Adaptive altruistic strategy in cyclic models during an epidemic

¹ Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam - Science Park 904, 1098 XH Amsterdam, The Netherlands
² School of Science and Technology, Federal University of Rio Grande do Norte - 59072-970, P.O. Box 1524, Natal, RN, Brazil
³ Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte Av. Senador Salgado Filho 300, Natal, 59078-970, Brazil
⁴ Edmond and Lily Safra International Institute of Neuroscience, Santos Dumont Institute Av Santos Dumont 1560, 59280-000, Macaiba, RN, Brazil
⁵ Department of Biomedical Engineering, Federal University of Rio Grande do Norte Av. Senador Salgado Filho 300, Lagoa Nova, 59078-970, Natal, RN, Brazil

^(a) E-mail: jmenezes@ect.ufrn.br (corresponding author)

Received: 29 August 2022
Accepted: 16 November 2022

Abstract

We investigate a cyclic game system where organisms face an epidemic beyond being threatened by natural enemies. As a survival strategy, individuals of one out of the species usually safeguard themselves by approaching the enemies of their enemies and performing social distancing to escape contamination when an outbreak affects the neighbourhood. We simulate how the survival movement strategy to local epidemic surges must adapt if a pathogen mutation makes the disease deadlier. We study the spatial distribution of local outbreaks and observe the influence of disease mortality on individuals' spatial organisation. We show that adapting the survival movement strategy for a high-mortality disease demands an altruistic behaviour of the organisms since their death risk increases. Despite weakening the disease transmission chain, which benefits the species, abandoning refuges provided by safeguarding social interaction increases the vulnerability to being eliminated in the cyclic game. Considering that not all individuals exhibit altruism, we find the relative growth in the species density as a function of the proportion of individuals behaving altruistically. Our results may be helpful for biologists and data scientists to understand how adaptive altruistic processes can affect population dynamics in complex systems.