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


Journal

Journal of biological dynamics
ISSN: 1751-3766
Titre abrégé: J Biol Dyn
Pays: England
ID NLM: 101299725

Informations de publication

Date de publication:
12 2020
Historique:
pubmed: 31 1 2020
medline: 9 6 2021
entrez: 31 1 2020
Statut: ppublish

Résumé

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).

Identifiants

pubmed: 31996099
doi: 10.1080/17513758.2020.1720322
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

57-89

Auteurs

Sheryl L Chang (SL)

Complex Systems Research Group, Faculty of Engineering and IT, The University of Sydney, Sydney, Australia.

Mahendra Piraveenan (M)

Complex Systems Research Group, Faculty of Engineering and IT, The University of Sydney, Sydney, Australia.
Charles Perkins Centre, The University of Sydney, John Hopkins Drive, Sydney, Australia.

Philippa Pattison (P)

Office of the Deputy Vice-Chancellor (Education), The University of Sydney, Sydney, Australia.

Mikhail Prokopenko (M)

Complex Systems Research Group, Faculty of Engineering and IT, The University of Sydney, Sydney, Australia.
Marie Bashir Institute for Infectious Diseases and Biosecurity, The University of Sydney, Sydney, Australia.

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Classifications MeSH