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Harnessing multiple data streams and artificial intelligence to better predict flu




Influenza is highly contagious and easily spreads as people move about and travel, making tracking and forecasting flu activity a challenge. While the CDC continuously monitors patient visits for flu-like illness in the U.S., this information can lag up to two weeks behind real time. A new study, led by the Computational Health Informatics Program (CHIP) at Boston Children’s Hospital, combines two forecasting methods with machine learning (artificial intelligence) to estimate local flu activity. Results are published today in Nature Communications.

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