Epidemic Spreading on Preferred Degree Adaptive Networks

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dc.contributor.author Jolad, Shivakumar
dc.contributor.author Liu, Wenjia
dc.contributor.author Zia, R. K. P.
dc.contributor.author Schmittmann, Beate
dc.date.accessioned 2014-03-16T12:18:15Z
dc.date.available 2014-03-16T12:18:15Z
dc.date.issued 2012-11
dc.identifier.citation Jolad, Shivakumar et al., “Epidemic spreading on preferred degree adaptive Networks”, PLoS ONE, DOI: 10.1371/journal.pone.0048686, vol. 7, no. 11, p. e48686, Nov. 2012. en_US
dc.identifier.issn 1932-6203
dc.identifier.uri http://dx.doi.org/10.1371/journal.pone.0048686
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/785
dc.description.abstract We study the standard SIS model of epidemic spreading on networks where individuals have a fluctuating number of connections around a preferred degree . Using very simple rules for forming such preferred degree networks, we find some unusual statistical properties not found in familiar Erdös-Rényi or scale free networks. By letting depend on the fraction of infected individuals, we model the behavioral changes in response to how the extent of the epidemic is perceived. In our models, the behavioral adaptations can be either ‘blind’ or ‘selective’ – depending on whether a node adapts by cutting or adding links to randomly chosen partners or selectively, based on the state of the partner. For a frozen preferred network, we find that the infection threshold follows the heterogeneous mean field result and the phase diagram matches the predictions of the annealed adjacency matrix (AAM) approach. With ‘blind’ adaptations, although the epidemic threshold remains unchanged, the infection level is substantially affected, depending on the details of the adaptation. The ‘selective’ adaptive SIS models are most interesting. Both the threshold and the level of infection changes, controlled not only by how the adaptations are implemented but also how often the nodes cut/add links (compared to the time scales of the epidemic spreading). A simple mean field theory is presented for the selective adaptations which capture the qualitative and some of the quantitative features of the infection phase diagram. en_US
dc.description.statementofresponsibility by Shivakumar Jolad et al.,
dc.format.extent vol. 7, no. 11, pp. e48686
dc.language.iso en en_US
dc.publisher Public Library of Science en_US
dc.subject Algorithms en_US
dc.subject Computer Simulation en_US
dc.subject Epidemics en_US
dc.subject Models en_US
dc.subject Theoretical en_US
dc.title Epidemic Spreading on Preferred Degree Adaptive Networks en_US
dc.type Article en_US
dc.relation.journal PLoS ONE

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