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An improved algorithm for outbreak detection in multiple surveillance systems

Noufaily, Angela; Enki, Doyo G.; Farrington, Paddy; Garthwaite, Paul; Andrews, Nick and Charlett, André (2013). An improved algorithm for outbreak detection in multiple surveillance systems. Statistics in Medicine, 32(7) pp. 1206–1222.

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In England and Wales, a large-scale multiple statistical surveillance system for infectious disease outbreaks has been in operation for nearly two decades. This system uses a robust quasi-Poisson regression algorithm to identify aberrances in weekly counts of isolates reported to the Health Protection Agency. In this paper, we review the performance of the system with a view to reducing the number of false reports, while retaining good power to detect genuine outbreaks. We undertook extensive simulations to evaluate the existing system in a range of contrasting scenarios. We suggest several improvements relating to the treatment of trends, seasonality, re-weighting of baselines and error structure. We validate these results by running the existing and proposed new systems in parallel on real data. We find that the new system greatly reduces the number of alarms while maintaining good overall performance and in some instances increasing the sensitivity.

Item Type: Journal Item
Copyright Holders: 2012 John Wiley & Sons, Ltd.
ISSN: 0277-6715
Keywords: negative binomial regression; outlier; outbreak; quasi-Poisson regression; robustness; statistical 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: 34507
Depositing User: Paddy Farrington
Date Deposited: 08 Oct 2012 09:07
Last Modified: 07 Dec 2018 10:08
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