# A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecasting

arXiv: https://arxiv.org/abs/1902.05113 · HTML: https://mlsanity.com/paper/1902.05113/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2019.

## Built on by (most cited first, 4 of 4)

- [Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey](https://mlsanity.com/paper/2303.14483/) (2023)
- [A Review of Graph Neural Networks in Epidemic Modeling](https://mlsanity.com/paper/2403.19852/) (2024)
- [Deep Generative Spatiotemporal Engression for Probabilistic Forecasting of Epidemics](https://mlsanity.com/paper/2603.07108/) (2026)
- [An integrated recurrent neural network and regression model with spatial and climatic couplings for vector-borne disease dynamics](https://mlsanity.com/paper/2201.09394/) (2022)

## Abstract (arXiv)

We study epidemic forecasting on real-world health data by a graph-structured recurrent neural network (GSRNN). We achieve state-of-the-art forecasting accuracy on the benchmark CDC dataset. To improve model efficiency, we sparsify the network weights via transformed-ℓ1 penalty and maintain prediction accuracy at the same level with 70% of the network weights being zero.