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

13 Feb 2019 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... (read more)

PDF Abstract

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet