A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States
Open Access
- 15 January 2019
- journal article
- research article
- Published by Proceedings of the National Academy of Sciences in Proceedings of the National Academy of Sciences of the United States of America
- Vol. 116 (8), 3146-3154
- https://doi.org/10.1073/pnas.1812594116
Abstract
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.Keywords
Funding Information
- HHS | NIH | National Institute of General Medical Sciences (R35GM119582)
- DOD | Defense Advanced Research Projects Agency (D16AP00144)
- DOD | Defense Threat Reduction Agency (HDTRA1-18-C-0008)
- Foundation for the National Institutes of Health (5U54GM088491)
- National Science Foundation (0946825, DGE-1252522, DGE-1745016)
- Uptake Technologies (NA)
- HHS | NIH | National Institute of General Medical Sciences (GM110748)
- DOD | Defense Threat Reduction Agency (HDTRA1-15-C-0018)
This publication has 31 references indexed in Scilit:
- Results from the centers for disease control and prevention’s predict the 2013–2014 Influenza Season ChallengeBMC Infectious Diseases, 2016
- Flexible Modeling of Epidemics with an Empirical Bayes FrameworkPLoS Computational Biology, 2015
- Comparison of Filtering Methods for the Modeling and Retrospective Forecasting of Influenza EpidemicsPLoS Computational Biology, 2014
- Estimating the impact of school closure on influenza transmission from Sentinel dataNature, 2008
- Influenza Virus Transmission Is Dependent on Relative Humidity and TemperaturePLoS Pathogens, 2007
- Strictly Proper Scoring Rules, Prediction, and EstimationJournal of the American Statistical Association, 2007
- Mortality Associated With Influenza and Respiratory Syncytial Virus in the United StatesJAMA, 2003
- Forecasting disease risk for increased epidemic preparedness in public healthAdvances In Parasitology, Vol 64, 2000
- Nonlinear forecasting as a way of distinguishing chaos from measurement error in time seriesNature, 1990
- Published by MRE Press