Print Email Facebook Twitter Epidemic outbreaks in two-scale community networks Title Epidemic outbreaks in two-scale community networks Author Bonaccorsi, S. Ottaviano, S. De Pellegrini, F. Socievole, A. Van Mieghem, P.F.A. Faculty Electrical Engineering, Mathematics and Computer Science Department Intelligent Systems Date 2014-07-21 Abstract We consider a model for the diffusion of epidemics in a population that is partitioned into local communities. In particular, assuming a mean-field approximation, we analyze a continuous-time susceptible-infected-susceptible (SIS) model that has appeared recently in the literature. The probability by which an individual infects individuals in its own community is different from the probability of infecting individuals in other communities. The aim of the model, compared to the standard, nonclustered one, is to provide a compact description for the presence of communities of local infection where the epidemic process is faster compared to the rate at which it spreads across communities. Ultimately, it provides a tool to express the probability of epidemic outbreaks in the form of a metastable infection probability. In the proposed model, the spatial structure of the network is encoded by the adjacency matrix of clusters, i.e., the connections between local communities, and by the vector of the sizes of local communities. Thus, the existence of a nontrivial metastable occupancy probability is determined by an epidemic threshold which depends on the clusters' size and on the intercommunity network structure. To reference this document use: http://resolver.tudelft.nl/uuid:d006e2cb-7906-4bed-8424-65ef12e6764c DOI https://doi.org/10.1103/PhysRevE.90.012810 Publisher American Physical Society ISSN 1539-3755 Source http://journals.aps.org/pre/abstract/10.1103/PhysRevE.90.012810 Source Physical Review E, 90, 2014 Part of collection Institutional Repository Document type journal article Rights © 2014 American Physical Society Files PDF VanMieghem_2014.pdf 792.42 KB Close viewer /islandora/object/uuid:d006e2cb-7906-4bed-8424-65ef12e6764c/datastream/OBJ/view