Random Forest of epidemiological models for Influenza forecasting

Forecasting the hospitalizations caused by the Influenza virus is vital for public health planning so hospitals can be better prepared for an influx of patients. Many forecasting methods have been used in real-time during the Influenza seasons and submitted to the CDC for public communication. We hypothesize that we can improve forecasting by using multiple mechanistic models to produce potential trajectories and use machine learning to learn how to combine those trajectories into an improved forecast. We propose a Tree Ensemble model design that utilizes the individual predictors of our baseline model SIkJalpha to improve its performance. Each predictor is generated by changing a set of hyperparameters. We compare our prospective forecasts deployed for the FluSight challenge (seasons ending in 2022, 2023, and 2024) to all the other submitted approaches. Our approach is fully automated and does not require any manual tuning. Our submissions remained in the top 33% of the models in all seasons. We demonstrate that our Random Forest-based approach is able to improve upon the forecasts of the individual predictors in terms of mean absolute error, coverage, and weighted interval score. Our method retrospectively outperformed all other models in terms of the mean absolute error and the weighted interval score based on the mean across all weekly submissions of the 2021-22 season.