Abstract. We study the inflence of global, local and community-level risk perception on the extinction probability of a disease in several mod- els of social networks. In particular, we study the infection progression as a susceptible-infected-susceptible (SIS) model on several modular net- works, formed by a certain number of random and scale-free communi- ties. 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 e ective 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/e ectiveness optimum is a trade of etween several parameters. Key words: risk perception, SIS model, complex networks



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