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dc.contributor.authorChan, Emily H.
dc.contributor.authorSahai, Vikram
dc.contributor.authorConrad, Corrie
dc.contributor.authorBrownstein, John Samuel
dc.date.accessioned2011-09-29T15:31:22Z
dc.date.issued2011
dc.identifier.citationChan, Emily H., Vikram Sahai, Corrie Conrad, and John S. Brownstein. 2011. Using web search query data to monitor dengue epidemics: A new model for neglected tropical disease surveillance. PLoS Neglected Tropical Diseases 5(5): e1206.en_US
dc.identifier.issn1935-2727en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:5146970
dc.description.abstractBackground: A variety of obstacles including bureaucracy and lack of resources have interfered with timely detection and reporting of dengue cases in many endemic countries. Surveillance efforts have turned to modern data sources, such as Internet search queries, which have been shown to be effective for monitoring influenza-like illnesses. However, few have evaluated the utility of web search query data for other diseases, especially those of high morbidity and mortality or where a vaccine may not exist. In this study, we aimed to assess whether web search queries are a viable data source for the early detection and monitoring of dengue epidemics. Methodology/Principal Findings: Bolivia, Brazil, India, Indonesia and Singapore were chosen for analysis based on available data and adequate search volume. For each country, a univariate linear model was then built by fitting a time series of the fraction of Google search query volume for specific dengue-related queries from that country against a time series of official dengue case counts for a time-frame within 2003-2010. The specific combination of queries used was chosen to maximize model fit. Spurious spikes in the data were also removed prior to model fitting. The final models, fit using a training subset of the data, were cross-validated against both the overall dataset and a holdout subset of the data. All models were found to fit the data quite well, with validation correlations ranging from 0.82 to 0.99. Conclusions/Significance: Web search query data were found to be capable of tracking dengue activity in Bolivia, Brazil, India, Indonesia and Singapore. Whereas traditional dengue data from official sources are often not available until after some substantial delay, web search query data are available in near real-time. These data represent valuable complement to assist with traditional dengue surveillance.en_US
dc.language.isoen_USen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.isversionofdoi:10.1371/journal.pntd.0001206en_US
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3104029/pdf/en_US
dash.licenseLAA
dc.subjectcomputer scienceen_US
dc.subjectinformation technologyen_US
dc.subjectmedicineen_US
dc.subjectepidemiologyen_US
dc.subjectdisease informaticsen_US
dc.subjectinfectious disease modelingen_US
dc.subjectdengue feveren_US
dc.titleUsing Web Search Query Data to Monitor Dengue Epidemics: A New Model for Neglected Tropical Disease Surveillanceen_US
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden_US
dc.relation.journalPLoS Neglected Tropical Diseasesen_US
dash.depositing.authorBrownstein, John Samuel
dc.date.available2011-09-29T15:31:22Z
dash.affiliation.otherHMS^Pediatrics-Children's Hospitalen_US
dc.identifier.doi10.1371/journal.pntd.0001206*
dash.contributor.affiliatedBrownstein, John


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