Multi-state epidemic processes on complex networks

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dc.creatorMasuda, Naoki
dc.creatorKonno, Norio
dc.date2005-04-13
dc.date2006-10-07
dc.date.accessioned2026-07-07T06:37:41Z
dc.date.available2026-07-07T06:37:41Z
dc.descriptionInfectious diseases are practically represented by models with multiple states and complex transition rules corresponding to, for example, birth, death, infection, recovery, disease progression, and quarantine. In addition, networks underlying infection events are often much more complex than described by meanfield equations or regular lattices. In models with simple transition rules such as the SIS and SIR models, heterogeneous contact rates are known to decrease epidemic thresholds. We analyze steady states of various multi-state disease propagation models with heterogeneous contact rates. In many models, heterogeneity simply decreases epidemic thresholds. However, in models with competing pathogens and mutation, coexistence of different pathogens for small infection rates requires network-independent conditions in addition to heterogeneity in contact rates. Furthermore, models without spontaneous neighbor-independent state transitions, such as cyclically competing species, do not show heterogeneity effects.
dc.description7 figures, 1 table
dc.identifierhttps://arxiv.org/abs/cond-mat/0504329
dc.identifierhttp://arxiv.org/abs/cond-mat/0504329
dc.identifierJournal of Theoretical Biology, 243, 64-75 (2006)
dc.identifierdoi:10.1016/j.jtbi.2006.06.010
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/100477
dc.subjectDisordered Systems and Neural Networks
dc.subjectPopulations and Evolution
dc.titleMulti-state epidemic processes on complex networks
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