File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Methods for monitoring influenza surveillance data

TitleMethods for monitoring influenza surveillance data
Authors
KeywordsDetection
Influenza
Population surveillance
Public health
Issue Date2006
PublisherOxford University Press. The Journal's web site is located at http://ije.oxfordjournals.org/
Citation
International Journal Of Epidemiology, 2006, v. 35 n. 5, p. 1314-1321 How to Cite?
AbstractBackground: A variety of Serfling-type statistical algorithms requiring long series of historical data, exclusively from temperate climate zones, have been proposed for automated monitoring of influenza sentinel surveillance data. We evaluated three alternative statistical approaches where alert thresholds are based on recent data in both temperate and subtropical regions. Methods: We compared time series, regression, and cumulative sum (CUSUM) models on empirical data from Hong Kong and the US using a composite index (range = 0-1) consisting of the key outcomes of sensitivity, specificity, and time to detection (lag). The index was calculated based on alarms generated within the first 2 or 4 weeks of the peak season. Results: We found that the time series model was optimal in the Hong Kong setting, while both the time series and CUSUM models worked equally well on US data. For alarms generated within the first 2 weeks (4 weeks) of the peak season in Hong Kong, the maximum values of the index were: time series 0.77 (0.86); regression 0.75 (0.82); CUSUM 0.56 (0.75). In the US data the maximum values of the index were: time series 0.81 (0.95); regression 0.81 (0.91); CUSUM 0.90 (0.94). Conclusions: Automated influenza surveillance methods based on short-term data, including time series and CUSUM models, can generate sensitive, specific, and timely alerts, and can offer a useful alternative to Serfling-like methods that rely on long-term, historically based thresholds. © Copyright 2006 Oxford University Press.
Persistent Identifierhttp://hdl.handle.net/10722/54272
ISSN
2015 Impact Factor: 7.522
2015 SCImago Journal Rankings: 4.381
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorCowling, BJen_HK
dc.contributor.authorWong, IOLen_HK
dc.contributor.authorHo, LMen_HK
dc.contributor.authorRiley, Sen_HK
dc.contributor.authorLeung, GMen_HK
dc.date.accessioned2009-04-03T07:41:46Z-
dc.date.available2009-04-03T07:41:46Z-
dc.date.issued2006en_HK
dc.identifier.citationInternational Journal Of Epidemiology, 2006, v. 35 n. 5, p. 1314-1321en_HK
dc.identifier.issn0300-5771en_HK
dc.identifier.urihttp://hdl.handle.net/10722/54272-
dc.description.abstractBackground: A variety of Serfling-type statistical algorithms requiring long series of historical data, exclusively from temperate climate zones, have been proposed for automated monitoring of influenza sentinel surveillance data. We evaluated three alternative statistical approaches where alert thresholds are based on recent data in both temperate and subtropical regions. Methods: We compared time series, regression, and cumulative sum (CUSUM) models on empirical data from Hong Kong and the US using a composite index (range = 0-1) consisting of the key outcomes of sensitivity, specificity, and time to detection (lag). The index was calculated based on alarms generated within the first 2 or 4 weeks of the peak season. Results: We found that the time series model was optimal in the Hong Kong setting, while both the time series and CUSUM models worked equally well on US data. For alarms generated within the first 2 weeks (4 weeks) of the peak season in Hong Kong, the maximum values of the index were: time series 0.77 (0.86); regression 0.75 (0.82); CUSUM 0.56 (0.75). In the US data the maximum values of the index were: time series 0.81 (0.95); regression 0.81 (0.91); CUSUM 0.90 (0.94). Conclusions: Automated influenza surveillance methods based on short-term data, including time series and CUSUM models, can generate sensitive, specific, and timely alerts, and can offer a useful alternative to Serfling-like methods that rely on long-term, historically based thresholds. © Copyright 2006 Oxford University Press.en_HK
dc.languageengen_HK
dc.publisherOxford University Press. The Journal's web site is located at http://ije.oxfordjournals.org/en_HK
dc.relation.ispartofInternational Journal of Epidemiologyen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsInternational Journal of Epidemiology. Copyright © Oxford University Press.en_HK
dc.rightsThis is a pre-copy-editing, author-produced PDF of an article accepted for publication in International Journal of Epidemiology following peer review. The definitive publisher-authenticated version [insert complete citation information here] is available online at: http://ije.oxfordjournals.org/en_HK
dc.subjectDetectionen_HK
dc.subjectInfluenzaen_HK
dc.subjectPopulation surveillanceen_HK
dc.subjectPublic healthen_HK
dc.subject.meshSentinel Surveillanceen_HK
dc.subject.meshAlgorithmsen_HK
dc.subject.meshDisease Outbreaksen_HK
dc.subject.meshInfluenza, Human - diagnosis - epidemiologyen_HK
dc.subject.meshPublic Healthen_HK
dc.titleMethods for monitoring influenza surveillance dataen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0300-5771&volume=35&issue=5&spage=1314&epage=1321&date=2006&atitle=Methods+for+monitoring+influenza+surveillance+dataen_HK
dc.identifier.emailCowling, BJ: bcowling@hku.hken_HK
dc.identifier.emailWong, IOL: iolwong@hku.hken_HK
dc.identifier.emailHo, LM: lmho@hkucc.hku.hken_HK
dc.identifier.emailRiley, S: sriley@hkucc.hku.hken_HK
dc.identifier.emailLeung, GM: gmleung@hkucc.hku.hken_HK
dc.identifier.authorityCowling, BJ=rp01326en_HK
dc.identifier.authorityWong, IOL=rp01806en_HK
dc.identifier.authorityHo, LM=rp00360en_HK
dc.identifier.authorityRiley, S=rp00511en_HK
dc.identifier.authorityLeung, GM=rp00460en_HK
dc.description.naturepostprinten_HK
dc.identifier.doi10.1093/ije/dyl162en_HK
dc.identifier.pmid16926216-
dc.identifier.scopuseid_2-s2.0-33750223871en_HK
dc.identifier.hkuros125555-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33750223871&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume35en_HK
dc.identifier.issue5en_HK
dc.identifier.spage1314en_HK
dc.identifier.epage1321en_HK
dc.identifier.isiWOS:000241429200039-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridCowling, BJ=8644765500en_HK
dc.identifier.scopusauthoridWong, IOL=7102513940en_HK
dc.identifier.scopusauthoridHo, LM=7402955625en_HK
dc.identifier.scopusauthoridRiley, S=7102619416en_HK
dc.identifier.scopusauthoridLeung, GM=7007159841en_HK
dc.identifier.citeulike910768-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats