title: Epidemic spreading with activity-driven awareness diffusion on multiplex network.
abstract: There has been growing interest in exploring the interplay between epidemic spreading with human response, since it is natural for people to take various measures when they become aware of epidemics. As a proper way to describe the multiple connections among people in reality, multiplex network, a set of nodes interacting through multiple sets of edges, has attracted much attention. In this paper, to explore the coupled dynamical processes, a multiplex network with two layers is built. Specifically, the information spreading layer is a time varying network generated by the activity driven model, while the contagion layer is a static network. We extend the microscopic Markov chain approach to derive the epidemic threshold of the model. Compared with extensive Monte Carlo simulations, the method shows high accuracy for the prediction of the epidemic threshold. Besides, taking different spreading models of awareness into consideration, we explored the interplay between epidemic spreading with awareness spreading. The results show that the awareness spreading can not only enhance the epidemic threshold but also reduce the prevalence of epidemics. When the spreading of awareness is defined as susceptible-infected-susceptible model, there exists a critical value where the dynamical process on the awareness layer can control the onset of epidemics; while if it is a threshold model, the epidemic threshold emerges an abrupt transition with the local awareness ratio α approximating 0.5. Moreover, we also find that temporal changes in the topology hinder the spread of awareness which directly affect the epidemic threshold, especially when the awareness layer is threshold model. Given that the threshold model is a widely used model for social contagion, this is an important and meaningful result. Our results could also lead to interesting future research about the different time-scales of structural changes in multiplex networks.
authors.affiliation: School of Mathematics and Systems Science, Beihang University, Beijing 100191, China.
authors.forename: Quantong
authors.initials: Q
authors.lastname: Guo
authors.affiliation: Key Laboratory of Mathematics Informatics Behavioral Semantics (LMIB), Ministry of Education, China.
authors.forename: Yanjun
authors.initials: Y
authors.lastname: Lei
authors.affiliation: School of Mathematics and Systems Science, Beihang University, Beijing 100191, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Jiang
authors.affiliation: Key Laboratory of Mathematics Informatics Behavioral Semantics (LMIB), Ministry of Education, China.
authors.forename: Yifang
authors.initials: Y
authors.lastname: Ma
authors.affiliation: School of Mathematics and Systems Science, Beihang University, Beijing 100191, China.
authors.forename: Guanying
authors.initials: G
authors.lastname: Huo
authors.affiliation: School of Mathematics and Systems Science, Beihang University, Beijing 100191, China.
authors.forename: Zhiming
authors.initials: Z
authors.lastname: Zheng
mesh_terms: D004058:Diffusion; D058872:Epidemics; D006801:Humans; D008390:Markov Chains; D009010:Monte Carlo Method