Novel spatiotemporal feature extraction parallel deep neural network for forecasting confirmed cases of coronavirus disease 2019

https://doi.org/10.1016/j.seps.2020.100976Get rights and content

Abstract

The coronavirus disease 2019 pandemic continues as of March 26 and spread to Europe on approximately February 24. A report from April 29 revealed 1.26 million confirmed cases and 125 928 deaths in Europe. To refer government and enterprise to arrange countermeasures. The paper proposes a novel deep neural network framework to forecast the COVID-19 outbreak. The COVID-19Net framework combined 1D convolutional neural network, 2D convolutional neural network, and bidirectional gated recurrent units. COVID-19Net can well integrate the characteristics of time, space, and influencing factors of the COVID-19 accumulative cases. Three European countries with severe outbreaks were studied—Germany, Italy, and Spain—to extract spatiotemporal features and predict the number of confirmed cases. The prediction results acquired from COVID-19Net are compared to those obtained using a CNN, GRU, and CNN-GRU. The mean absolute error, mean absolute percentage error, and root mean square error, which is commonly used model assessment indices, were used to compare the accuracy of the models. The results verified that COVID-19Net was notably more accurate than the other models. The mean absolute percentage error generated by COVID-19Net was 1.447 for Germany, 1.801 for Italy, and 2.828 for Spain, which was considerably better than those of the other models. This indicated that the proposed framework could accurately predict the accumulated number of confirmed cases in the three countries and serve as an essential reference for devising public health strategies. And also indicated that COVID-19 has high spatiotemporal relations, it suggests us to keep a social distance and avoid unnecessary trips.

Keywords

Confirmed cases forecasting
COVID-19Net
Parallel deep neural network

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Chiou-Jye Huang received the Ph.D. degree in the Department of Electrical Engineering from National Cheng Kung University, Tainan, Taiwan, in 2009. From 2010 to 2011 he was an electronics R&D engineer at the EXA Energy, Ltd., Taichung, Taiwan. From 2011 to 2016 he was a Research Fellow and Project Leader at the Green Energy and Environment Research Laboratories, Industrial Technology Research Institute, Hsinchu, Taiwan. From 2016 to 2017 he was an Associate Professor at School of Information Technology, Beijing Institute of Technology, Zhuhai, Guangdong, China. Since 2018, he has been with the School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, Jiangxi, China, where he is currently an Associate Professor. His major research interests include big data analysis, machine learning and deep learning applications in Internet of Energy (IoE) and environmental science, especially in renewable energy.

Yamin Shen was born in Shaoxing, Zhejiang, China in 1996. He received the B.E. degree in electrical engineering and automation from Zhonghuan Information College Tianjin University of Technology, in 2018 and he is currently pursuing the M.S. degree in electrical engineering and automation from Jiangxi University of Science and Technology.

Ping-Huan Kuo received the B.S., M.S., and Ph.D. degrees from the Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, in 2008, 2010, and 2015, respectively. From 2017 to 2021, he was an Assistant Professor at Computer and Intelligent Robot Program for Bachelor Degree, National Pingtung University. Since 2021, he is currently an Assistant Professor at Department of Mechanical Engineering, National Chung Cheng University. His major research interests include fuzzy control, intelligent algorithms, humanoid robot, image processing, robotic application, big data analysis, machine learning, deep learning applications.

Yung-Hsiang Chen received his Ph.D. degree from the Department of Electrical Engineering at National Cheng Kung University, Taiwan, in 2013. For currently, he is an assistant professor at National Pingtung University of Science and Technology, Taiwan. His research interests are in nonlinear control, big data analysis, deep learning and machine learning applications.

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