Epidemic spreading and risk perception in multiplex networks: a self-organized percolation method

Abstract

In this paper we study the interplay between epidemic spreading and risk perception on multiplex networks. The basic idea is that the effective infection probability is affected by the perception of the risk of being infected, which we assume to be related to the fraction of infected neighbours, as introduced by Bagnoli et al., PRE 76:061904 (2007). We re-derive previous results using a self-organized method, that automatically gives the percolation threshold in just one simulation. We then extend the model to multiplex networks considering that people get infected by contacts in real life but often gather information from an information networks, that may be quite different from the real ones. The similarity between the real and information networks determine the possibility of stopping the infection for a sufficiently high precaution level: if the networks are too different there is no mean of avoiding the epidemics.

9 pages, 8 figures

References in corpus (3)

• Multirelational Organization of Large-scale Social Networks in an Online World
• Competing spreading processes on multiplex networks: awareness and epidemics
• Risk perception in epidemic modeling

Cited by in corpus (5)

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• Competing spreading processes and immunization in multiplex networks
• Impact of asymptomatic infection on coupled disease-behavior dynamics in complex networks
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