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.
Original language | English (US) |
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Article number | 683 |
Journal | Scientific reports |
Volume | 9 |
Issue number | 1 |
DOIs | |
State | Published - Dec 1 2019 |
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ASJC Scopus subject areas
- General
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Collaborative efforts to forecast seasonal influenza in the United States, 2015–2016. / The Influenza Forecasting Working Group.
In: Scientific reports, Vol. 9, No. 1, 683, 01.12.2019.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Collaborative efforts to forecast seasonal influenza in the United States, 2015–2016
AU - The Influenza Forecasting Working Group
AU - McGowan, Craig J.
AU - Biggerstaff, Matthew
AU - Johansson, Michael
AU - Apfeldorf, Karyn M.
AU - Ben-Nun, Michal
AU - Brooks, Logan
AU - Convertino, Matteo
AU - Erraguntla, Madhav
AU - Farrow, David C.
AU - Freeze, John
AU - Ghosh, Saurav
AU - Hyun, Sangwon
AU - Kandula, Sasikiran
AU - Lega, Joceline C
AU - Liu, Yang
AU - Michaud, Nicholas
AU - Morita, Haruka
AU - Niemi, Jarad
AU - Ramakrishnan, Naren
AU - Ray, Evan L.
AU - Reich, Nicholas G.
AU - Riley, Pete
AU - Shaman, Jeffrey
AU - Tibshirani, Ryan
AU - Vespignani, Alessandro
AU - Zhang, Qian
AU - Reed, Carrie
AU - Rosenfeld, Roni
AU - Ulloa, Nehemias
AU - Will, Katie
AU - Turtle, James
AU - Bacon, David
AU - Riley, Steven
AU - Yang, Wan
PY - 2019/12/1
Y1 - 2019/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85060552300&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060552300&partnerID=8YFLogxK
U2 - 10.1038/s41598-018-36361-9
DO - 10.1038/s41598-018-36361-9
M3 - Article
C2 - 30679458
AN - SCOPUS:85060552300
VL - 9
JO - Scientific Reports
JF - Scientific Reports
SN - 2045-2322
IS - 1
M1 - 683
ER -