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Collaborative efforts to forecast seasonal influenza in the United States, 2015–2016
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 usi...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6346105/ https://www.ncbi.nlm.nih.gov/pubmed/30679458 http://dx.doi.org/10.1038/s41598-018-36361-9 |
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author | McGowan, Craig J. Biggerstaff , Matthew Johansson, Michael Apfeldorf, Karyn M. Ben-Nun, Michal Brooks, Logan Convertino, Matteo Erraguntla, Madhav Farrow, David C. Freeze, John Ghosh, Saurav Hyun, Sangwon Kandula, Sasikiran Lega, Joceline Liu, Yang Michaud, Nicholas Morita, Haruka Niemi, Jarad Ramakrishnan, Naren Ray, Evan L. Reich, Nicholas G. Riley, Pete Shaman, Jeffrey Tibshirani, Ryan Vespignani, Alessandro Zhang, Qian Reed, Carrie |
author_facet | McGowan, Craig J. Biggerstaff , Matthew Johansson, Michael Apfeldorf, Karyn M. Ben-Nun, Michal Brooks, Logan Convertino, Matteo Erraguntla, Madhav Farrow, David C. Freeze, John Ghosh, Saurav Hyun, Sangwon Kandula, Sasikiran Lega, Joceline Liu, Yang Michaud, Nicholas Morita, Haruka Niemi, Jarad Ramakrishnan, Naren Ray, Evan L. Reich, Nicholas G. Riley, Pete Shaman, Jeffrey Tibshirani, Ryan Vespignani, Alessandro Zhang, Qian Reed, Carrie |
author_sort | McGowan, Craig J. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6346105 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-63461052019-01-29 Collaborative efforts to forecast seasonal influenza in the United States, 2015–2016 McGowan, Craig J. Biggerstaff , Matthew Johansson, Michael Apfeldorf, Karyn M. Ben-Nun, Michal Brooks, Logan Convertino, Matteo Erraguntla, Madhav Farrow, David C. Freeze, John Ghosh, Saurav Hyun, Sangwon Kandula, Sasikiran Lega, Joceline Liu, Yang Michaud, Nicholas Morita, Haruka Niemi, Jarad Ramakrishnan, Naren Ray, Evan L. Reich, Nicholas G. Riley, Pete Shaman, Jeffrey Tibshirani, Ryan Vespignani, Alessandro Zhang, Qian Reed, Carrie Sci Rep Article 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. Nature Publishing Group UK 2019-01-24 /pmc/articles/PMC6346105/ /pubmed/30679458 http://dx.doi.org/10.1038/s41598-018-36361-9 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article McGowan, Craig J. Biggerstaff , Matthew Johansson, Michael Apfeldorf, Karyn M. Ben-Nun, Michal Brooks, Logan Convertino, Matteo Erraguntla, Madhav Farrow, David C. Freeze, John Ghosh, Saurav Hyun, Sangwon Kandula, Sasikiran Lega, Joceline Liu, Yang Michaud, Nicholas Morita, Haruka Niemi, Jarad Ramakrishnan, Naren Ray, Evan L. Reich, Nicholas G. Riley, Pete Shaman, Jeffrey Tibshirani, Ryan Vespignani, Alessandro Zhang, Qian Reed, Carrie Collaborative efforts to forecast seasonal influenza in the United States, 2015–2016 |
title | Collaborative efforts to forecast seasonal influenza in the United States, 2015–2016 |
title_full | Collaborative efforts to forecast seasonal influenza in the United States, 2015–2016 |
title_fullStr | Collaborative efforts to forecast seasonal influenza in the United States, 2015–2016 |
title_full_unstemmed | Collaborative efforts to forecast seasonal influenza in the United States, 2015–2016 |
title_short | Collaborative efforts to forecast seasonal influenza in the United States, 2015–2016 |
title_sort | collaborative efforts to forecast seasonal influenza in the united states, 2015–2016 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6346105/ https://www.ncbi.nlm.nih.gov/pubmed/30679458 http://dx.doi.org/10.1038/s41598-018-36361-9 |
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