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

Authors :
Wu, Dongxia
Gao, Liyao
Xiong, Xinyue
Chinazzi, Matteo
Vespignani, Alessandro
Ma, Yi-An
Yu, Rose
Publication Year :
2021

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.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2102.06684
Document Type :
Working Paper