The U.S. Centers for Disease Control and Prevention has named Carnegie Mellon University as an Influenza Forecasting Center of Excellence, a five-year designation that includes $3 million in research funding.

For four of the past five years, Carnegie Mellon's forecasting efforts have proven the most accurate of all the research groups participating in the CDC's FluSight Network.

In addition to expanding CMU's existing forecasting research, the new funding will enable CMU to initiate studies on how to best communicate forecast information to the public and to leaders. It will also support efforts to determine how forecasting techniques might apply to pandemics — the rare occasions when a truly novel strain of flu is prevalent around the world.

Roni Rosenfeld, head of CMU's Machine Learning Department and leader of its epidemic forecasting efforts, said the designation of CMU and the University of Massachusetts at Amherst as the first two CDC flu forecasting centers of excellence marks a coming of age for the epidemic forecasting community.

"When the CDC began soliciting flu forecasts, they ran it as an experiment," without funding, Rosenfeld said. But as the usefulness of the forecasts became apparent, the CDC has placed greater reliance on them. "The CDC now routinely includes our forecasting in their messaging to the public and to decision makers."

"In the beginning, we had about 10 groups that voluntarily submitted forecasts," he said. "Now the CDC receives more than 40 forecast submissions. It has become a community and more and more groups are getting involved, which is the real win."

The CDC has historically tracked flu epidemics through a surveillance network that includes doctors' offices and clinics. But just as weather forecasting is more useful than only reporting the current weather, accurate flu forecasting enables health officials to make more timely decisions to launch public information and vaccination campaigns and helps health providers plan clinic schedules and staffing.

CMU has focused on two methods for flu forecasting — one that uses machine learning and computational statistics to make predictions based on both past patterns and input from the CDC's domestic flu surveillance system and a second that bases its predictions on the judgments of human volunteers who submit weekly predictions.

Work will continue on both those methods, said Ryan Tibshirani, associate professor of statistics and machine learning and co-leader of the Delphi Research Group, which is devoted to epidemic forecasting.

Baruch Fischhoff, professor of engineering and public policy and an expert on risk communication, will join the expanded effort. He will explore how flu forecast information can be communicated so both decision makers and the general public can use it effectively, while understanding the limits of the forecasts.

"These audiences want to know what will happen," Tibshirani said. "We can only tell them what will probably happen. We want to be sure we send them messages that they interpret properly."

Researchers at the Harvard School of Public Health also will join with CMU to explore how forecasting technology might apply to pandemics. These events only happen "once in a blue moon," Rosenfeld said, but are critically serious when they do occur. The University of Pittsburgh School of Public Health, a previous collaborator with CMU, also will be part of the new center of excellence, providing important new sources of information to improve forecasts' accuracy.

Previous sponsors of CMU's forecasting research have included the Defense Threat Reduction Agency and the National Institute of General Medical Sciences' Models of Infectious Disease Agency Study (MIDAS).