Learning to Mitigate Epidemic Risks: A Dynamic Population Game Approach
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Date
2023-12
Publication Type
Journal Article
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yes
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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.
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Publication status
published
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Book title
Journal / series
Volume
13 (4)
Pages / Article No.
1106 - 1129
Publisher
Birkhäuser
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Dynamic population game; Epidemic mitigation; Vaccination; Testing; Perturbed best response dynamics
Organisational unit
09478 - Dörfler, Florian / Dörfler, Florian
Notes
Funding
180545 - NCCR Automation (phase I) (SNF)