Learning to Mitigate Epidemic Risks: A Dynamic Population Game Approach

Author:

Hota Ashish R.,Maitra Urmee,Elokda Ezzat,Bolognani Saverio

Abstract

AbstractWe 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.

Funder

The Institute of Data Engineering, Analytics, and Science Foundation (IDEAS), Technology Innovation Hub (TIH) situated at Indian Statistical Institute, Kolkata

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Swiss Federal Institute of Technology Zurich

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computational Mathematics,Computational Theory and Mathematics,Computer Graphics and Computer-Aided Design,Computer Science Applications,Statistics and Probability,Economics and Econometrics

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