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Predictive Analytics Models Accurately Forecast Flu Trends

January 22, 2019 / Jessica Kent

Predictive analytics models were able to forecast trends in influenza outbreaks with greater accuracy than historical baseline models. Led by biostatistician Nicholas Reich, researchers at the University of Massachusetts Amherst formed a group called the FluSight Network. The team compared the forecast accuracy of 20 predictive models to a historical baseline seasonal average, using data from influenza seasons in 2010 through 2017. The team found that the predictive analytics models achieved greater accuracy than other approaches. “Across all regions of the United States, over half of the models showed consistently better performance than the historical baseline when forecasting incidence of influenza-like illness one, two and three weeks ahead of available data and when forecasting the timing and magnitude of the seasonal peak,” the researchers said.