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Proc Natl Acad Sci U S A. 2019 Feb 19;116(8):3146-3154. doi: 10.1073/pnas.1812594116. Epub 2019 Jan 15.

A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States.

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Department of Biostatistics and Epidemiology, University of Massachusetts-Amherst, Amherst, MA 01003;
Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, 15213.
Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712.
Department of Environmental Health Sciences, Columbia University, New York, NY 10032.
Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA 30333.
Department of Biostatistics and Epidemiology, University of Massachusetts-Amherst, Amherst, MA 01003.
Statistical Sciences Group, Los Alamos National Laboratory, Los Alamos, NM 87545.
Department of Mathematics and Statistics, Mount Holyoke College, South Hadley, MA 01075.
Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, San Juan, PR 00920.
Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213.


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.


forecasting; infectious disease; influenza; public health; statistics

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