Campus Units
Statistics
Document Type
Article
Publication Version
Published Version
Publication Date
2019
Journal or Book Title
Scientific Reports
Volume
9
First Page
683
DOI
10.1038/s41598-018-36361-9
Abstract
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.
Rights
Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted.
Language
en
File Format
application/pdf
Recommended Citation
McGowan, Craig J.; Niemi, Jarad; Ulloa, Nehemias; Will, Katie; and et al., "Collaborative efforts to forecast seasonal influenza in the United States, 2015–2016" (2019). Statistics Publications. 151.
https://lib.dr.iastate.edu/stat_las_pubs/151
Comments
This article is published as McGowan, Craig J., Matthew Biggerstaff, Michael Johansson, Karyn M. Apfeldorf, Michal Ben-Nun, Logan Brooks, Matteo Convertino et al. "Collaborative efforts to forecast seasonal influenza in the United States, 2015–2016." Scientific reports 9 (2019): 683. doi: 10.1038/s41598-018-36361-9.