title: Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge.
abstract: Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season.
  
  
  
  Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet).
  
  
  
  Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones.
  
  
  
  Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts.
authors.affiliation: Epidemiology and Prevention Branch, Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, USA. mbiggerstaff@cdc.gov.
authors.forename: Matthew
authors.initials: M
authors.lastname: Biggerstaff
authors.affiliation: Everyday Health, New York, New York, USA.
authors.forename: David
authors.initials: D
authors.lastname: Alper
authors.affiliation: Johns Hopkins University, Baltimore, Maryland, USA.
authors.forename: Mark
authors.initials: M
authors.lastname: Dredze
authors.affiliation: University of Texas at Austin, Austin, Texas, USA.
authors.forename: Spencer
authors.initials: S
authors.lastname: Fox
authors.affiliation: Georgia Southern University, Statesboro, Georgia, USA.
authors.forename: Isaac Chun-Hai
authors.initials: IC
authors.lastname: Fung
authors.affiliation: Los Alamos National Laboratory, Los Alamos, New Mexico, USA.|Tulane University, New Orleans, Louisiana, USA.
authors.forename: Kyle S
authors.initials: KS
authors.lastname: Hickmann
authors.affiliation: Virginia Tech, Blacksburg, Virginia, USA.
authors.forename: Bryan
authors.initials: B
authors.lastname: Lewis
authors.affiliation: Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
authors.forename: Roni
authors.initials: R
authors.lastname: Rosenfeld
authors.affiliation: Columbia University, New York, New York, USA.
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Shaman
authors.affiliation: San Diego State University, San Diego, California, USA.
authors.forename: Ming-Hsiang
authors.initials: MH
authors.lastname: Tsou
authors.affiliation: Sapienza University of Roma, Rome, Italy.
authors.forename: Paola
authors.initials: P
authors.lastname: Velardi
authors.affiliation: Northeastern University, Boston, Massachusetts, USA.
authors.forename: Alessandro
authors.initials: A
authors.lastname: Vespignani
authors.affiliation: Epidemiology and Prevention Branch, Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
authors.forename: Lyn
authors.initials: L
authors.lastname: Finelli
mesh_terms: D002487:Centers for Disease Control and Prevention, U.S.; D005544:Forecasting; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology / Q000517:prevention & control*; D008954:Models, Biological; D015233:Models, Statistical; D062486:Public Health Surveillance; D012621:Seasons; D014481:United States / Q000453:epidemiology
keywords: Forecasting; Influenza; Modeling; Prediction