Author: Wu, Dongxia; Gao, Liyao; Xiong, Xinyue; Chinazzi, Matteo; Vespignani, Alessandro; Ma, Yian; Yu, Rose
Title: DeepGLEAM: a hybrid mechanistic and deep learning model for COVID-19 forecasting Cord-id: 1tpyvonk Document date: 2021_2_12
ID: 1tpyvonk
Snippet: 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.
Document: 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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