Emergence of more contagious COVID-19 variants from the coevolution of viruses and policy interventions

TL;DR

This paper models the coevolution of COVID-19 viruses and policy interventions using genetic algorithms, revealing how policy responses can influence the emergence of more contagious variants.

Contribution

It introduces a dual genetic algorithm model to simulate virus and policy coevolution, providing insights into how interventions impact virus evolution.

Findings

Simulation reproduces emergence of more contagious variants

Policy responses significantly influence virus evolution

Coevolution visualization aids in policy efficacy assessment

Abstract

At the end of 2020, policy responses to the SARS-CoV-2 outbreak have been shaken by the emergence of virus variants. The emergence of these more contagious, more severe, or even vaccine-resistant strains have challenged worldwide policy interventions. Anticipating the emergence of these mutations to plan ahead adequate policies, and understanding how human behaviors may affect the evolution of viruses by coevolution, are key challenges. In this article, we propose coevolution with genetic algorithms (GAs) as a credible approach to model this relationship, highlighting its implications, potential and challenges. We present a dual GA model in which both viruses aiming for survival and policy measures aiming at minimising infection rates in the population, competitively evolve. Simulation runs reproduce the emergence of more contagious variants, and identifies the evolution of policy responses as a determinant cause of this phenomenon. This coevolution opens new possibilities to visualise the impact of governments interventions not only on outbreak dynamics, but also on its evolution, to improve the efficacy of policies.