SEIRD model in heterogenous populations: The role of commuting and social inequalities in the COVID-19 dynamics

TL;DR

This study uses a SEIRD model to explore how commuting and social inequalities influence COVID-19 spread in heterogeneous populations, highlighting the importance of social and mobility factors in epidemic dynamics.

Contribution

It introduces a SEIRD model incorporating heterogeneity, commuting, and social inequalities, and analyzes the impact on COVID-19 transmission dynamics.

Findings

Higher social inequalities increase infection rates.

Commuting patterns significantly affect epidemic spread.

Sensitivity analysis of R0 highlights key parameters.

Abstract

In this paper we analyze the effects of commuting and social inequalities for the epidemic development of the novel coronavirus (COVID-19). With this aim we consider a SEIRD (susceptible, exposed, infected, recovered and dead by disease) model without vital dynamics in a population divided into patches that have different economic resources and in which the individuals can commute from one patch to another (bilaterally). In the modeling we choose the social and commuting parameters arbitrarily. We calculate the basic reproductive number $R_0$ with the next generation approach and analyze the sensitivity of $R_0$ with respect to the parameters. Furthermore, we run numerical simulations considering a population divided into two patches to bring some conclusions on the number of total infected individuals and cumulative deaths for our model considering heterogeneous populations.