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Title: A Recurrent Neural Network and Differential Equation based Spatiotemporal Infectious Disease Model with Application to COVID-19
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.
Authors:
; ; ;
Award ID(s):
1924548 1632935
Publication Date:
NSF-PAR ID:
10252842
Journal Name:
12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
Volume:
Volume 1: KDIR
Page Range or eLocation-ID:
93-103
Sponsoring Org:
National Science Foundation
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