The goals of this study are to: 1) understand the seasonal flu intervention decision-making process from the perspective of local public health officials and health care providers; and 2) identify these stakeholders’ data needs and priorities for flu forecasting models.
Illustrating the importance of public health data
Infleunza dashbaord at state level, showing ILI+ and hospitalization rates
We propose a novel Cluster-Aggregate-Pool or `CAP’ ensemble algorithm that first clusters together individual forecasts, aggregates individual models that belong to the same cluster into a single forecast (called a cluster forecast), and then pools together cluster forecasts via a linear pool.
Needing no data at the beginning of an epidemic, an adaptive ensemble can quickly train and forecast an outbreak, providing a practical tool to public health officials looking for a forecast to conform to unique features of a specific season.