  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.
