Selected article for: "simulation model and stochastic simulation model"

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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