Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S.
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- Univ. of Massachusetts, Amherst, MA (United States)
- Centers for Disease Control and Prevention, Atlanta, GA (United States)
- Columbia Univ., New York, NY (United States)
- Mount Holyoke College, South Hadley, MA (United States)
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Carnegie Mellon Univ., Pittsburgh, PA (United States)
- Smith College, Northampton, MA (United States)
- Amherst College, MA (United States)
- Centers for Disease Control and Prevention, San Juan, PR (United States)
Seasonal influenza results in substantial annual morbidity and mortality in the United States and worldwide. Accurate forecasts of key features of influenza epidemics, such as the timing and severity of the peak incidence in a given season, can inform public health response to outbreaks. As part of ongoing efforts to incorporate data and advanced analytical methods into public health decision-making, the United States Centers for Disease Control and Prevention (CDC) has organized seasonal influenza forecasting challenges since the 2013/2014 season. In the 2017/2018 season, 22 teams participated. A subset of four teams created a research consortium called the FluSight Network in early 2017. During the 2017/2018 season they worked together to produce a collaborative multi-model ensemble that combined 21 separate component models into a single model using a machine learning technique called stacking. This approach creates a weighted average of predictive densities where the weight for each component is determined by maximizing overall ensemble accuracy over past seasons. In the 2017/2018 influenza season, one of the largest seasonal outbreaks in the last 15 years, this multi-model ensemble performed better on average than all individual component models and placed second overall in the CDC challenge. It also outperformed the baseline multi-model ensemble created by the CDC that took a simple average of all models submitted to the forecasting challenge. This project shows that collaborative efforts between research teams to develop ensemble forecasting approaches can bring measurable improvements in forecast accuracy and important reductions in the variability of performance from year to year. Efforts such as this, that emphasize real-time testing and evaluation of forecasting models and facilitate the close collaboration between public health officials and modeling researchers, are essential to improving our understanding of how best to use forecasts to improve public health response to seasonal and emerging epidemic threats.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA); National Institutes of Health (NIH); Defense Advanced Research Projects Agency (DARPA); Defense Threat Reduction Agency (DTRA); Uptake Technologies
- Grant/Contract Number:
- 89233218CNA000001; R35GM119582; HDTRA1-18-C-0008; 5U54GM088491; 0946825; DGE-1252522; DGE-1745016; GM110748
- OSTI ID:
- 1604056
- Report Number(s):
- LA-UR-20-22025
- Journal Information:
- PLoS Computational Biology (Online), Vol. 15, Issue 11; ISSN 1553-7358
- Publisher:
- Public Library of ScienceCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
Improving probabilistic infectious disease forecasting through coherence
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journal | January 2021 |
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