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dc.contributor.authorHerrera, Jose L.en_US
dc.contributor.authorSrinivasan, Ravien_US
dc.contributor.authorBrownstein, John S.en_US
dc.contributor.authorGalvani, Alison P.en_US
dc.contributor.authorMeyers, Lauren Ancelen_US
dc.date.accessioned2016-10-11T20:25:51Z
dc.date.issued2016en_US
dc.identifier.citationHerrera, Jose L., Ravi Srinivasan, John S. Brownstein, Alison P. Galvani, and Lauren Ancel Meyers. 2016. “Disease Surveillance on Complex Social Networks.” PLoS Computational Biology 12 (7): e1004928. doi:10.1371/journal.pcbi.1004928. http://dx.doi.org/10.1371/journal.pcbi.1004928.en
dc.identifier.issn1553-734Xen
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:29002416
dc.description.abstractAs infectious disease surveillance systems expand to include digital, crowd-sourced, and social network data, public health agencies are gaining unprecedented access to high-resolution data and have an opportunity to selectively monitor informative individuals. Contact networks, which are the webs of interaction through which diseases spread, determine whether and when individuals become infected, and thus who might serve as early and accurate surveillance sensors. Here, we evaluate three strategies for selecting sensors—sampling the most connected, random, and friends of random individuals—in three complex social networks—a simple scale-free network, an empirical Venezuelan college student network, and an empirical Montreal wireless hotspot usage network. Across five different surveillance goals—early and accurate detection of epidemic emergence and peak, and general situational awareness—we find that the optimal choice of sensors depends on the public health goal, the underlying network and the reproduction number of the disease (R0). For diseases with a low R0, the most connected individuals provide the earliest and most accurate information about both the onset and peak of an outbreak. However, identifying network hubs is often impractical, and they can be misleading if monitored for general situational awareness, if the underlying network has significant community structure, or if R0 is high or unknown. Taking a theoretical approach, we also derive the optimal surveillance system for early outbreak detection but find that real-world identification of such sensors would be nearly impossible. By contrast, the friends-of-random strategy offers a more practical and robust alternative. It can be readily implemented without prior knowledge of the network, and by identifying sensors with higher than average, but not the highest, epidemiological risk, it provides reasonably early and accurate information.en
dc.language.isoen_USen
dc.publisherPublic Library of Scienceen
dc.relation.isversionofdoi:10.1371/journal.pcbi.1004928en
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC4944951/pdf/en
dash.licenseLAAen_US
dc.subjectComputer and Information Sciencesen
dc.subjectNetwork Analysisen
dc.subjectCentralityen
dc.subjectMedicine and Health Sciencesen
dc.subjectEpidemiologyen
dc.subjectInfectious Disease Epidemiologyen
dc.subjectInfectious Diseasesen
dc.subjectDisease Surveillanceen
dc.subjectInfectious Disease Surveillanceen
dc.subjectInfectious Disease Controlen
dc.subjectScale-Free Networksen
dc.subjectPhysical Sciencesen
dc.subjectMathematicsen
dc.subjectAlgebraen
dc.subjectLinear Algebraen
dc.subjectEigenvectorsen
dc.subjectSocial Networksen
dc.subjectSocial Sciencesen
dc.subjectSociologyen
dc.subjectMathematical and Statistical Techniquesen
dc.subjectMathematical Modelsen
dc.subjectRandom Walken
dc.titleDisease Surveillance on Complex Social Networksen
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden
dc.relation.journalPLoS Computational Biologyen
dash.depositing.authorBrownstein, John S.en_US
dc.date.available2016-10-11T20:25:51Z
dc.identifier.doi10.1371/journal.pcbi.1004928*
dash.contributor.affiliatedBrownstein, John


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