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arXiv · 2102.06684

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

Dongxia Wu·Liyao Gao·Xinyue Xiong·Matteo Chinazzi·Alessandro Vespignani·Yi-An Ma·Rose Yu
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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.

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Dongxia Wu, Liyao Gao, Xinyue Xiong, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu. 2021-02-12. DeepGLEAM: A hybrid mechanistic and deep learning model for COVID-19 forecasting. https://arxiv.org/abs/2102.06684

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