English

DeepGLEAM: A hybrid mechanistic and deep learning model for COVID-19 forecasting

Machine Learning 2021-03-24 v3

Abstract

We introduce DeepGLEAM, a hybrid model for COVID-19 forecasting. DeepGLEAM combines a mechanistic stochastic simulation model GLEAM with deep learning. It uses deep learning to learn the correction terms from GLEAM, which leads to improved performance. We further integrate various uncertainty quantification methods to generate confidence intervals. We demonstrate DeepGLEAM on real-world COVID-19 mortality forecasting tasks.

Keywords

Cite

@article{arxiv.2102.06684,
  title  = {DeepGLEAM: A hybrid mechanistic and deep learning model for COVID-19 forecasting},
  author = {Dongxia Wu and Liyao Gao and Xinyue Xiong and Matteo Chinazzi and Alessandro Vespignani and Yi-An Ma and Rose Yu},
  journal= {arXiv preprint arXiv:2102.06684},
  year   = {2021}
}