A Recurrent Neural Network and Differential Equation based Spatiotemporal Infectious Disease Model with Application to COVID-19

Zhijian Li, Yunling Zheng, Jack Xin, Guofa Zhou

2020

Abstract

The outbreaks of Coronavirus Disease 2019 (COVID-19) have impacted the world significantly. Modeling the trend of infection and real-time forecasting of cases can help decision making and control of the disease spread. However, data-driven methods such as recurrent neural networks (RNN) can perform poorly due to limited daily samples in time. In this work, we develop an integrated spatiotemporal model based on the epidemic differential equations (SIR) and RNN. The former after simplification and discretization is a compact model of temporal infection trend of a region while the latter models the effect of nearest neighboring regions. The latter captures latent spatial information. We trained and tested our model on COVID-19 data in Italy, and show that it out-performs existing temporal models (fully connected NN, SIR, ARIMA) in 1-day, 3-day, and 1-week ahead forecasting especially in the regime of limited training data.

Paper Citation

in Harvard Style

Li Z., Zheng Y., Xin J. and Zhou G. (2020). A Recurrent Neural Network and Differential Equation based Spatiotemporal Infectious Disease Model with Application to COVID-19. In Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 1: KDIR; ISBN 978-989-758-474-9, SciTePress, pages 93-103. DOI: 10.5220/0010130000930103

in Bibtex Style

@conference{kdir20,
author={Zhijian Li and Yunling Zheng and Jack Xin and Guofa Zhou},
title={A Recurrent Neural Network and Differential Equation based Spatiotemporal Infectious Disease Model with Application to COVID-19},
booktitle={Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 1: KDIR},
year={2020},
pages={93-103},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010130000930103},
isbn={978-989-758-474-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 1: KDIR
TI - A Recurrent Neural Network and Differential Equation based Spatiotemporal Infectious Disease Model with Application to COVID-19
SN - 978-989-758-474-9
AU - Li Z.
AU - Zheng Y.
AU - Xin J.
AU - Zhou G.
PY - 2020
SP - 93
EP - 103
DO - 10.5220/0010130000930103
PB - SciTePress