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Harnessing multiple data streams and artificial intelligence to better predict flu

When the approach, called ARGONet, was applied to flu seasons from September 2014 to May 2017, it made more accurate predictions than the team's earlier high-performing forecasting approach, ARGO, in more than 75 percent of the states studied.

This suggests that ARGONet produces the most accurate estimates of influenza activity available to date, a week ahead of traditional healthcare-based reports, at the state level across the U.S. 'Timely and reliable methodologies for tracking influenza activity across locations can help public health officials mitigate epidemic outbreaks and may improve communication with the public to raise awareness of potential risks,' says Mauricio Santillana, Ph.D., a CHIP faculty member and the paper' senior author.