Game theoretic modelling of infectious disease dynamics and intervention methods: a mini-review

Abstract

We review research papers which use game theory to model the decision making of individuals during an epidemic, attempting to classify the literature and identify the emerging trends in this field. We show that the literature can be classified based on (i) type of population modelling (compartmental or network-based), (ii) frequency of the game (non-iterative or iterative), and (iii) type of strategy adoption (self-evaluation or imitation). We highlight that the choice of model depends on many factors such as the type of immunity the disease confers, the type of immunity the vaccine confers, and size of population and level of mixing therein. We show that while early studies used compartmental modelling with self-evaluation based strategy adoption, the recent trend is to use network-based modelling with imitation-based strategy adoption. Our review indicates that game theory continues to be an effective tool to model intervention (vaccination or social distancing) decision-making by individuals.

arXiv comment24 pages, 10 figures

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• Epidemic processes in complex networks
• Inductive Reasoning Games as Influenza Vaccination Models: Mean Field Analysis
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• Rational social distancing policy during epidemics with limited healthcare capacity
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• Alternating the Population and Control Neural Networks to Solve High-Dimensional Stochastic Mean-Field Games
• Rational social distancing in epidemics with uncertain vaccination timing
• Corona Games: Masks, Social Distancing and Mechanism Design
• Control of cascading failures using protective measures
• Recent trends in socio-epidemic modelling: behaviours and their determinants
• The Co-evolution of Cooperation and Epidemic Spreading
• Mass testing and proactiveness affect epidemic spreading
• Data-driven optimal control of a SEIR model for COVID-19
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