In this paper we study the interplay between epidemic spreading and riskperception on multiplex networks. The basic idea is that the effectiveinfection probability is affected by the perception of the risk of beinginfected, 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 previousresults using a self-organized method, that automatically gives the percolationthreshold in just one simulation. We then extend the model to multiplexnetworks considering that people get infected by contacts in real life butoften gather information from an information networks, that may be quitedifferent from the real ones. The similarity between the real and informationnetworks determine the possibility of stopping the infection for a sufficientlyhigh precaution level: if the networks are too different there is no mean ofavoiding the epidemics.
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