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Optimizing sentinel surveillance in temporal network epidemiology

dc.contributor.authorBai, Yuan
dc.contributor.authorYang, Bo
dc.contributor.authorLin, Lijuan
dc.contributor.authorHerrera, Jose L. [UNESP]
dc.contributor.authorDu, Zhanwei
dc.contributor.authorHolme, Petter
dc.contributor.institutionJilin Univ
dc.contributor.institutionUniv Texas Austin
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionTokyo Inst Technol
dc.date.accessioned2018-11-26T15:44:38Z
dc.date.available2018-11-26T15:44:38Z
dc.date.issued2017-07-06
dc.description.abstractTo help health policy makers gain response time to mitigate infectious disease threats, it is essential to have an efficient epidemic surveillance. One common method of disease surveillance is to carefully select nodes (sentinels, or sensors) in the network to report outbreaks. One would like to choose sentinels so that they discover the outbreak as early as possible. The optimal choice of sentinels depends on the network structure. Studies have addressed this problem for static networks, but this is a first step study to explore designing surveillance systems for early detection on temporal networks. This paper is based on the idea that vaccination strategies can serve as a method to identify sentinels. The vaccination problem is a related question that is much more well studied for temporal networks. To assess the ability to detect epidemic outbreaks early, we calculate the time difference (lead time) between the surveillance set and whole population in reaching 1% prevalence. We find that the optimal selection of sentinels depends on both the network's temporal structures and the infection probability of the disease. We find that, for a mild infectious disease (low infection probability) on a temporal network in relation to potential disease spreading (the Prostitution network), the strategy of selecting latest contacts of random individuals provide the most amount of lead time. And for a more uniform, synthetic network with community structure the strategy of selecting frequent contacts of random individuals provide the most amount of lead time.en
dc.description.affiliationJilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China
dc.description.affiliationJilin Univ, Minist Educ, Key Lab Symbol Computat & Knowledge Engn, Changchun 130012, Peoples R China
dc.description.affiliationUniv Texas Austin, Dept Integrat Biol, Austin, TX 78705 USA
dc.description.affiliationSao Paulo State Univ, ICTP South Amer Inst Fundamental Res, BR-03001000 Sao Paulo, Brazil
dc.description.affiliationTokyo Inst Technol, Inst Innovat Res, Tokyo 1528550, Japan
dc.description.affiliationUnespSao Paulo State Univ, ICTP South Amer Inst Fundamental Res, BR-03001000 Sao Paulo, Brazil
dc.description.sponsorshipNational Natural Science Foundation of China
dc.description.sponsorshipIdNational Natural Science Foundation of China: 61572226
dc.description.sponsorshipIdNational Natural Science Foundation of China: 61373053
dc.format.extent10
dc.identifierhttp://dx.doi.org/10.1038/s41598-017-03868-6
dc.identifier.citationScientific Reports. London: Nature Publishing Group, v. 7, 10 p., 2017.
dc.identifier.doi10.1038/s41598-017-03868-6
dc.identifier.fileWOS000404841100060.pdf
dc.identifier.issn2045-2322
dc.identifier.urihttp://hdl.handle.net/11449/159605
dc.identifier.wosWOS:000404841100060
dc.language.isoeng
dc.publisherNature Publishing Group
dc.relation.ispartofScientific Reports
dc.relation.ispartofsjr1,533
dc.rights.accessRightsAcesso aberto
dc.sourceWeb of Science
dc.titleOptimizing sentinel surveillance in temporal network epidemiologyen
dc.typeArtigo
dcterms.rightsHolderNature Publishing Group
unesp.author.orcid0000-0003-2156-1096[6]

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