Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/67553

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dc.contributor.authorVan Segbroeck, Svenpor
dc.contributor.authorSantos, Francisco C.por
dc.contributor.authorPacheco, Jorge Manuel Santospor
dc.date.accessioned2020-10-16T18:49:05Z-
dc.date.available2020-10-16T18:49:05Z-
dc.date.issued2010-
dc.identifier.issn1553-734Xpor
dc.identifier.urihttp://hdl.handle.net/1822/67553-
dc.description.abstractHuman societies are organized in complex webs that are constantly reshaped by a social dynamic which is influenced by the information individuals have about others. Similarly, epidemic spreading may be affected by local information that makes individuals aware of the health status of their social contacts, allowing them to avoid contact with those infected and to remain in touch with the healthy. Here we study disease dynamics in finite populations in which infection occurs along the links of a dynamical contact network whose reshaping may be biased based on each individual's health status. We adopt some of the most widely used epidemiological models, investigating the impact of the reshaping of the contact network on the disease dynamics. We derive analytical results in the limit where network reshaping occurs much faster than disease spreading and demonstrate numerically that this limit extends to a much wider range of time scales than one might anticipate. Specifically, we show that from a population-level description, disease propagation in a quickly adapting network can be formulated equivalently as disease spreading on a well-mixed population but with a rescaled infectiousness. We find that for all models studied here--SI, SIS and SIR--the effective infectiousness of a disease depends on the population size, the number of infected in the population, and the capacity of healthy individuals to sever contacts with the infected. Importantly, we indicate how the use of available information hinders disease progression, either by reducing the average time required to eradicate a disease (in case recovery is possible), or by increasing the average time needed for a disease to spread to the entire population (in case recovery or immunity is impossible).por
dc.description.sponsorshipFinancial support from FWO-Belgium (S. V. S.) and FCT-Portugal (F. C. S and J.M.P) is gratefully acknowledged. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.por
dc.language.isoengpor
dc.publisherPublic Library of Sciencepor
dc.rightsopenAccesspor
dc.subjectCommunicable Diseasespor
dc.subjectComputer Simulationpor
dc.subjectDisease Outbreakspor
dc.subjectHumanspor
dc.subjectNumerical Analysis, Computer-Assistedpor
dc.subjectPopulation Densitypor
dc.subjectModels, Biologicalpor
dc.subjectSocial Supportpor
dc.titleAdaptive contact networks change effective disease infectiousness and dynamicspor
dc.typearticle-
dc.peerreviewedyespor
dc.relation.publisherversionhttps://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000895por
oaire.citationIssue8por
oaire.citationVolume6por
dc.identifier.doi10.1371/journal.pcbi.1000895por
dc.identifier.pmid20808884por
dc.subject.wosScience & Technologypor
sdum.journalPLoS Computational Biologypor
oaire.versionVoRpor
Appears in Collections:DMA - Artigos (Papers)

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