A Mean Field Game model for COVID-19 with human capital accumulation

TL;DR

This paper develops a mean field game model to analyze how human capital accumulation influences COVID-19 spread, combining epidemiological dynamics with economic decision-making in a tractable framework.

Contribution

It introduces a simplified SEIRD model where infection rates depend on human capital distribution derived from a mean field game approach, linking economic behavior and epidemiology.

Findings

Numerical analysis of the simplified model shows the impact of human capital on infection dynamics.

The model captures complex interactions between individual optimization and disease spread.

Structural issues in multi-agent models are identified and addressed.

Abstract

In this manuscript we present several possible ways of modeling human capital accumulation during the spread of a disease following an agent based approach, where agents behave maximizing their intertemporal utility. We assume that the interaction between agents is of mean field type, yielding a Mean Field Game description of the problem. We discuss how the analysis of a model including both the mechanism of change of species from one epidemiological state to the other and an optimization problem for each agent leads to an aggregate behavior that is not easy to describe, and that sometimes exhibits structural problems. Therefore we eventually propose and study numerically a SEIRD model in which the rate of infection depends on the distribution of the population, given exogenously as the solution to the the Mean Field Game system arising as the macroscopic description of the discrete multi-agent economic model for the accumulation of human capital. Such model arises in fact as a simplified but tractable version of the initial one.