mlsanityData to 7 Oct 2026

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

arXiv 2102.06684 ↗First posted 12 Feb 2021cs.LGcited by 11

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 17 of its 51 references that are in the map. First posted on arXiv 12 Feb 2021.

Builds on

17 of 51 references.

References · 17

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  4. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  5. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,428
  6. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  7. 2019A Simple Baseline for Bayesian Uncertainty in Deep Learningcited by 555
  8. 2017Concrete Dropoutcited by 227
  9. 2020Efficient and Scalable Bayesian Neural Nets with Rank-1 Factorscited by 171
  10. 2015A Complete Recipe for Stochastic Gradient MCMCcited by 146
  11. 2018High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approachcited by 117
  12. 2016Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriorscited by 82
5 more
  1. 2019Adaptive, Distribution-Free Prediction Intervals for Deep Networkscited by 56
  2. 2017Deep and Confident Prediction for Time Series at Ubercited by 50
  3. 2019Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernelcited by 19
  4. 2019Bayesian Inference for Large Scale Image Classificationcited by 18
  5. 2015Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Samplingcited by 10

Built on by

Cited by year

Citing papers per year: 2021: 3; 2022: 2; 2023: 2; 2024: 1; 2025: 3.

11 papers.

  1. 2021Physics-Guided Deep Learning for Dynamical Systems: A Surveycited by 43
  2. 2022Data-Centric Epidemic Forecasting: A Surveycited by 8
  3. 2021The Prominence of Artificial Intelligence in COVID-19cited by 7
  4. 2025Mantis: A Foundation Model for Mechanistic Disease Forecastingcited by 3
  5. 2021COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 Predictioncited by 1
  6. 2024WeatherFormer: A Pretrained Encoder Model for Learning Robust Weather Representations from Small Datasetscited by 1
  7. 2025A Physiologically-Constrained Neural Network Digital Twin Framework for Replicating Glucose Dynamics in Type 1 Diabetescited by 1
  8. 2022Phy-Taylor: Physics-Model-Based Deep Neural Networkscited by 0
  9. 2023Deep COVID-19 Forecasting for Multiple States with Data Augmentationcited by 0
  10. 2023Deep Learning-Based Spatiotemporal Multi-Event Reconstruction for Delay Line Detectorscited by 0
  11. 2025TGLF-WINN: Data-Efficient Deep Learning Surrogate for Turbulent Transport Modeling in Fusioncited by 0