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

Authors :
Biggerstaff, Matthew
Alper, David
Dredze, Mark
Fox, Spencer
Fung, Isaac Chun Hai
Hickmann, Kyle S.
Lewis, Bryan
Rosenfeld, Roni
Shaman, Jeffrey
Tsou, Ming Hsiang
Velardi, Paola
Vespignani, Alessandro
Finelli, Lyn
Chandra, Priyadarshini
Kaup, Hemchandra
Krishnan, Ramesh
Madhavan, Satish
Markar, Ashirwad
Pashley, Bryanne
Paul, Michael
Meyers, Lauren Ancel
Eggo, Rosalind
Henderson, Jette
Ramakrishnan, Anurekha
Scott, James
Singh, Bismark
Srinivasan, Ravi
Bakach, Iurii
Hao, Yi
Schaible, Braydon J.
Sexton, Jessica K.
Del Valle, Sara Y.
Deshpande, Alina
Fairchild, Geoffrey
Generous, Nicholas
Priedhorsky, Reid
Hickman, Kyle S.
Hyman, James M.
Brooks, Logan
Farrow, David
Hyun, Sangwon
Tibshirani, Ryan J.
Yang, Wan
Allen, Christopher
Aslam, Anoshã
Nagel, Anna
Stilo, Giovanni
Basagni, Stefano
Zhang, Qian
Perra, Nicola
Chakraborty, Prithwish
Butler, Patrick
Khadivi, Pejman
Ramakrishnan, Naren
Chen, Jiangzhuo
Barrett, Chris
Bisset, Keith
Eubank, Stephen
Anil Kumar, V. S.
Laskowski, Kathy
Lum, Kristian
Marathe, Madhav
Aman, Susan
Brownstein, John S.
Goldstein, Ed
Lipsitch, Marc
Mekaru, Sumiko R.
Nsoesie, Elaine O.
Gesualdo, Francesco
Tozzi, Alberto E.
Broniatowski, David
Karspeck, Alicia
Tse, Zion Tsz Ho
Ying, Yuchen
Gambhir, Manoj
Scarpino, Sam
Source :
BMC Infectious Diseases
Publication Year :
2016
Publisher :
Springer Science and Business Media LLC, 2016.

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. Electronic supplementary material The online version of this article (doi:10.1186/s12879-016-1669-x) contains supplementary material, which is available to authorized users.

Details

ISSN :
14712334
Volume :
16
Database :
OpenAIRE
Journal :
BMC Infectious Diseases
Accession number :
edsair.doi.dedup.....6d3ec83d7bbcc203277f99f854ae3599
Full Text :
https://doi.org/10.1186/s12879-016-1669-x