Abstract
We review research studies 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. The literature is classified based on (i) type of population modelling (classical or network-based), (ii) frequency of the game (non-repeated or repeated), and (iii) type of strategy adoption (self-learning or imitation). The choice of model is shown to depend on many factors such as the immunity to the disease, the strength of immunity conferred by the vaccine, the size of population and the level of mixing therein. We highlight that while early studies used classical compartmental modelling with self-learning games, in recent years, there is a substantial growth of network-based modelling with imitation games. The review indicates that game theory continues to be an effective tool to model decision-making by individuals with respect to intervention (vaccination or social distancing).
Highlights
Computational epidemiology offers a diverse set of tools for modelling the spread of diseases and the effectiveness of various public health interventions, such as vaccination and social distancing
Individuals in the population groups affected by epidemics decide independently whether or not to follow an intervention policy, and in order to estimate the effects of these individual decisions on the overall epidemic spread, this decision-making is often represented as a separate component of a model
The scale and diversity of interaction patterns call for innovative modelling methodologies that can successfully and coherently accommodate three key elements: infectious disease dynamics, interaction patterns, and the decision-making of individuals
Summary
Computational epidemiology offers a diverse set of tools for modelling the spread of diseases and the effectiveness of various public health interventions, such as vaccination and social distancing. Individuals in the population groups affected by epidemics decide independently whether or not to follow an intervention policy, and in order to estimate the effects of these individual decisions on the overall epidemic spread, this decision-making is often represented as a separate component of a model. In large populations of interacting individuals, the interaction time, number of contacts, nature of interactions, etc. Vary significantly, and this diversity is typically modelled with complex networks, aimed to accurately capture the nuances of the interaction patterns. For large-scale epidemics, the number of individuals in the modelled population can.
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