Using Evolutionary Game Dynamics to Reproduce Phase Portrait Diversity in a Pandemic Scenario

Abstract

Modeling the time evolution of a pandemic in distinct social and economic environments has proven to be a great challenge for scientists due to the strong coupling of infection dynamics to complex collective human behavior. In this work, we propose a relatively simple extension of the classical Susceptible-InfectedRecovered (SIR) model that encompasses evolutionary game imitation dynamics and is capable of reproducing basic features of real data such as recurrent waves with suppression and re-bounce of cases. In particular, we depict different quantitative and qualitative aspects of infection time series by representing them as trajectories in the modified phase portrait.