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Harnessing multiple data streams and artificial intelligence to better predict flu

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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 US, 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 to estimate local flu activity.

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