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Comparison of statistical algorithms for daily syndromic surveillance aberration detection
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Noufaily, Angela, Morbey, Roger A., Colón-González, Felipe J., Elliot, Alex J., Smith, Gillian E., Lake, Iain R. and McCarthy, Noel D. (2019) Comparison of statistical algorithms for daily syndromic surveillance aberration detection. Bioinformatics, 35 (17). pp. 3110-3118. doi:10.1093/bioinformatics/bty997 ISSN 1367-4803.
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Official URL: https://doi.org/10.1093/bioinformatics/bty997
Abstract
Public health authorities can provide more effective and timely interventions to protect populations during health events if they have effective multi-purpose surveillance systems. These systems rely on aberration detection algorithms to identify potential threats within large datasets. Ensuring the algorithms are sensitive, specific and timely is crucial for protecting public health. Here, we evaluate the performance of three detection algorithms extensively used for syndromic surveillance: the ‘rising activity, multilevel mixed effects, indicator emphasis’ (RAMMIE) method and the improved quasi-Poisson regression-based method known as ‘Farrington Flexible’ both currently used at Public Health England, and the ‘Early Aberration Reporting System’ (EARS) method used at the US Centre for Disease Control and Prevention. We model the wide range of data structures encountered within the daily syndromic surveillance systems used by PHE. We undertake extensive simulations to identify which algorithms work best across different types of syndromes and different outbreak sizes. We evaluate RAMMIE for the first time since its introduction. Performance metrics were computed and compared in the presence of a range of simulated outbreak types that were added to baseline data.
Item Type: | Journal Article | |||||||||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine |
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Divisions: | Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Health Sciences > Population, Evidence & Technologies (PET) Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Health Sciences > Statistics and Epidemiology Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School |
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Library of Congress Subject Headings (LCSH): | Human beings -- Diseases -- Control -- Statistical methods, Algorithms | |||||||||||||||
Journal or Publication Title: | Bioinformatics | |||||||||||||||
Publisher: | Oxford University Press | |||||||||||||||
ISSN: | 1367-4803 | |||||||||||||||
Official Date: | September 2019 | |||||||||||||||
Dates: |
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Volume: | 35 | |||||||||||||||
Number: | 17 | |||||||||||||||
Page Range: | pp. 3110-3118 | |||||||||||||||
DOI: | 10.1093/bioinformatics/bty997 | |||||||||||||||
Status: | Peer Reviewed | |||||||||||||||
Publication Status: | Published | |||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||
Date of first compliant deposit: | 20 December 2018 | |||||||||||||||
Date of first compliant Open Access: | 10 April 2019 | |||||||||||||||
RIOXX Funder/Project Grant: |
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