Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge

Matthew Biggerstaff, David Alper, Mark Dredze, Spencer Fox, Isaac Chun Hai Fung, Kyle S. Hickmann, Bryan Lewis, Roni Rosenfeld, Jeffrey Shaman, Ming Hsiang Tsou, Paola Velardi, Alessandro Vespignani, Lyn Finelli, Priyadarshini Chandra, Hemchandra Kaup, Ramesh Krishnan, Satish Madhavan, Ashirwad Markar, Bryanne Pashley, Michael PaulLauren Ancel Meyers, Rosalind Eggo, Jette Henderson, Anurekha Ramakrishnan, James Scott, Bismark Singh, Ravi Srinivasan, Iurii Bakach, Yi Hao, Braydon J. Schaible, Jessica K. Sexton, Sara Y. Del Valle, Alina Deshpande, Geoffrey Fairchild, Nicholas Generous, Reid Priedhorsky, Kyle S. Hickman, James M. Hyman, Logan Brooks, David Farrow, Sangwon Hyun, Ryan J. Tibshirani, Wan Yang, Christopher Allen, Anoshé Aslam, Anna Nagel, Giovanni Stilo, Stefano Basagni, Qian Zhang, Nicola Perra, Prithwish Chakraborty, Patrick Butler, Pejman Khadivi, Naren Ramakrishnan, Jiangzhuo Chen, Chris Barrett, Keith Bisset, Stephen Eubank, V. S. Anil Kumar, Kathy Laskowski, Kristian Lum, Madhav Marathe, Susan Aman, John S. Brownstein, Ed Goldstein, Marc Lipsitch, Sumiko R. Mekaru, Elaine O. Nsoesie, Francesco Gesualdo, Alberto E. Tozzi, David Broniatowski, Alicia Karspeck, Zion Tsz Ho Tse, Yuchen Ying, Manoj Gambhir, Sam Scarpino

Research output: Contribution to journalArticlepeer-review

Abstract

Background: 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. Methods: 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). Results: 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. Conclusion: 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.

Original languageEnglish
Article number357
JournalBMC Infectious Diseases
Volume16
Issue number1
DOIs
Publication statusPublished - Jul 22 2016

Keywords

  • Forecasting
  • Influenza
  • Modeling
  • Prediction

ASJC Scopus subject areas

  • Infectious Diseases

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