Small-World Effect in Epidemics Using Cellular Automata

dc.creatorGAGLIARDI, Henrique Fabricio
dc.creatorALVES, Domingos
dc.date.accessioned2012-10-19T23:01:46Z
dc.date.accessioned2018-07-04T15:18:15Z
dc.date.available2012-10-19T23:01:46Z
dc.date.available2018-07-04T15:18:15Z
dc.date.created2012-10-19T23:01:46Z
dc.date.issued2010
dc.identifierMATHEMATICAL POPULATION STUDIES, v.17, n.2, p.79-90, 2010
dc.identifier0889-8480
dc.identifierhttp://producao.usp.br/handle/BDPI/24582
dc.identifier10.1080/08898481003689486
dc.identifierhttp://dx.doi.org/10.1080/08898481003689486
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1621310
dc.description.abstractThe spread of an infectious disease in a population involves interactions leading to an epidemic outbreak through a network of contacts. Extending on Watts and Strogatz (1998) who showed that short-distance connections create a small-world effect, a model combining short-and long-distance probabilistic and regularly updated contacts helps considering spatial heterogeneity. The method is based on cellular automata. The presence of long-distance connections accelerates the small-world effect, as if the world shrank in proportion of their total number.
dc.languageeng
dc.publisherTAYLOR & FRANCIS INC
dc.relationMathematical Population Studies
dc.rightsCopyright TAYLOR & FRANCIS INC
dc.rightsrestrictedAccess
dc.subjectautomata cellular
dc.subjectepidemic spreading
dc.subjectSIR
dc.subjectSIRS
dc.subjectsmall-world
dc.subjecttransmission rules
dc.titleSmall-World Effect in Epidemics Using Cellular Automata
dc.typeArtículos de revistas


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