The impact of individual heterogeneity on the coupled awareness-epidemic dynamics in multiplex networks

Abstract

Awareness of disease outbreaks can trigger changes in human behavior and has a significant impact on the spread of epidemics. Previous studies usually considered the coupled awareness-epidemic dynamics to be two competing processes that interact in the information and epidemic layers. However, these studies mostly assumed that all aware individuals have the same reduced infectivity and that different neighbors have the same influence on one's perception, ignoring the heterogeneity of individuals. In this paper, we propose a coupled awareness-epidemic spreading model in multiplex networks incorporating three types of heterogeneity: (1) the heterogeneity of individual responses to disease outbreaks, (2) the influence heterogeneity in the epidemic layer, and (3) the influence heterogeneity in the information layer. The theoretical analysis shows that the influence heterogeneity in the information layer has two-stage effects on the epidemic threshold. Moreover, we find that the epidemic threshold in the higher stage depends on the heterogeneity of individual responses and the influence heterogeneity in the epidemic layer, while the epidemic threshold in the lower stage is independent of awareness spreading and individual behaviors. The results give us a better understanding of how individual heterogeneity affects epidemic spreading and provide some practical implications for the control of epidemics.

Coevolution spreading in complex networks

“…The epidemic threshold increases with the contact-based information, but does not change with the information about the local and global prevalence of the epidemic. Furthermore, the effects of the heterogeneity of individuals' responses, and the structures of the virtual and contact networks are investigated, and the existence of two-stage effects on the epidemic threshold is demonstrated [274]. Zang considered that the awareness transmission probability equals the fraction of nodes in the aware state, and found that the epidemic spreading is greatly suppressed [275].…”

The impact of firm heterogeneity and awareness in modeling risk propagation on multiplex networks

“…The same happens to firms: When enterprise are aware of risks, they will take certain measures and countermeasures to avoid being triggered by these firms that have already occurred risk. In recent years, there is a growing interest in studying the dynamical interplay between epidemic spreading and awareness diffusion [19][20][21][22][23][24][25][26], which is typically modeled as two competing spreading in multiplex networks. Two diffusive processes are interacting with each other in a two-layer network, where the epidemic spreads on one layer and the awareness propagates on another one.…”

“…Following this thought, Granell et al proposed a UAU-SIS model to study the interplay of epidemic spreading and diffusion of awareness, and found that the spreading of awareness is able to control the onset of epidemic [19]. Different models have been proposed in order to extend the coupled awareness-epidemic dynamics corresponding to diverse realistic scenarios by considering other various factors, such as local awareness [20], individual heterogeneity [21,25,27], self-initiated awareness [22], global awareness [25,28], etc.…”

A colored mean-field model for analyzing the effects of awareness on epidemic spreading in multiplex networks

“…The friendship network may not be exactly the same as the contact network [13], yet there may exist remarkable link overlap between the friendship and contact networks. In this sense, the framework of multiplex/multiplayer networks can be effective in analyzing the dynamical interplay between individual behaviors and spreading dynamics [14][15][16].…”

“…While extensive studies have been carried out on epidemic spreading in multiplex networks with a strong sense on analyzing awareness spreading dynamics (e.g., [16,17]), studies focusing on the effects of risk perception in multiplex networks models are still relatively limited. Massaro and Bagnoli [18] were the first to consider a discrete-time model with risk perception in a multiplex network.…”