Adaptive altruistic strategy in cyclic models during an epidemic

TL;DR

This study explores how organisms adapt their survival strategies, including altruism, during epidemics in cyclic populations, especially when facing deadlier pathogen mutations, affecting spatial distribution and species survival.

Contribution

It introduces a model of adaptive altruistic behavior in cyclic epidemic systems and analyzes its impact on spatial organization and species survival under increased pathogen mortality.

Findings

Altruistic behavior increases species resilience during high-mortality epidemics.

Adapting survival strategies influences spatial distribution of outbreaks.

Higher proportion of altruists enhances population density and survival.

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.