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dc.contributor.authorOren, Eyal*
dc.contributor.authorFrere, Justin*
dc.contributor.authorYom-Tov, Eran*
dc.contributor.authorYom-Tov, Elad*
dc.date.accessioned2018-05-16T16:54:21Z
dc.date.available2018-05-16T16:54:21Z
dc.date.issued2018-04-03
dc.identifier.citationOren et al. BMC Public Health (2018) 18:445 https://doi.org/10.1186/s12889-018-5367-zen_US
dc.identifier.issn1471-2458
dc.identifier.pmid29615018
dc.identifier.doi10.1186/s12889-018-5367-z
dc.identifier.urihttp://hdl.handle.net/10150/627639
dc.description.abstractBackground: Respiratory Syncytial Virus (RSV) is the leading cause of hospitalization in children less than 1 year of age in the United States. Internet search engine queries may provide high resolution temporal and spatial data to estimate and predict disease activity. Methods: After filtering an initial list of 613 symptoms using high-resolution Bing search logs, we used Google Trends data between 2004 and 2016 for a smaller list of 50 terms to build predictive models of RSV incidence for five states where long-term surveillance data was available. We then used domain adaptation to model RSV incidence for the 45 remaining US states. Results: Surveillance data sources (hospitalization and laboratory reports) were highly correlated, as were laboratory reports with search engine data. The four terms which were most often statistically significantly correlated as time series with the surveillance data in the five state models were RSV, flu, pneumonia, and bronchiolitis. Using our models, we tracked the spread of RSV by observing the time of peak use of the search term in different states. In general, the RSV peak moved from south-east (Florida) to the north-west US. Conclusions: Our study represents the first time that RSV has been tracked using Internet data results and highlights successful use of search filters and domain adaptation techniques, using data at multiple resolutions. Our approach may assist in identifying spread of both local and more widespread RSV transmission and may be applicable to other seasonal conditions where comprehensive epidemiological data is difficult to collect or obtain.en_US
dc.language.isoenen_US
dc.publisherBIOMED CENTRAL LTDen_US
dc.relation.urlhttps://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-018-5367-zen_US
dc.rights© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License.en_US
dc.subjectRSVen_US
dc.subjectInternet dataen_US
dc.subjectGoogle trendsen_US
dc.subjectDomain adaptationen_US
dc.titleRespiratory syncytial virus tracking using internet search engine dataen_US
dc.typeArticleen_US
dc.contributor.departmentUniv Arizona, Coll Publ Hlth, Div Epidemiol & Biostat, Tucson, AZ 85721 USAen_US
dc.identifier.journalBMC PUBLIC HEALTHen_US
dc.description.noteOpen access journal.en_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal published versionen_US
dc.source.journaltitleBMC Public Health
dc.source.volume18
dc.source.issue1
refterms.dateFOA2018-05-16T16:54:22Z


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