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Detection of Infectious Disease Outbreaks From Laboratory Data With Reporting Delays

Noufaily, Angela; Farrington, Paddy; Garthwaite, Paul; Enki, Doyo Gragn; Andrews, Nick and Charlett, Andre (2016). Detection of Infectious Disease Outbreaks From Laboratory Data With Reporting Delays. Journal of the American Statistical Association, 111(514) pp. 488–499.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1080/01621459.2015.1119047
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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.

Item Type: Journal Item
Copyright Holders: 2016 American Statistical Association
ISSN: 1537-274X
Keywords: aberrance; epidemic; exceedance; infection; Quasi-Poisson; surveillance
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 60683
Depositing User: ORO Import
Date Deposited: 30 Apr 2019 10:20
Last Modified: 15 Jun 2019 17:05
URI: http://oro.open.ac.uk/id/eprint/60683
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