Predicting the Epidemics Trend of COVID-19 Using Epidemiological-Based Generative Adversarial Networks

doi:10.1109/JSTSP.2022.3152375

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	Predicting the Epidemics Trend of COVID-19 Using Epidemiological-Based Generative Adversarial Networks
	Wang, Haishuai 8; Tao, Guangyu 7; Ma, Jiali 6; Jia, Shangru 6; Chi, Lianhua 5; Yang, Hong 4; Zhao, Ziping 6; Tao, Jianhua 1,2,3
刊名	IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
	2022-02-01
卷号	16 期号:2 页码:276-288
关键词	COVID-19 Predictive models Mathematical models Data models Pandemics Market research Biological system modeling COVID-19 generative adversarial networks time series prediction SIR simulation
ISSN号	1932-4553
DOI	10.1109/JSTSP.2022.3152375
通讯作者	Zhao, Ziping(ztianjin@126.com) ; Tao, Jianhua(jhtao@nlpr.ia.ac.cn)
英文摘要	The Coronavirus disease 2019 (COVID-19) is a respiratory illness that can spread from person to person. Since the COVID-19 pandemic is spreading rapidly over the world and its outbreak has affected different people in different ways, it is significant to study or predict the evolution of its epidemic trend. However, most of the studies focused solely on either classical epidemiological models or machine learning models for COVID-19 pandemic forecasting, which either suffer from the limitation of the generalization ability and scalability or the lack of surveillance data. In this work, we propose T-SIRGAN that integrates the strengths of the epidemiological theories and deep learning models to be able to represent complex epidemic processes and model the non-linear relationship for more accurate prediction of the growth of COVID-19. T-SIRGAN first adopts the Susceptible-Infectious-Recovered (SIR) model to generate epidemiological-based simulation data, which are then fed into a generative adversarial network (GAN) as adversarial examples for data augmentation. Then, Transformers are used to predict the future trends of COVID-19 based on the generated synthetic data. Extensive experiments on real-world datasets demonstrate the superiority of our method. We also discuss the effectiveness of vaccine based on the difference between the predicted and the reported number of COVID-19 cases.
资助项目	Zhejiang Provincial Key Research and Development Program of China[2021C01106] ; Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science, Zhejiang University
WOS关键词	A-PRIORI PATHOMETRY ; MODEL ; PROBABILITIES ; SEIR
WOS研究方向	Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000803107800015
资助机构	Zhejiang Provincial Key Research and Development Program of China ; Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science, Zhejiang University
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/49542]
专题	模式识别国家重点实验室_智能交互
通讯作者	Zhao, Ziping; Tao, Jianhua
作者单位	1.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100083, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Huairou 101408, Peoples R China 3.Chinese Acad Sci, Natl Lab Pattern Recognit NLPR, Inst Automat, Beijing 100098, Peoples R China 4.Guangzhou Univ, Cyberspace Inst Adv Technol, Guangzhou 510006, Peoples R China 5.La Trobe Univ, Dept Comp Sci, Melbourne, Vic 3086, Australia 6.Tianjin Normal Univ, Coll Comp & Informat Engn, Tianjin 300382, Peoples R China 7.Shanghai Jiao Tong Univ, Shanghai Chest Hosp, Dept Radiol, Shanghai 200240, Peoples R China 8.Shanghai Jiao Tong Univ, Shanghai Chest Hosp, Dept Radiol, Shanghai 200240, Peoples R China
推荐引用方式 GB/T 7714	Wang, Haishuai,Tao, Guangyu,Ma, Jiali,et al. Predicting the Epidemics Trend of COVID-19 Using Epidemiological-Based Generative Adversarial Networks[J]. IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING,2022,16(2):276-288.
APA	Wang, Haishuai.,Tao, Guangyu.,Ma, Jiali.,Jia, Shangru.,Chi, Lianhua.,...&Tao, Jianhua.(2022).Predicting the Epidemics Trend of COVID-19 Using Epidemiological-Based Generative Adversarial Networks.IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING,16(2),276-288.
MLA	Wang, Haishuai,et al."Predicting the Epidemics Trend of COVID-19 Using Epidemiological-Based Generative Adversarial Networks".IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 16.2(2022):276-288.