influenza

We present three combination forecasting techniques. The FluSight Network (FSN) ensemble model is a multimodel ensemble; a convex combination of component predictive densities. The adaptive ensemble is a multimodel ensemble, but unlike the FSN ensemble, recomputes component model weights every week.

Seasonal Influenza infects an average 30 million people in the United States every year, overburdening hospitals during weeks of peak incidence.Named by the CDC as an important tool to fight the damaging effects of these epidemics, accurate forecasts of influenza and influenza like illness (ILI) forewarn public health officials about when, and where, seasonal influenza outbreaks will hit hardest.

Influenza prediction can better prepare public health officials for seasonal outbreaks. We propose an adaptive multi-model ensemble forecast method that changes model weights week-by-week throughout the flu season. Without historical training data or when models do not have a track-record, an adaptive model can enhance the public health impact of ensemble forecasts.

Seasonal Influenza infects an average 30 million people in the United States every year, overburdening hospitals during weeks of peak incidence.Named by the CDC as an important tool to fight the damaging effects of these epidemics, accurate forecasts of influenza and influenza like illness (ILI) forewarn public health officials about when, and where, seasonal influenza outbreaks will hit hardest.