. Self loops are omitted for better readability
Fig. 2
Fig. 2
Evolution of vaccination, testing and activation policy for agents in state T (top row), proportion of agents in states A,I,U (second row), and quantities related to vaccination and testing (last two rows) with time for different choice of vaccination cost and availability limits (shown at the top of each column)
Fig. 3
Fig. 3
Impact of evolutionary learning rate ηπ on policy and state evolution of agents in state T when state distribution is updated at rate ηd=0.25
Fig. 4
Fig. 4
Aggregate outcome of game-theoretic decisions made by myopic and far-sighted agents under different types of social restrictions imposed by authorities
See this image and copyright information in PMC

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