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

doi:10.1109/JSTSP.2022.3152375

	Predicting the Epidemics Trend of COVID-19 Using Epidemiological-Based Generative Adversarial Networks
	Wang, Haishuai 1; Tao, Guangyu 2; Ma, Jiali 3; Jia, Shangru 3; Chi, Lianhua 4; Yang, Hong 5; Zhao, Ziping 3; Tao, Jianhua6,7,8
发表期刊	IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
ISSN	1932-4553
	2022-02-01
卷号	16 期号:2 页码:276-288
通讯作者	Zhao, Ziping(ztianjin@126.com) ; Tao, Jianhua(jhtao@nlpr.ia.ac.cn)
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.
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
DOI	10.1109/JSTSP.2022.3152375
关键词[WOS]	A-PRIORI PATHOMETRY ; MODEL ; PROBABILITIES ; SEIR
收录类别	SCI
语种	英语
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
项目资助者	Zhejiang Provincial Key Research and Development Program of China ; Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science, Zhejiang University
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:000803107800015
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：15[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/49543
专题	多模态人工智能系统全国重点实验室_智能交互
通讯作者	Zhao, Ziping; Tao, Jianhua
作者单位	1.Shanghai Jiao Tong Univ, Shanghai Chest Hosp, Dept Radiol, Shanghai 200240, Peoples R China 2.Shanghai Jiao Tong Univ, Shanghai Chest Hosp, Dept Radiol, Shanghai 200240, Peoples R China 3.Tianjin Normal Univ, Coll Comp & Informat Engn, Tianjin 300382, Peoples R China 4.La Trobe Univ, Dept Comp Sci, Melbourne, Vic 3086, Australia 5.Guangzhou Univ, Cyberspace Inst Adv Technol, Guangzhou 510006, Peoples R China 6.Chinese Acad Sci, Natl Lab Pattern Recognit NLPR, Inst Automat, Beijing 100098, Peoples R China 7.Univ Chinese Acad Sci, Sch Artificial Intelligence, Huairou 101408, Peoples R China 8.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100083, Peoples R China
通讯作者单位	模式识别国家重点实验室
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.