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

• URL: http://arxiv.org/abs/2007.10929v2
• Date: Thu, 17 Sep 2020 08:26:13 GMT
• Title: A Recurrent Neural Network and Differential Equation Based Spatiotemporal Infectious Disease Model with Application to COVID-19
• Authors: Zhijian Li, Yunling Zheng, Jack Xin, and Guofa Zhou
• Abstract summary: We develop an integrated disease model based on epidemic differential equations (SIR) and recurrent neural networks (RNN) We trained and tested our model on CO-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.
• Score: 3.464871689508835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. %that is not publicly reported. 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.

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