Collaborative efforts to forecast seasonal influenza in the United States, 2015–2016

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作者
Craig J. McGowan
Matthew Biggerstaff 
Michael Johansson
Karyn M. Apfeldorf
Michal Ben-Nun
Logan Brooks
Matteo Convertino
Madhav Erraguntla
David C. Farrow
John Freeze
Saurav Ghosh
Sangwon Hyun
Sasikiran Kandula
Joceline Lega
Yang Liu
Nicholas Michaud
Haruka Morita
Jarad Niemi
Naren Ramakrishnan
Evan L. Ray
Nicholas G. Reich
Pete Riley
Jeffrey Shaman
Ryan Tibshirani
Alessandro Vespignani
Qian Zhang
Carrie Reed
机构
[1] Centers for Disease Control and Prevention,Epidemiology and Prevention Branch, Influenza Division
[2] Centers for Disease Control and Prevention,Dengue Branch, Division of Vector
[3] Arete Associates,Borne Diseases
[4] Predictive Science,Computer Science Department
[5] Inc.,Division of Media and Network Technologies and Division of Frontier Science, Graduate School of Information Science and Technology
[6] Carnegie Mellon University,Division of Environmental Health Sciences
[7] Gi-CoRE Station for Big Data & Cybersecurity,Computational Biology Department
[8] Hokkaido University,Discovery Analytics Center
[9] School of Public Health,Department of Statistics and Data Science
[10] University of Minnesota,Department of Environmental Health Sciences, Mailman School of Public Health
[11] Knowledge Based Systems,Department of Mathematics
[12] Inc.,Department of Statistics
[13] Carnegie Mellon University,Department of Statistics
[14] Virginia Tech University,Department of Mathematics and Statistics
[15] Carnegie Mellon University,Department of Statistics and Data Science, Machine Learning Department
[16] Columbia University,undefined
[17] New York,undefined
[18] University of Arizona,undefined
[19] University of California,undefined
[20] Berkeley,undefined
[21] Iowa State University,undefined
[22] Mount Holyoke College,undefined
[23] Department of Biostatistics and Epidemiology,undefined
[24] School of Public Health and Health Sciences,undefined
[25] University of Massachusetts,undefined
[26] Amherst,undefined
[27] Carnegie Mellon University,undefined
[28] Northeastern University,undefined
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摘要
Since 2013, the Centers for Disease Control and Prevention (CDC) has hosted an annual influenza season forecasting challenge. The 2015–2016 challenge consisted of weekly probabilistic forecasts of multiple targets, including fourteen models submitted by eleven teams. Forecast skill was evaluated using a modified logarithmic score. We averaged submitted forecasts into a mean ensemble model and compared them against predictions based on historical trends. Forecast skill was highest for seasonal peak intensity and short-term forecasts, while forecast skill for timing of season onset and peak week was generally low. Higher forecast skill was associated with team participation in previous influenza forecasting challenges and utilization of ensemble forecasting techniques. The mean ensemble consistently performed well and outperformed historical trend predictions. CDC and contributing teams will continue to advance influenza forecasting and work to improve the accuracy and reliability of forecasts to facilitate increased incorporation into public health response efforts.
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