Influence of individual nodes for continuous-time susceptible-infected-susceptible dynamics on synthetic and real-world networks

In the study of epidemic dynamics a fundamental question is whether a pathogen initially affecting only one individual will give rise to a limited outbreak or to a widespread pandemic. The answer to this question crucially depends not only on the parameters describing the infection and recovery processes but also on where, in the network of interactions, the infection starts from. We study the dependence on the location of the initial seed for the susceptible-infected-susceptible epidemic dynamics in continuous time on networks. We first derive analytical predictions for the dependence on the initial node of three indicators of spreading influence (probability to originate an infinite outbreak, average duration, and size of finite outbreaks) and compare them with numerical simulations on random uncorrelated networks, finding a very good agreement. We then show that the same theoretical approach works fairly well also on a set of real-world topologies of diverse nature. We conclude by briefly investigating which topological network features determine deviations from the theoretical predictions.