title: The application of a novel 'rising activity, multi-level mixed effects, indicator emphasis' (RAMMIE) method for syndromic surveillance in England.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
  
  
  BACKGROUND
  roger.morbey@phe.gov.uk.
authors.affiliation: Real-time Syndromic Surveillance Team, Public Health England, Birmingham B3 2PW, UK and.
authors.forename: Roger A
authors.initials: RA
authors.lastname: Morbey
authors.affiliation: Real-time Syndromic Surveillance Team, Public Health England, Birmingham B3 2PW, UK and.
authors.forename: Alex J
authors.initials: AJ
authors.lastname: Elliot
authors.affiliation: Statistics and Modelling Economics Department, Public Health England, London, UK.
authors.forename: Andre
authors.initials: A
authors.lastname: Charlett
authors.affiliation: Statistics and Modelling Economics Department, Public Health England, London, UK.
authors.forename: Neville Q
authors.initials: NQ
authors.lastname: Verlander
authors.affiliation: Statistics and Modelling Economics Department, Public Health England, London, UK.
authors.forename: Nick
authors.initials: N
authors.lastname: Andrews
authors.affiliation: Real-time Syndromic Surveillance Team, Public Health England, Birmingham B3 2PW, UK and.
authors.forename: Gillian E
authors.initials: GE
authors.lastname: Smith
mesh_terms: D000465:Algorithms; D004636:Emergency Service, Hospital; D004739:England; D006801:Humans; D011159:Population Surveillance; D011237:Predictive Value of Tests; D011634:Public Health