SEAIR epidemic spreading model of COVID-19

TL;DR

This paper introduces a SEAIR epidemic model for COVID-19 that accounts for delayed infectiousness and asymptomatic spread, analyzing its behavior on various network structures and identifying factors influencing infection risk.

Contribution

It provides a theoretical analysis of the SEAIR model on different network types, establishing relationships between epidemic thresholds and endemic equilibria, and links eigenvector centrality to infection risk.

Findings

Analytical relationships between epidemic thresholds and susceptible populations.

Eigenvector centrality approximately predicts individual infection risk.

Model captures COVID-19's delayed and asymptomatic infectiousness characteristics.

Abstract

We study Susceptible-Exposed-Asymptomatic-Infectious-Recovered (SEAIR) epidemic spreading model of COVID-19. It captures two important characteristics of the infectiousness of COVID-19: delayed start and its appearance before onset of symptoms, or even with total absence of them. The model is theoretically analyzed in continuous-time compartmental version and discrete-time version on random regular graphs and complex networks. We show analytically that there are relationships between the epidemic thresholds and the equations for the susceptible populations at the endemic equilibrium in all three versions, which hold when the epidemic is weak. We provide theoretical arguments that eigenvector centrality of a node approximately determines its risk to become infected.