Effects of seeds on cooperate epidemic spreading on complex networks

Abstract

We study the impact of seeds on cooperate epidemic spreading on complex networks. A cooperative spreading model is proposed, in which two diseases are spreading simultaneously. Once the nodes are infected by one disease, they will have a larger probability of being infected by the other. Besides, we adopt five different selection strategies to choose the seeds, and the set size of seeds is fixed at five nodes. Through extensive Monte Carlo simulations, we find that the final fraction of nodes that have been infected by one or both diseases display continuous phase transition on both synthetic networks and real-world networks, and the selection strategy does not alter the transition type. Besides, we find that the eigenvector centrality promotes the cooperative spreading on the artificial network, and the degree centrality promotes the spreading of the two cooperative diseases on the real-world networks. The results of this study are of great significance for the development of the targeted strategies of disease control.