Papers › A Study on Graph-Structured Recurrent Neural Networks and Sparsification with...
A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecasting
Zhijian Li, Xiyang Luo, Bao Wang, Andrea L. Bertozzi, Jack Xin
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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections