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Article: An Early Warning System for Detecting H1N1 Disease Outbreak - A Spatio-temporal Approach

TitleAn Early Warning System for Detecting H1N1 Disease Outbreak - A Spatio-temporal Approach
Authors
KeywordsDisease modeling
Infectious disease
H1N1
Early warning
Hong Kong
Issue Date2015
PublisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/13658816.asp
Citation
International Journal of Geographical Information Science, 2015, v. 29 n. 7, p. 1251-1268 How to Cite?
AbstractThe outbreaks of new and emerging infectious diseases in recent decades have caused widespread social and economic disruptions in the global economy. Various guidelines for pandemic influenza planning are based upon traditional infection control, best practice and evidence. This article describes the development of an early warning system for detecting disease outbreaks in the urban setting of Hong Kong, using 216 confirmed cases of H1N1 influenza from 1 May 2009 to 20 June 2009. The prediction model uses two variables – daily influenza cases and population numbers – as input to the spatio-temporal and stochastic SEIR model to forecast impending disease cases. The fairly encouraging forecast accuracy metrics for the 1- and 2-day advance prediction suggest that the number of impending cases could be estimated with some degree of certainty. Much like a weather forecast system, the procedure combines technical and scientific skills using empirical data but the interpretation requires experience and intuitive reasoning.
Persistent Identifierhttp://hdl.handle.net/10722/201032
ISSN
2015 Impact Factor: 2.065
2015 SCImago Journal Rankings: 1.156

 

DC FieldValueLanguage
dc.contributor.authorLai, PC-
dc.contributor.authorChow, CB-
dc.contributor.authorWong, HT-
dc.contributor.authorKwong, KH-
dc.contributor.authorKwan, YW-
dc.contributor.authorLiu, SH-
dc.contributor.authorTong, WK-
dc.contributor.authorCheung, WK-
dc.contributor.authorWong, WL-
dc.date.accessioned2014-08-21T07:10:33Z-
dc.date.available2014-08-21T07:10:33Z-
dc.date.issued2015-
dc.identifier.citationInternational Journal of Geographical Information Science, 2015, v. 29 n. 7, p. 1251-1268-
dc.identifier.issn1365-8816-
dc.identifier.urihttp://hdl.handle.net/10722/201032-
dc.description.abstractThe outbreaks of new and emerging infectious diseases in recent decades have caused widespread social and economic disruptions in the global economy. Various guidelines for pandemic influenza planning are based upon traditional infection control, best practice and evidence. This article describes the development of an early warning system for detecting disease outbreaks in the urban setting of Hong Kong, using 216 confirmed cases of H1N1 influenza from 1 May 2009 to 20 June 2009. The prediction model uses two variables – daily influenza cases and population numbers – as input to the spatio-temporal and stochastic SEIR model to forecast impending disease cases. The fairly encouraging forecast accuracy metrics for the 1- and 2-day advance prediction suggest that the number of impending cases could be estimated with some degree of certainty. Much like a weather forecast system, the procedure combines technical and scientific skills using empirical data but the interpretation requires experience and intuitive reasoning.-
dc.languageeng-
dc.publisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/13658816.asp-
dc.relation.ispartofInternational Journal of Geographical Information Science-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsThis is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Geographical Information Science on 20 May 2015, available online: http://www.tandfonline.com/doi/full/10.1080/13658816.2015.1030671-
dc.subjectDisease modeling-
dc.subjectInfectious disease-
dc.subjectH1N1-
dc.subjectEarly warning-
dc.subjectHong Kong-
dc.titleAn Early Warning System for Detecting H1N1 Disease Outbreak - A Spatio-temporal Approach-
dc.typeArticle-
dc.identifier.emailLai, PC: pclai@hku.hk-
dc.identifier.emailChow, CB: chowcb@hku.hk-
dc.identifier.emailWong, HT: fhtwong@hku.hk-
dc.identifier.emailKwong, KH: h0110454@hkusua.hku.hk-
dc.identifier.emailCheung, WK: alessi@hku.hk-
dc.identifier.authorityLai, PC=rp00565-
dc.identifier.authorityCheung, WK=rp01590-
dc.description.naturepostprint-
dc.identifier.doi10.1080/13658816.2015.1030671-
dc.identifier.hkuros234280-
dc.identifier.hkuros244747-
dc.identifier.volume29-
dc.identifier.issue7-
dc.identifier.spage1251-
dc.identifier.epage1268-
dc.publisher.placeUnited Kingdom-

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