
When Data Disappear: Public Health Pays As Policy Strays
Illustrating the importance of public health data
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.