Results from the centers for disease control and prevention’s predict the 2013–2014 Influenza Season Challenge
Open Access
- 22 July 2016
- journal article
- research article
- Published by Springer Science and Business Media LLC in BMC Infectious Diseases
- Vol. 16 (1), 357
- https://doi.org/10.1186/s12879-016-1669-x
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.Funding Information
- National Institute of General Medical Sciences (5U01GM070694-13, U01GM087719)
- Defense Threat Reduction Agency (HDTRA1-11-D-0016-0001)
- Centers for Disease Control and Prevention (15IPA1509134)
- National Institutes of Health (GM100467, GM110748 and 1U54GM088558)
- National Science Foundation (1416509)
This publication has 27 references indexed in Scilit:
- Reassessing Google Flu Trends Data for Detection of Seasonal and Pandemic Influenza: A Comparative Epidemiological Study at Three Geographic ScalesPLoS Computational Biology, 2013
- Monitoring Influenza Epidemics in China with Search Query from BaiduPLOS ONE, 2013
- New technologies for reporting real-time emergent infectionsParasitology, 2012
- Digital EpidemiologyPLoS Computational Biology, 2012
- Social and News Media Enable Estimation of Epidemiological Patterns Early in the 2010 Haitian Cholera OutbreakThe American Journal of Tropical Medicine and Hygiene, 2012
- Assessing Vaccination Sentiments with Online Social Media: Implications for Infectious Disease Dynamics and ControlPLoS Computational Biology, 2011
- Assessing Google Flu Trends Performance in the United States during the 2009 Influenza Virus A (H1N1) PandemicPLOS ONE, 2011
- Surveillance for Influenza during the 2009 Influenza A (H1N1) Pandemic-United States, April 2009-March 2010Clinical Infectious Diseases, 2010
- Detecting influenza epidemics using search engine query dataNature, 2009
- Using Internet Searches for Influenza SurveillanceClinical Infectious Diseases, 2008