Spatial Big Data Analytics of Influenza Epidemic in Vellore, India

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dc.contributor.authorLopez, Daphneen
dc.contributor.authorGunasekaran, M.en
dc.contributor.authorMurugan, B. Senthilen
dc.contributor.authorKaur, Harpreeten
dc.contributor.authorAbbas, Kaja M.en
dc.date.accessioned2017-04-14T02:00:33Zen
dc.date.available2017-04-14T02:00:33Zen
dc.date.issued2014-01-01en
dc.description.abstractThe study objective is to develop a big spatial data model to predict the epidemiological impact of influenza in Vellore, India. Large repositories of geospatial and health data provide vital statistics on surveillance and epidemiological metrics, and valuable insight into the spatiotemporal determinants of disease and health. The integration of these big data sources and analytics to assess risk factors and geospatial vulnerability can assist to develop effective prevention and control strategies for influenza epidemics and optimize allocation of limited public health resources. We used the spatial epidemiology data of the HIN1 epidemic collected at the National Informatics Center during 2009-2010 in Vellore. We developed an ecological niche model based on geographically weighted regression for predicting influenza epidemics in Vellore, India during 2013-2014. Data on rainfall, temperature, wind speed, humidity and population are included in the geographically weighted regression analysis. We inferred positive correlations for H1N1 influenza prevalence with rainfall and wind speed, and negative correlations for H1N1 influenza prevalence with temperature and humidity. We evaluated the results of the geographically weighted regression model in predicting the spatial distribution of the influenza epidemic during 2013-2014.en
dc.description.versionPublished versionen
dc.format.extent6 pagesen
dc.format.mimetypeapplication/pdfen
dc.identifier.orcidAbbas, KM [0000-0003-0563-1576]en
dc.identifier.urihttp://hdl.handle.net/10919/77409en
dc.language.isoenen
dc.publisherIEEEen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000380462900219&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectTechnologyen
dc.subjectComputer Science, Information Systemsen
dc.subjectEngineering, Electrical & Electronicen
dc.subjectComputer Scienceen
dc.subjectEngineeringen
dc.subjectdisease forecastingen
dc.subjectecological niche modelen
dc.subjectepidemiologyen
dc.subjectgeographically weighted regressionen
dc.subjectH1N1 influenzaen
dc.subjectMODELSen
dc.subjectREGRESSIONen
dc.subjectTESTSen
dc.titleSpatial Big Data Analytics of Influenza Epidemic in Vellore, Indiaen
dc.title.serial2014 IEEE International Conference On Big Dataen
dc.typeConference proceedingen
dc.type.dcmitypeTexten
dc.type.otherProceedings Paperen
dc.type.otherMeetingen
dc.type.otherBooken
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Faculty of Health Sciencesen
pubs.organisational-group/Virginia Tech/Veterinary Medicineen
pubs.organisational-group/Virginia Tech/Veterinary Medicine/CVM T&R Facultyen
pubs.organisational-group/Virginia Tech/Veterinary Medicine/Population Health Sciencesen

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