# Integrating LSTMs and GNNs for COVID-19 Forecasting

arXiv: https://arxiv.org/abs/2108.10052 · HTML: https://mlsanity.com/paper/2108.10052/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 14 Jul 2021.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting](https://mlsanity.com/paper/2103.07719/) (2021)
- [Structured Sequence Modeling with Graph Convolutional Recurrent Networks](https://mlsanity.com/paper/1612.07659/) (2016)
- [PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models](https://mlsanity.com/paper/2104.07788/) (2021)
- [Coronavirus (COVID-19) Classification using CT Images by Machine Learning Methods](https://mlsanity.com/paper/2003.09424/) (2020)
- [Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany](https://mlsanity.com/paper/2101.00661/) (2021)

## Built on by (most cited first, 8 of 8)

- [A Review of Graph Neural Networks in Epidemic Modeling](https://mlsanity.com/paper/2403.19852/) (2024)
- [Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph](https://mlsanity.com/paper/2410.00049/) (2024)
- [Sparsification and Filtering for Spatial-temporal GNN in Multivariate Time-series](https://mlsanity.com/paper/2203.03991/) (2022)
- [Multiwave COVID-19 Prediction from Social Awareness using Web Search and Mobility Data](https://mlsanity.com/paper/2110.11584/) (2021)
- [miMamba: EEG-based Emotion Recognition with Multi-scale Inverted Mamba Models](https://mlsanity.com/paper/2409.07589/) (2024)
- [Deep Learning-Derived Optimal Aviation Strategies to Control Pandemics](https://mlsanity.com/paper/2210.10888/) (2022)
- [Futures Quantitative Investment with Heterogeneous Continual Graph Neural Network](https://mlsanity.com/paper/2303.16532/) (2023)
- [MP-PINN: A Multi-Phase Physics-Informed Neural Network for Epidemic Forecasting](https://mlsanity.com/paper/2411.06781/) (2024)

## Abstract (arXiv)

The spread of COVID-19 has coincided with the rise of Graph Neural Networks (GNNs), leading to several studies proposing their use to better forecast the evolution of the pandemic. Many such models also include Long Short Term Memory (LSTM) networks, a common tool for time series forecasting. In this work, we further investigate the integration of these two methods by implementing GNNs within the gates of an LSTM and exploiting spatial information. In addition, we introduce a skip connection which proves critical to jointly capture the spatial and temporal patterns in the data. We validate our daily COVID-19 new cases forecast model on data of 37 European nations for the last 472 days and show superior performance compared to state-of-the-art graph time series models based on mean absolute scaled error (MASE). This area of research has important applications to policy-making and we analyze its potential for pandemic resource control.