Analysis of continuous-time Markovian ɛ -SIS epidemics on networks

Abstract

We analyze continuous-time Markovian ε-SIS epidemics with self-infections on the complete graph. The majority of the graphs are analytically intractable, but many physical features of the ε-SIS process observed in the complete graph can occur in any other graph. In this work, we illustrate that the timescales of the ε-SIS process are related to the eigenvalues of the tridiagonal matrix of the SIS Markov chain. We provide a detailed analysis of all eigenvalues and illustrate that the eigenvalues show staircases, which are caused by the nearly degenerate (but strictly distinct) pairs of eigenvalues. We also illustrate that the ratio between the second-largest and third-largest eigenvalue is a good indicator of metastability in the ε-SIS process. Additionally, we show that the epidemic threshold of the Markovian ε-SIS process can be accurately approximated by the effective infection rate for which the third-largest eigenvalue of the transition matrix is the smallest. Finally, we derive the exact mean-field solution for the ε-SIS process on the complete graph, and we show that the mean-field approximation does not correctly represent the metastable behavior of Markovian ε-SIS epidemics.