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dc.contributor.authorNoufaily, A
dc.contributor.authorFarrington, P
dc.contributor.authorGarthwaite, P
dc.contributor.authorEnki, DG
dc.contributor.authorAndrews, N
dc.contributor.authorCharlett, A
dc.date.accessioned2016-05-12T15:21:25Z
dc.date.available2016-05-12T15:21:25Z
dc.date.issued2016-08
dc.identifier.issn0162-1459
dc.identifier.issn1537-274X
dc.identifier.urihttp://hdl.handle.net/10026.1/4622
dc.description.abstract

Many statistical surveillance systems for the timely detection of outbreaks of infectious disease operate on laboratory data. Such data typically incur reporting delays between the time at which a specimen is collected for diagnostic purposes, and the time at which the results of the laboratory analysis become available. Statistical surveillance systems currently in use usually make some ad hoc adjustment for such delays, or use counts by time of report. We propose a new statistical approach that takes account of the delays explicitly, by monitoring the number of specimens identified in the current and past m time units, where m is a tuning parameter. Values expected in the absence of an outbreak are estimated from counts observed in recent years (typically 5 years). We study the method in the context of an outbreak detection system used in the United Kingdom and several other European countries. We propose a suitable test statistic for the null hypothesis that no outbreak is currently occurring. We derive its null variance, incorporating uncertainty about the estimated delay distribution. Simulations and applications to some test datasets suggest the method works well, and can improve performance over ad hoc methods in current use. Supplementary materials for this article are available online.

dc.format.extent488-499
dc.languageen
dc.language.isoen
dc.publisherTaylor & Francis
dc.subject38 Economics
dc.subject4905 Statistics
dc.subject3802 Econometrics
dc.subject49 Mathematical Sciences
dc.subjectInfectious Diseases
dc.subjectPrevention
dc.subjectEmerging Infectious Diseases
dc.subjectVaccine Related
dc.subject4 Detection, screening and diagnosis
dc.subject2 Aetiology
dc.subject4.1 Discovery and preclinical testing of markers and technologies
dc.subject2.5 Research design and methodologies (aetiology)
dc.subjectInfection
dc.subject3 Good Health and Well Being
dc.titleDetection of infectious disease outbreaks from laboratory data with reporting delays
dc.typejournal-article
dc.typeArticle
plymouth.issue514
plymouth.volume111
plymouth.publisher-urlhttp://dx.doi.org/10.1080/01621459.2015.1119047
plymouth.publication-statusPublished
plymouth.journalJournal of the American Statistical Association
dc.identifier.doi10.1080/01621459.2015.1119047
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA03 Allied Health Professions, Dentistry, Nursing and Pharmacy
plymouth.organisational-group/Plymouth/Research Groups
plymouth.organisational-group/Plymouth/Research Groups/Institute of Health and Community
plymouth.organisational-group/Plymouth/Research Groups/Institute of Translational and Stratified Medicine (ITSMED)
plymouth.organisational-group/Plymouth/Research Groups/Institute of Translational and Stratified Medicine (ITSMED)/CBBB
dcterms.dateAccepted2015-10-29
dc.rights.embargodate2017-6
dc.identifier.eissn1537-274X
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1080/01621459.2015.1119047
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2016-08
rioxxterms.typeJournal Article/Review


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