  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.
