  We study the influence of global, local and community-level risk perception
on the extinction probability of a disease in several models of social
networks. In particular, we study the infection progression as a
susceptible-infected-susceptible (SIS) model on several modular networks,
formed by a certain number of random and scale-free communities. We find that
in the scale-free networks the progression is faster than in random ones with
the same average connectivity degree. For what concerns the role of perception,
we find that the knowledge of the infection level in one's own neighborhood is
the most effective property in stopping the spreading of a disease, but at the
same time the more expensive one in terms of the quantity of required
information, thus the cost/effectiveness optimum is a tradeoff between several
parameters.
