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dc.creatorScarpino, Samuel V.en
dc.creatorDimitrov, Nedialko B.en
dc.creatorMeyers, Lauren Ancelen
dc.date.accessioned2013-06-26T16:33:09Zen
dc.date.available2013-06-26T16:33:09Zen
dc.date.issued2012-04-12en
dc.identifier.citationScarpino SV, Dimitrov NB, Meyers LA (2012) Optimizing Provider Recruitment for Influenza Surveillance Networks. PLoS Comput Biol 8(4): e1002472. doi:10.1371/journal.pcbi.1002472en
dc.identifier.urihttp://hdl.handle.net/2152/20417en
dc.descriptionSamuel V. Scarpino is with UT Austin, Nedialko B. Dimitrov is with the Naval Postgraduate School, Lauren Ancel Meyers is with UT Austin and the Santa Fe Institute.en
dc.description.abstractThe increasingly complex and rapid transmission dynamics of many infectious diseases necessitates the use of new, more advanced methods for surveillance, early detection, and decision-making. Here, we demonstrate that a new method for optimizing surveillance networks can improve the quality of epidemiological information produced by typical provider-based networks. Using past surveillance and Internet search data, it determines the precise locations where providers should be enrolled. When applied to redesigning the provider-based, influenza-like-illness surveillance network (ILINet) for the state of Texas, the method identifies networks that are expected to significantly outperform the existing network with far fewer providers. This optimized network avoids informational redundancies and is thereby more effective than networks designed by conventional methods and a recently published algorithm based on maximizing population coverage. We show further that Google Flu Trends data, when incorporated into a network as a virtual provider, can enhance but not replace traditional surveillance methods.en
dc.description.sponsorshipThis study was supported by the National Science Foundation DEB-0749097 to LAM and graduate research fellowship to SVS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.en
dc.language.isoengen
dc.publisherPublic Library of Scienceen
dc.rightsAttribution 3.0 United Statesen
dc.rightsCC-BYen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/en
dc.subjectGeographyen
dc.subjectHospitalizationsen
dc.subjectInfluenzaen
dc.subjectInterneten
dc.subjectNatural history of diseaseen
dc.subjectOptimizationen
dc.subjectPublic and occupational healthen
dc.subjectTexasen
dc.titleOptimizing Provider Recruitment for Influenza Surveillance Networksen
dc.typeArticleen
dc.description.departmentBiological Sciences, School ofen
dc.identifier.doi10.1371/journal.pcbi.1002472en


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Attribution 3.0 United States
Except where otherwise noted, this item's license is described as Attribution 3.0 United States