DeepGLEAM: A hybrid mechanistic and deep learning model for COVID-19 forecasting
arXiv:2102.06684
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.
References in corpus (11)
- Adam: A Method for Stochastic Optimization
- PyTorch: An Imperative Style, High-Performance Deep Learning Library
- Semi-Supervised Classification with Graph Convolutional Networks
- Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
- Stochastic Backpropagation and Approximate Inference in Deep Generative Models
- Multiscale mobility networks and the large scale spreading of infectious diseases
- High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach
- Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors
- Single-Model Uncertainties for Deep Learning
- Adaptive, Distribution-Free Prediction Intervals for Deep Networks
- Bayesian Inference for Large Scale Image Classification
Cited by in corpus (4)
- Some Challenges in Monitoring Epidemics
- Deep Learning-Based Spatiotemporal Multi-Event Reconstruction for Delay Line Detectors
- A Physiologically-Constrained Neural Network Digital Twin Framework for Replicating Glucose Dynamics in Type 1 Diabetes
- COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 Prediction