Identifying super-spreaders in information–epidemic coevolving dynamics on multiplex networks

Abstract

Identifying super-spreaders in epidemics is important to suppress the spreading of disease especially when the medical resource is limited. In the modern society, the information on epidemics transmits swiftly through various communication channels which contributes much to the suppression of epidemics. Here we study on the identification of super-spreaders in the information–disease coupled spreading dynamics. Firstly, we find that the centralities in physical contact layer are no longer effective to identify super-spreaders in epidemics, which is due to the suppression effects from the information spreading. Then by considering the structural and dynamical couplings between the communication layer and physical contact layer, we propose a centrality measure called coupling-sensitive centrality to identify super-spreaders of disease in the coevolving dynamics. Simulation results on synthesized and real-world multiplex networks show that the proposed measure is not only much more accurate than centralities on the single-layer network, but also outperforms two typical multilayer centralities in identifying super-spreaders. These findings imply that considering the structural and dynamical couplings between layers is very necessary in identifying the key roles in the coupled multilayer systems.