Mean field game for modeling of COVID-19 spread

TL;DR

This paper introduces a mean-field control model for COVID-19 spread, dividing the population into groups and providing a numerical algorithm to simulate epidemic propagation while conserving total population, demonstrated through experiments in Novosibirsk.

Contribution

It develops a novel mean-field control approach for epidemic modeling with a numerical algorithm ensuring population conservation.

Findings

Successful simulation of COVID-19 spread over 100 days in Novosibirsk

Model accurately conserves total population during simulations

Demonstrates applicability of mean-field control in epidemic modeling

Abstract

The paper presents the one of possible approach to model the epidemic propagation. The proposed model is based on the mean-field control inside separate groups of population, namely, suspectable (S), infected (I), removed (R) and cross-immune (C). In the paper the numerical algorithm to solve such a problem is presented, which ensures the conservation the total mass of population during timeline. Numerical experiments demonstrate the result of modelling the propagation of COVID-19 virus during two 100 day periods in Novosibirsk (Russia).