  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.
