Random Forest of Epidemiological Models for Influenza Forecasting
- URL: http://arxiv.org/abs/2206.08967v1
- Date: Fri, 17 Jun 2022 18:47:40 GMT
- Title: Random Forest of Epidemiological Models for Influenza Forecasting
- Authors: Majd Al Aawar, Ajitesh Srivastava
- Abstract summary: We propose a Tree Ensemble model design that utilizes the individual predictors of our baseline model SIkJalpha to improve its performance.
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.
- Score: 7.050453841068465
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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