Random Forest of Epidemiological Models for Influenza Forecasting

TL;DR

This paper introduces a Random Forest ensemble method that combines multiple mechanistic influenza forecasting models to improve prediction accuracy, outperforming existing models in the 2022 FluSight challenge.

Contribution

It presents a novel ensemble approach using machine learning to integrate mechanistic models for influenza forecasting, fully automated and without manual tuning.

Findings

Outperforms all other models in mean absolute error

Improves forecast coverage and interval scores

Provides insights through explainability of the ensemble model

Abstract

Forecasting the hospitalizations caused by the Influenza virus is vital for public health planning so that 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. The forecasting models range from mechanistic models, and auto-regression models to machine learning models. 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 hyper-parameters. We compare our prospective forecasts deployed for the FluSight challenge (2022) to all the other submitted approaches. Our approach is fully automated and does not require any manual tuning. 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 outperforms all other models in terms of the mean absolute error and the weighted interval score based on the mean across all weekly submissions in the current season (2022). Explainability of the Random Forest (through analysis of the trees) enables us to gain insights into how it improves upon the individual predictors.