FluSight: Flu Forecasting
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Unlike CDC’s traditional influenza surveillance systems, which measure influenza activity after it has occurred, flu forecasting offers the possibility to look into the future and better plan ahead, potentially reducing the impact of flu.
Current Flu Forecasting:
Each week during the influenza season, CDC displays the forecasts received through the Epidemic Prediction Initiative(EPI). This early in the season, when flu activity is relatively low, forecast of the peak of flu activity are less precise.
How Flu Forecasting Works
The Influenza Division at CDC has engaged with members of the scientific community on real-world influenza forecasting challenges known as FluSight.
Why Forecast Flu
Flu forecasting offers the possibility to look into the future and better plan ahead, potentially reducing the impact of flu.
“FluSight” Flu Forecasting Website
“FluSight,” flu forecasting website is part of CDC’s Epidemic Prediction Initiative. This website facilitates the real-time sharing and visualization of weekly flu forecasts.
CDC’s efforts with forecasting began in 2013 with the “Predict the Influenza Season Challenge”, a competition that encouraged outside academic and private industry researchers to forecast the timing, peak, and intensity of the flu season. Each influenza season since then, flu experts within the Influenza Division have worked with CDC’s Epidemic Prediction Initiative (EPI) and external researchers to advance flu forecasting. CDC provides forecasting teams data, relevant public health forecasting targets, and forecast accuracy metrics evaluated against actual flu activity while each team submits their forecasts based on a variety of methods and data sources each week. During the 2018–19 season, CDC expects forecasting teams to provide over 30 national-level forecasts each week.
Interested in participating in the challenge? Please email flucontest@cdc.gov for more information.
Flu Forecasting Accuracy Results for the 2017-2018 Season
During the 2017-2018 flu season, 21 different teams participated in the forecasting initiative, submitting 30 different weekly forecasts. The Delphi group at Carnegie Mellon University contributed the most accurate national-, regional-, and state-level influenza-like illness and national-level hospitalization forecasts to the site. The team, led by Dr. Roni Rosenfeld, used a combination of machine learning and crowd-sourcing methods to generate the forecasts. This marks the fourth influenza season in a row where forecasts from Dr. Rosenfeld’s team have been named the most accurate.
- Page last reviewed: December 13, 2018
- Page last updated: December 13, 2018
- Content source:
- Centers for Disease Control and Prevention, National Center for Immunization and Respiratory Diseases (NCIRD)
- Page maintained by: Office of the Associate Director for Communication, Digital Media Branch, Division of Public Affairs