Modeling COVID-19 with Mean Field Evolutionary Dynamics: Social Distancing and Seasonality

Hao Gao, Wuchen Li, Miao Pan, Zhu Han, and H. Vincent Poor

116 314.314-325

10.23919/JCN.2021.000032

Abstract :The coronavirus pandemic has been declared aworld health emergency by the World Health Organization,which has raised the importance of an accurate epidemiologicalmodel to predict the evolution of COVID-19. In this paper, wepropose mean field evolutionary dynamics (MFEDs), inspired byoptimal transport theory and mean field games on graphs, tomodel the evolution of COVID-19. In the MFEDs, we derivethe payoff functions for different individual states from thecommonly used replicator dynamics (RDs) and employ them togovern the evolution of epidemics. We also compare epidemicmodeling based on MFEDs with that based on RDs throughnumerical experiments. Moreover, we show the efficiency of theproposed MFED-based model by fitting it to the COVID-19statistics of Wuhan, China. Finally, we analyze the effects ofone-time social distancing as well as the seasonality of COVID-19 through the post-pandemic period.​ 

Index terms :COVID-19, mean field evolutionary dynamics, replicator dynamics, seasonality, social distancing.