Threshold and quasi-stationary distribution for the susceptible-infectious-susceptible model on networks

We study the Susceptible-Infectious-Susceptible (SIS) model on arbitrary networks. The well-established \emph{pair approximation} treats neighboring pairs of nodes exactly while making a mean field approximation for the rest of the network. We improve the method by expanding the state space dynamically, giving nodes a memory of when they last became susceptible. The resulting approximation is simple to implement and appears to be highly accurate, both in locating the epidemic threshold and in computing the quasistationary fraction of infected individuals above the threshold, for both finite graphs and infinite random graphs.