Jan. 16 (UPI) -- Several real-time infectious disease forecasting models have shown promise in accurately predicting the flu, according to new reports.
A coalition of researchers known as the FluSight Network analyzed 20 models and found that over half of them performed better than the historical baseline seasonal averages.
Their findings appeared Tuesday in Proceedings of the National Academy of Sciences.
"We have brought together some of the top flu forecasting teams in the world, and through this collaboration have enabled an apples-to-apples comparison of our different methods and results," Nicholas Reich, biostatistician at the University of Massachusetts who led FluSight and study author, said in a news release.
He said the predictions held true "one, two and three weeks ahead of available data and when forecasting the timing and magnitude of the seasonal peak."
The FluSight Network also helped create an "ensemble" model where each member of the group uses their own scientific techniques to forecast the flu course for the year. Then they combine those individual trajectories for a cumulative model.
The CDC uses the same method to plan and communicate its strategy for addressing flu season.
The flu spreads to between nine and 35 million Americans each year, causing between 12,000 and 56,000 deaths, the researchers say. More than six million people in the U.S. have already been infected this flu season, with more than 80,000 people checking into the hospital for treatment.
"The field of infectious disease forecasting is in its infancy and we expect that innovation will spur improvements in forecasting in the coming years," Reich said. "Public health officials are still learning how best to integrate forecasts into real-time decision making. Close collaboration between public health policy-makers and quantitative modelers is necessary to ensure that forecasts have maximum impact and are appropriately communicated to the public and the broader public health community."