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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 XinEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 991)

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-\(\ell _1\) penalty without losing prediction accuracy in numerical experiments.

Keywords

Spatio-temporal data Spatio-temporal graph Graph structured recurrent neural network Epidemic forecasting Sparsification 

Notes

Acknowledgments

This material is based on research sponsored by the Air Force Research Laboratory and DARPA under agreement number FA8750-18-2-0066; the U.S. Department of Energy, Office of Science, DOE-SC0013838; the National Science Foundation DMS-1554564 (STROBE), DMS-1737770, DMS-1522383, IIS-1632935. The authors thank Profs. M. Hyman, and J. Lega for helpful discussions.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Zhijian Li
    • 1
  • Xiyang Luo
    • 2
  • Bao Wang
    • 2
  • Andrea L. Bertozzi
    • 2
  • Jack Xin
    • 1
    Email author
  1. 1.UC IrvineIrvineUSA
  2. 2.UCLALos AngelesUSA

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