This paper is in the following e-collection/theme issue:

Presentations, Abstracts, or Demonstrations (740) Influenza and Influenza-Like-Illness (ILI) Surveillance (207)

Published on in Vol 7, No 1 (2015):

An Early Warning Influenza Model using Alberta Real- Time Syndromic Data (ARTSSN)

An Early Warning Influenza Model using Alberta Real- Time Syndromic Data (ARTSSN)

An Early Warning Influenza Model using Alberta Real- Time Syndromic Data (ARTSSN)

Authors of this article:

Paul Smetanin ;   Rita K. Biel ;   David Stiff ;   Douglas McNeil ;   Lawrence Svenson ;   Hussain R. Usman ;   David P. Meurer ;   Jane Huang ;   Vanessa Nardelli ;   Christopher Sikora ;   James Talbot
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    We developed early warning algorithms for influenza using data from the Alberta Real-Time Syndromic Surveillance Net (ARTSSN). In addition to looking for signatures of potential pandemics, the model was operationalized by using the algorithms to provide regular weekly forecasts on the influenza trends in Alberta during 2012-2014. We describe the development of the early warning model and the predicted influenza peak time and attack rate results. We report on the usefulness of this model using real-time ARTSSN data, discuss how it was used by decision makers and suggest future enhancements for this promising tool in influenza planning and preparedness.