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Article: Online detection and quantification of epidemics

TitleOnline detection and quantification of epidemics
Authors
KeywordsReferences (33) View In Table Layout
Issue Date2007
PublisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmcmedinformdecismak/
Citation
Bmc Medical Informatics And Decision Making, 2007, v. 7 How to Cite?
AbstractBackground. Time series data are increasingly available in health care, especially for the purpose of disease surveillance. The analysis of such data has long used periodic regression models to detect outbreaks and estimate epidemic burdens. However, implementation of the method may be difficult due to lack of statistical expertise. No dedicated tool is available to perform and guide analyses. Results. We developed an online computer application allowing analysis of epidemiologic time series. The system is available online at http://www.u707.jussieu.fr/periodic_regression/. The data is assumed to consist of a periodic baseline level and irregularly occurring epidemics. The program allows estimating the periodic baseline level and associated upper forecast limit. The latter defines a threshold for epidemic detection. The burden of an epidemic is defined as the cumulated signal in excess of the baseline estimate. The user is guided through the necessary choices for analysis. We illustrate the usage of the online epidemic analysis tool with two examples: the retrospective detection and quantification of excess pneumonia and influenza (P&I) mortality, and the prospective surveillance of gastrointestinal disease (diarrhoea). Conclusion. The online application allows easy detection of special events in an epidemiologic time series and quantification of excess mortality/morbidity as a change from baseline. It should be a valuable tool for field and public health practitioners. © 2007 Pelat et al.; licensee BioMed Central Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/92586
ISSN
2015 Impact Factor: 2.042
2015 SCImago Journal Rankings: 1.100
PubMed Central ID
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorPelat, Cen_HK
dc.contributor.authorBoëlle, PYen_HK
dc.contributor.authorCowling, BJen_HK
dc.contributor.authorCarrat, Fen_HK
dc.contributor.authorFlahault, Aen_HK
dc.contributor.authorAnsart, Sen_HK
dc.contributor.authorValleron, AJen_HK
dc.date.accessioned2010-09-17T10:50:53Z-
dc.date.available2010-09-17T10:50:53Z-
dc.date.issued2007en_HK
dc.identifier.citationBmc Medical Informatics And Decision Making, 2007, v. 7en_HK
dc.identifier.issn1472-6947en_HK
dc.identifier.urihttp://hdl.handle.net/10722/92586-
dc.description.abstractBackground. Time series data are increasingly available in health care, especially for the purpose of disease surveillance. The analysis of such data has long used periodic regression models to detect outbreaks and estimate epidemic burdens. However, implementation of the method may be difficult due to lack of statistical expertise. No dedicated tool is available to perform and guide analyses. Results. We developed an online computer application allowing analysis of epidemiologic time series. The system is available online at http://www.u707.jussieu.fr/periodic_regression/. The data is assumed to consist of a periodic baseline level and irregularly occurring epidemics. The program allows estimating the periodic baseline level and associated upper forecast limit. The latter defines a threshold for epidemic detection. The burden of an epidemic is defined as the cumulated signal in excess of the baseline estimate. The user is guided through the necessary choices for analysis. We illustrate the usage of the online epidemic analysis tool with two examples: the retrospective detection and quantification of excess pneumonia and influenza (P&I) mortality, and the prospective surveillance of gastrointestinal disease (diarrhoea). Conclusion. The online application allows easy detection of special events in an epidemiologic time series and quantification of excess mortality/morbidity as a change from baseline. It should be a valuable tool for field and public health practitioners. © 2007 Pelat et al.; licensee BioMed Central Ltd.en_HK
dc.languageengen_HK
dc.publisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmcmedinformdecismak/en_HK
dc.relation.ispartofBMC Medical Informatics and Decision Makingen_HK
dc.subjectReferences (33) View In Table Layouten_HK
dc.titleOnline detection and quantification of epidemicsen_HK
dc.typeArticleen_HK
dc.identifier.emailCowling, BJ:bcowling@hku.hken_HK
dc.identifier.authorityCowling, BJ=rp01326en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1186/1472-6947-7-29en_HK
dc.identifier.pmid17937786-
dc.identifier.pmcidPMC2151935-
dc.identifier.scopuseid_2-s2.0-37549041366en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-37549041366&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume7en_HK
dc.identifier.isiWOS:000252408100001-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridPelat, C=15132786800en_HK
dc.identifier.scopusauthoridBoëlle, PY=7003593801en_HK
dc.identifier.scopusauthoridCowling, BJ=8644765500en_HK
dc.identifier.scopusauthoridCarrat, F=7003977391en_HK
dc.identifier.scopusauthoridFlahault, A=7005138560en_HK
dc.identifier.scopusauthoridAnsart, S=6602115945en_HK
dc.identifier.scopusauthoridValleron, AJ=7004672683en_HK
dc.identifier.citeulike1773203-

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