Using Machine Learning for Epidemiological Forecasting

Research Area(s): Computational Biology and Epidemiology
This research group focuses on the use of machine learning to solve real world problems, particularly those involving natural language, forecasting, and health.

Roni Rosenfeld

Case Study: Improving Epidemiological Forecasting with the Centers for Disease Control and Prevention (CDC)

Epidemiological forecasting is critically needed for decision making by public health officials, commercial and non-commercial institutions, and the general public. Our group at Carnegie Mellon University focuses on developing the technological capability of epi-forecasting, and its role in decision making, both public and private.

One vision is to make epidemiological forecasting as universally accepted and useful as weather forecasting is today. To create the tools to achieve this end, we select high value epidemiological forecasting targets (currently Influenza and Dengue); create baseline forecasting methods for them; establish metrics for measuring and tracking forecasting accuracy; estimate the limits of forecastability for each target; and identify new sources of data that could be helpful to the forecasting goal.

Our Delphi research group  is the only group to have participated (and done very well) in all epidemic forecasting challenges organized by the U.S. government to date (influenza, dengue, chikungunya).  Most recently, the U.S. Centers for Disease Control and Prevention (U.S. CDC) named us “Most Accurate Forecaster” for the most recent flu season.  

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