Interplay between epidemic spread and information diffusion on two-layered networks with partial mapping

Abstract

We propose an epidemic model to investigate the interplay between disease spread and information diffusion on two-layered networks, in which there exists the mapping relationship among only partial node pairs. In the model, one layer characterizes the spread of epidemics in the physical contact network, while the other layer represents the spread of epidemic-related prevention information in the social network. If a pair of nodes have the correspondence, the individual with knowledge of prevention information will take effective measures to avoid being infected; otherwise, the epidemic and related information will independently spread on the corresponding network. Then, we build the dynamical equations for the epidemic dynamics by using the micro Markov chain (MMC) method, and the epidemic threshold can be calculated on the basis of adjacent matrix constructed from the contact network. The analytical results indicate that the correspondence rate between node pairs on two-layered networks can significantly influence this threshold. Finally, by comparing the MMC and Monte Carlo simulation results, it is found that they are highly consistent and thus MMC method can be used to predict the outbreak of epidemics.