title: Syndromic surveillance using veterinary laboratory data: algorithm combination and customization of alerts.
abstract: BACKGROUND
  Syndromic surveillance research has focused on two main themes: the search for data sources that can provide early disease detection; and the development of efficient algorithms that can detect potential outbreak signals.
  
  
  METHODS
  This work combines three algorithms that have demonstrated solid performance in detecting simulated outbreak signals of varying shapes in time series of laboratory submissions counts. These are: the Shewhart control charts designed to detect sudden spikes in counts; the EWMA control charts developed to detect slow increasing outbreaks; and the Holt-Winters exponential smoothing, which can explicitly account for temporal effects in the data stream monitored. A scoring system to detect and report alarms using these algorithms in a complementary way is proposed.
  
  
  RESULTS
  The use of multiple algorithms in parallel resulted in increased system sensitivity. Specificity was decreased in simulated data, but the number of false alarms per year when the approach was applied to real data was considered manageable (between 1 and 3 per year for each of ten syndromic groups monitored). The automated implementation of this approach, including a method for on-line filtering of potential outbreak signals is described.
  
  
  CONCLUSIONS
  The developed system provides high sensitivity for detection of potential outbreak signals while also providing robustness and flexibility in establishing what signals constitute an alarm. This flexibility allows an analyst to customize the system for different syndromes.
authors.affiliation: Department of Health Management, University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada.
authors.forename: Fernanda C
authors.initials: FC
authors.lastname: Dórea
authors.affiliation: Animal Health Laboratory, University of Guelph, Guelph, Ontario, Canada.
authors.forename: Beverly J
authors.initials: BJ
authors.lastname: McEwen
authors.affiliation: Animal Health and Welfare Branch, Ontario Ministry of Agriculture Food and Rural Affairs, Guelph, Ontario, Canada.
authors.forename: W Bruce
authors.initials: WB
authors.lastname: McNab
authors.affiliation: Department of Health Management, University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada.
authors.forename: Javier
authors.initials: J
authors.lastname: Sanchez
authors.affiliation: Department of Health Management, University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada.
authors.forename: Crawford W
authors.initials: CW
authors.lastname: Revie
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D004196:Disease Outbreaks / Q000662:veterinary*; D042241:Early Diagnosis; D007753:Laboratories; D057230:Limit of Detection; D011159:Population Surveillance; D013223:Statistics as Topic; D013577:Syndrome; D014730:Veterinary Medicine