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dc.contributor.authorBARBOZA Philippeen_GB
dc.contributor.authorVAILLANT Laetitiaen_GB
dc.contributor.authorHARTLEY Daviden_GB
dc.contributor.authorNELSON Noeleen_GB
dc.contributor.authorMAWUDEKU Ablaen_GB
dc.contributor.authorMADOFF Larryen_GB
dc.contributor.authorLINGE Jensen_GB
dc.contributor.authorCOLLIER Nigelen_GB
dc.contributor.authorBROWNSTEIN Johnen_GB
dc.contributor.authorASTAGNEAU Pascalen_GB
dc.date.accessioned2014-09-25T00:03:46Z-
dc.date.available2014-09-24en_GB
dc.date.available2014-09-25T00:03:46Z-
dc.date.created2014-09-17en_GB
dc.date.issued2014en_GB
dc.date.submitted2014-09-03en_GB
dc.identifier.citationPLOS ONE vol. 9 no. 3 p. e90536en_GB
dc.identifier.issn1932-6203en_GB
dc.identifier.urihttp://www.plosone.org/article/fetchObject.action?uri=info%3Adoi%2F10.1371%2Fjournal.pone.0090536&representation=PDFen_GB
dc.identifier.urihttp://publications.jrc.ec.europa.eu/repository/handle/JRC91599-
dc.description.abstractBackground: Internet-based biosurveillance systems have been developed to detect health threats using information available on the Internet, but system performance has not been assessed relative to end-user needs and perspectives. Method and Findings: Infectious disease events from the French Institute for Public Health Surveillance (InVS) weekly international epidemiological bulletin published in 2010 were used to construct the gold-standard official dataset. Data from six biosurveillance systems were used to detect raw signals (infectious disease events from informal Internet sources): Argus, BioCaster, GPHIN, HealthMap, MedISys and ProMED-mail. Crude detection rates (C-DR), crude sensitivity rates (C-Se) and intrinsic sensitivity rates (I-Se) were calculated from multivariable regressions to evaluate the systems’ performance (events detected compared to the gold-standard) 472 raw signals (Internet disease reports) related to the 86 events included in the gold-standard data set were retrieved from the six systems. 84 events were detected before their publication in the gold-standard. The type of sources utilised by the systems varied significantly (p,0001). I-Se varied significantly from 43% to 71% (p = 0001) whereas other indicators were similar (C-DR: p = 020; C-Se, p = 013). I-Se was significantly associated with individual systems, types of system, languages, regions of occurrence, and types of infectious disease. Conversely, no statistical difference of C-DR was observed after adjustment for other variables. Conclusion: Although differences could result from a biosurveillance system’s conceptual design, findings suggest that the combined expertise amongst systems enhances early detection performance for detection of infectious diseases. While all systems showed similar early detection performance, systems including human moderation were found to have a 53% higher I-Se (p = 00001) after adjustment for other variables. Overall, the use of moderation, sources, languages, regions of occurrence, and types of cases were found to influence system performance.en_GB
dc.description.sponsorshipJRC.G.2-Global security and crisis managementen_GB
dc.format.mediumOnlineen_GB
dc.languageENGen_GB
dc.publisherPUBLIC LIBRARY SCIENCEen_GB
dc.relation.ispartofseriesJRC91599en_GB
dc.titleFactors Influencing Performance of Internet-Based Biosurveillance Systems Used in Epidemic Intelligence for Early Detection of Infectious Diseases Outbreaksen_GB
dc.typeArticles in periodicals and booksen_GB
dc.identifier.doi10.1371/journal.pone.0090536en_GB
JRC Directorate:Space, Security and Migration

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