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 . doi:10.1093/bioinformatics/bty997 (In Press)

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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
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
Divisions:	Faculty of Medicine > Warwick Medical School > Health Sciences > Population, Evidence & Technologies (PET) Faculty of Medicine > Warwick Medical School > Health Sciences > Statistics and Epidemiology Faculty of Medicine > Warwick Medical School
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:	25 January 2019
Dates:	Date Event 25 January 2019 Published 22 January 2019 Accepted
Date of first compliant deposit:	20 December 2018
DOI:	10.1093/bioinformatics/bty997
Status:	Peer Reviewed
Publication Status:	In Press
Access rights to Published version:	Open Access
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID UNSPECIFIED [NIHR] National Institute for Health Research http://dx.doi.org/10.13039/501100000272 UNSPECIFIED Public Health England http://dx.doi.org/10.13039/501100002141 HPRU-2012-10038 Quadram Institute Bioscience http://dx.doi.org/10.13039/501100009314 HPRU-2012-10141 [NIHR] National Institute for Health Research http://dx.doi.org/10.13039/501100000272
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