The application of a novel 'rising activity, multi-level mixed effects, indicator emphasis' (RAMMIE) method for syndromic surveillance in England

Roger Morbey, Alex Elliot, Andre Charlett, Neville Verlander, Nicholas Andrews, Gillian Smith

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)

Abstract

Motivation: Syndromic surveillance is the real-time collection and interpretation of data to allow the early identification of public health threats and their impact, enabling public health action. The 'rising activity, multi-level mixed effects, indicator emphasis' method was developed to provide a single robust method enabling detection of unusual activity across a wide range of syndromes, nationally and locally. Results: The method is shown here to have a high sensitivity (92%) and specificity (99%) compared to previous methods, whilst halving the time taken to detect increased activity to 1.3 days. Availability and implementation: The method has been applied successfully to syndromic surveillance systems in England providing realistic models for baseline activity and utilizing prioritization rules to ensure a manageable number of 'alarms' each day.

Original languageEnglish
Pages (from-to)3660-3665
Number of pages6
JournalBioinformatics
Volume31
Issue number22
DOIs
Publication statusPublished - 25 May 2015

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