Public Goods Games in Disease Evolution and Spread

Journal Article (2025)
Author(s)

Christo Morison (Queen Mary University of London)

Małgorzata Fic (Max-Planck Institute for Evolutionary Biology, Julius-Maximilians-Universität Würzburg)

Thomas Marcou (University of South Bohemia)

Javier Redondo Antón (University of Turin)

Alexander Stein (Barts and The London School of Medicine and Dentistry)

Frank Bastian (Cork Constraint Computation Centre)

Hana Krakovská (Medical University of Vienna)

Mohammadreza Satouri (Student TU Delft)

Frederik J. Thomsen (TU Delft - Mathematical Physics)

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DOI related publication
https://doi.org/10.1007/s13235-025-00619-5 Final published version
More Info
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Publication Year
2025
Language
English
Journal title
Dynamic Games and Applications
Issue number
5
Volume number
15
Article number
030901
Pages (from-to)
1733-1749
Downloads counter
189
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Abstract

Cooperation arises in nature at every scale, from within cells to entire ecosystems. Public goods games (PGGs) are used to represent scenarios characterised by the conflict/dilemma between choosing cooperation as a socially optimal strategy and defection as an individually optimal strategy. Evolutionary game theory is often used to analyse the dynamics of behaviour emergence in this context. Here, we focus on PGGs arising in the disease modelling of cancer evolution and the spread of infectious diseases. We use these two systems as case studies for the development of the theory and applications of PGGs, which we succinctly review. We also posit that applications of evolutionary game theory to decision-making in cancer, such as interactions between a clinician and a tumour, can learn from the PGGs studied in epidemiology, where cooperative behaviours such as quarantine and vaccination compliance have been more thoroughly investigated. Furthermore, instances of cellular-level cooperation observed in cancers point to a corresponding area of potential interest for modellers of other diseases, be they viral, bacterial or otherwise. We aim to demonstrate the breadth of applicability of PGGs in disease modelling while providing a starting point for those interested in quantifying cooperation arising in healthcare.