A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecasting
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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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Feb 2019.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 0; 2026: 1.
4 papers.
- 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
- 2024A Review of Graph Neural Networks in Epidemic Modelingcited by 31
- 2026Deep Generative Spatiotemporal Engression for Probabilistic Forecasting of Epidemicscited by 4
- 2022An integrated recurrent neural network and regression model with spatial and climatic couplings for vector-borne disease dynamicscited by 0
- 2026Deep Generative Spatiotemporal Engression for Probabilistic Forecasting of Epidemics7 Mar 2026
- 2024A Review of Graph Neural Networks in Epidemic Modeling28 Mar 2024
- 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey25 Mar 2023
- 2022An integrated recurrent neural network and regression model with spatial and climatic couplings for vector-borne disease dynamics23 Jan 2022