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

Abstract

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 a transformed- penalty without losing prediction accuracy in numerical experiments.