FluSight: Flu Forecasting
Each week during the influenza season, CDC displays the forecasts received through the Epidemic Prediction Initiative(EPI). Forecasting for the 2018–2019 influenza season has ended. Forecasting for the 2019–2020 influenza season will resume in late fall 2019.
- Flu activity will likely increase over the next four weeks and the highest flu activity will likely occur in the next two months.
- There is about a 70% chance that the highest flu activity for this season will occur by the end of January and a greater than 95% chance that the highest flu activity will occur by the end of February.
The Influenza Division at CDC has engaged with members of the scientific community on real-world influenza forecasting challenges known as FluSight.
Flu forecasting offers the possibility to look into the future and better plan ahead, potentially reducing the impact of flu.
“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 firstname.lastname@example.org for more information.
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.