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Networked SIS Epidemics With Awareness

dc.contributor.authorPaarporn, Keith
dc.contributor.authorEksin, Ceyhun
dc.contributor.authorWeitz, Joshua S.
dc.contributor.authorShamma, Jeff S.
dc.date.accessioned2018-01-01T12:19:03Z
dc.date.available2018-01-01T12:19:03Z
dc.date.issued2017-07-20
dc.identifier.citationPaarporn K, Eksin C, Weitz JS, Shamma JS (2017) Networked SIS Epidemics With Awareness. IEEE Transactions on Computational Social Systems 4: 93–103. Available: http://dx.doi.org/10.1109/tcss.2017.2719585.
dc.identifier.issn2329-924X
dc.identifier.doi10.1109/tcss.2017.2719585
dc.identifier.urihttp://hdl.handle.net/10754/626611
dc.description.abstractWe study a susceptible-infected-susceptible epidemic process over a static contact network where the nodes have partial information about the epidemic state. They react by limiting their interactions with their neighbors when they believe the epidemic is currently prevalent. A node's awareness is weighted by the fraction of infected neighbors in their social network, and a global broadcast of the fraction of infected nodes in the entire network. The dynamics of the benchmark (no awareness) and awareness models are described by discrete-time Markov chains, from which mean-field approximations (MFAs) are derived. The states of the MFA are interpreted as the nodes' probabilities of being infected. We show a sufficient condition for the existence of a
dc.description.sponsorshipThis work was supported in part by the Army Research Office under Grant W911NF-14-1-0402, and in part by the King Abdullah University of Science and Technology.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/document/7987055/
dc.subjectBenchmark testing
dc.subjectCouplings
dc.subjectDiseases
dc.subjectEpidemics
dc.subjectMarkov processes
dc.subjectMarkov processes
dc.subjectnetworks
dc.subjectSilicon
dc.subjectSocial network services
dc.subjectstochastic processes
dc.titleNetworked SIS Epidemics With Awareness
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentRISC Laboratory
dc.identifier.journalIEEE Transactions on Computational Social Systems
dc.contributor.institutionSchool of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA.
dc.contributor.institutionSchool of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332 USA.
dc.contributor.institutionSchool of Physics, Georgia Institute of Technology, Atlanta, GA 30332 USA.
kaust.personShamma, Jeff S.
dc.date.published-online2017-07-20
dc.date.published-print2017-09


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