Imperial College London
Alternate

Steven Riley

Faculty of Medicine, School of Public Health

Professor of Infectious Disease Dynamics
 
 
 
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Contact

 

+44 (0)20 7594 2452s.riley

 
 
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Location

 

UG8Medical SchoolSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{McGowan:2019:10.1038/s41598-018-36361-9,
author = {McGowan, CJ and Biggerstaff, M and Johansson, M and Apfeldorf, KM and Ben-Nun, M and Brooks, L and Convertino, M and Erraguntla, M and Farrow, DC and Freeze, J and Ghosh, S and Hyun, S and Kandula, S and Lega, J and Liu, Y and Michaud, N and Morita, H and Niemi, J and Ramakrishnan, N and Ray, EL and Reich, NG and Riley, P and Shaman, J and Tibshirani, R and Vespignani, A and Zhang, Q and Reed, C and Influenza, Forecasting Working Group},
doi = {10.1038/s41598-018-36361-9},
journal = {Sci Rep},
title = {Collaborative efforts to forecast seasonal influenza in the United States, 2015-2016.},
url = {http://dx.doi.org/10.1038/s41598-018-36361-9},
volume = {9},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
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.
AU  - McGowan,CJ
AU  - Biggerstaff,M
AU  - Johansson,M
AU  - Apfeldorf,KM
AU  - Ben-Nun,M
AU  - Brooks,L
AU  - Convertino,M
AU  - Erraguntla,M
AU  - Farrow,DC
AU  - Freeze,J
AU  - Ghosh,S
AU  - Hyun,S
AU  - Kandula,S
AU  - Lega,J
AU  - Liu,Y
AU  - Michaud,N
AU  - Morita,H
AU  - Niemi,J
AU  - Ramakrishnan,N
AU  - Ray,EL
AU  - Reich,NG
AU  - Riley,P
AU  - Shaman,J
AU  - Tibshirani,R
AU  - Vespignani,A
AU  - Zhang,Q
AU  - Reed,C
AU  - Influenza,Forecasting Working Group
DO  - 10.1038/s41598-018-36361-9
PY  - 2019///
TI  - Collaborative efforts to forecast seasonal influenza in the United States, 2015-2016.
T2  - Sci Rep
UR  - http://dx.doi.org/10.1038/s41598-018-36361-9
UR  - https://www.ncbi.nlm.nih.gov/pubmed/30679458
VL  - 9
ER  -
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