Influenza Forecaster
Contribute weekly state-level influenza hospitalization forecasts to the CDC FluSight project (Fall 2026–Spring 2027).
Influenza
Enhancing local public health decision-making: Incorporating end-user perspectives into influenza forecasting models
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.
When Data Disappear: Public Health Pays As Policy Strays
Illustrating the importance of public health data
Influenza Dashboard
Infleunza dashbaord at state level, showing ILI+ and hospitalization rates
A Cluster-Aggregate-Pool (CAP) Ensemble Algorithm for Improved Forecast Performance of influenza-like illness
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.
Adaptively stacking ensembles for influenza forecasting with incomplete data
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.