Authors:
Zhijian Li
1
;
Yunling Zheng
1
;
Jack Xin
1
and
Guofa Zhou
2
Affiliations:
1
Department of Mathematics, University of California, Irvine, U.S.A.
;
2
College of Health Science, University of California, Irvine, U.S.A.
Keyword(s):
COVID-19, Recurrent Neural Network, Discrete Epidemic Model, Spatiotemporal Deep Learning.
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.