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A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States
Influenza infects an estimated 9–35 million individuals each year in the United States and is a contributing cause for between 12,000 and 56,000 deaths annually. Seasonal outbreaks of influenza are common in temperate regions of the world, with highest incidence typically occurring in colder and dri...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6386665/ https://www.ncbi.nlm.nih.gov/pubmed/30647115 http://dx.doi.org/10.1073/pnas.1812594116 |
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author | Reich, Nicholas G. Brooks, Logan C. Fox, Spencer J. Kandula, Sasikiran McGowan, Craig J. Moore, Evan Osthus, Dave Ray, Evan L. Tushar, Abhinav Yamana, Teresa K. Biggerstaff, Matthew Johansson, Michael A. Rosenfeld, Roni Shaman, Jeffrey |
author_facet | Reich, Nicholas G. Brooks, Logan C. Fox, Spencer J. Kandula, Sasikiran McGowan, Craig J. Moore, Evan Osthus, Dave Ray, Evan L. Tushar, Abhinav Yamana, Teresa K. Biggerstaff, Matthew Johansson, Michael A. Rosenfeld, Roni Shaman, Jeffrey |
author_sort | Reich, Nicholas G. |
collection | PubMed |
description | Influenza infects an estimated 9–35 million individuals each year in the United States and is a contributing cause for between 12,000 and 56,000 deaths annually. Seasonal outbreaks of influenza are common in temperate regions of the world, with highest incidence typically occurring in colder and drier months of the year. Real-time forecasts of influenza transmission can inform public health response to outbreaks. We present the results of a multiinstitution collaborative effort to standardize the collection and evaluation of forecasting models for influenza in the United States for the 2010/2011 through 2016/2017 influenza seasons. For these seven seasons, we assembled weekly real-time forecasts of seven targets of public health interest from 22 different models. We compared forecast accuracy of each model relative to a historical baseline seasonal average. Across all regions of the United States, over half of the models showed consistently better performance than the historical baseline when forecasting incidence of influenza-like illness 1 wk, 2 wk, and 3 wk ahead of available data and when forecasting the timing and magnitude of the seasonal peak. In some regions, delays in data reporting were strongly and negatively associated with forecast accuracy. More timely reporting and an improved overall accessibility to novel and traditional data sources are needed to improve forecasting accuracy and its integration with real-time public health decision making. |
format | Online Article Text |
id | pubmed-6386665 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-63866652019-02-26 A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States Reich, Nicholas G. Brooks, Logan C. Fox, Spencer J. Kandula, Sasikiran McGowan, Craig J. Moore, Evan Osthus, Dave Ray, Evan L. Tushar, Abhinav Yamana, Teresa K. Biggerstaff, Matthew Johansson, Michael A. Rosenfeld, Roni Shaman, Jeffrey Proc Natl Acad Sci U S A PNAS Plus Influenza infects an estimated 9–35 million individuals each year in the United States and is a contributing cause for between 12,000 and 56,000 deaths annually. Seasonal outbreaks of influenza are common in temperate regions of the world, with highest incidence typically occurring in colder and drier months of the year. Real-time forecasts of influenza transmission can inform public health response to outbreaks. We present the results of a multiinstitution collaborative effort to standardize the collection and evaluation of forecasting models for influenza in the United States for the 2010/2011 through 2016/2017 influenza seasons. For these seven seasons, we assembled weekly real-time forecasts of seven targets of public health interest from 22 different models. We compared forecast accuracy of each model relative to a historical baseline seasonal average. Across all regions of the United States, over half of the models showed consistently better performance than the historical baseline when forecasting incidence of influenza-like illness 1 wk, 2 wk, and 3 wk ahead of available data and when forecasting the timing and magnitude of the seasonal peak. In some regions, delays in data reporting were strongly and negatively associated with forecast accuracy. More timely reporting and an improved overall accessibility to novel and traditional data sources are needed to improve forecasting accuracy and its integration with real-time public health decision making. National Academy of Sciences 2019-02-19 2019-01-15 /pmc/articles/PMC6386665/ /pubmed/30647115 http://dx.doi.org/10.1073/pnas.1812594116 Text en Copyright © 2019 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/ This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | PNAS Plus Reich, Nicholas G. Brooks, Logan C. Fox, Spencer J. Kandula, Sasikiran McGowan, Craig J. Moore, Evan Osthus, Dave Ray, Evan L. Tushar, Abhinav Yamana, Teresa K. Biggerstaff, Matthew Johansson, Michael A. Rosenfeld, Roni Shaman, Jeffrey A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States |
title | A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States |
title_full | A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States |
title_fullStr | A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States |
title_full_unstemmed | A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States |
title_short | A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States |
title_sort | collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the united states |
topic | PNAS Plus |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6386665/ https://www.ncbi.nlm.nih.gov/pubmed/30647115 http://dx.doi.org/10.1073/pnas.1812594116 |
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