Risk Perception and Epidemics in Complex Computer Networks

Abstract

We present a self-organised method for quickly obtaining the epidemic threshold of infective processes on networks. Starting from simple percolation models, we introduce the possibility that the effective infection probability is affected by the perception of the risk of being infected, given by the fraction of infected neighbours. We then extend the model to multiplex networks considering that agents (computer) can be infected by contacts on the physical network, while the information about the infection level may come from a partially different network. Finally, we consider more complex infection processes, with nonlinear interactions among agents.