Abstract
Networks which trace the activities and interactions of individuals, social patterns, transportation fluxes and population movements on a local and global scale have been analyzed and found to exhibit complex features encoded in large scale heterogeneity, self-organization and other properties typical of complex systems.
We discuss the impact of these complex features on the behavior of epidemic spreading
processes. We first discuss the general results obtained for basic compartmental models (SIR, SIS) and then report on the effect of the heterogeneity of real world transportation networks in meta-population models for the forecast of the large scale spreading of diseases.
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