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

TL;DR

This paper investigates how risk perception influences epidemic spread on multiplex networks, introducing a self-organized percolation method to efficiently determine epidemic thresholds and exploring the impact of network similarity on epidemic control.

Contribution

It develops a self-organized percolation approach for epidemic thresholds and analyzes the effect of real and information network similarity on epidemic prevention.

Findings

Self-organized method efficiently finds percolation thresholds.

Network similarity affects the ability to prevent epidemics.

High precaution levels can stop epidemics if networks are similar.

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.