Using deep learning to predict the hand-foot-and-mouth disease of enterovirus A71 subtype in Beijing from 2011 to 2018

doi:10.1038/s41598-020-68840-3

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 互联网大数据与安全信息学研究中心

	Using deep learning to predict the hand-foot-and-mouth disease of enterovirus A71 subtype in Beijing from 2011 to 2018
	Wang, Yuejiao 1,2; Cao, Zhidong 2; Zeng, Daniel 2; Wang, Xiaoli 3; Wang, Quanyi 3
刊名	SCIENTIFIC REPORTS
	2020-07-22
卷号	10 期号:1 页码:10
ISSN号	2045-2322
DOI	10.1038/s41598-020-68840-3
通讯作者	Cao, Zhidong(zhidong.cao@ia.ac.cn)
英文摘要	Hand-foot-and-month disease (HFMD), especially the enterovirus A71 (EV-A71) subtype, is a major health problem in Beijing, China. Previous studies mainly used regressive models to forecast the prevalence of HFMD, ignoring its intrinsic age groups. This study aims to predict HFMD of EV-A71 subtype in three age groups (0-3, 3-6 and>6 years old) from 2011 to 2018 using residual-convolutional-recurrent neural network (CNNRNN-Res), convolutional-recurrent neural network (CNNRNN) and recurrent neural network (RNN). They were compared with auto-regressio, global auto-regression and vector auto-regression on both short-term and long-term prediction. Results showed that CNNRNN-Res and RNN had higher accuracies on point forecast tasks, as well as robust performances in long-term prediction. Three deep learning models also had better skills in peak intensity forecast, and CNNRNN-Res achieved the best results in the peak month forecast. We also found that three age groups had consistent outbreak trends and similar patterns of prediction errors. These results highlight the superior performance of deep learning models in HFMD prediction and can assist the decision-makers to refine the HFMD control measures according to age groups.
资助项目	National key research and development program[2016YFC1200702] ; National key research and development program[2016QY02D0305] ; National Natural Science Foundation of China[72042018] ; National Natural Science Foundation of China[91546112] ; National Natural Science Foundation of China[71621002]
WOS关键词	TIME-SERIES ; HFMD ; GUANGDONG ; CHINA
WOS研究方向	Science & Technology - Other Topics
语种	英语
出版者	NATURE PUBLISHING GROUP
WOS记录号	WOS:000556401400028
资助机构	National key research and development program ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/40342]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心
通讯作者	Cao, Zhidong
作者单位	1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Beijing Ctr Dis Prevent & Control, Inst Infect Dis & Endem Dis Control, Beijing 100013, Peoples R China
推荐引用方式 GB/T 7714	Wang, Yuejiao,Cao, Zhidong,Zeng, Daniel,et al. Using deep learning to predict the hand-foot-and-mouth disease of enterovirus A71 subtype in Beijing from 2011 to 2018[J]. SCIENTIFIC REPORTS,2020,10(1):10.
APA	Wang, Yuejiao,Cao, Zhidong,Zeng, Daniel,Wang, Xiaoli,&Wang, Quanyi.(2020).Using deep learning to predict the hand-foot-and-mouth disease of enterovirus A71 subtype in Beijing from 2011 to 2018.SCIENTIFIC REPORTS,10(1),10.
MLA	Wang, Yuejiao,et al."Using deep learning to predict the hand-foot-and-mouth disease of enterovirus A71 subtype in Beijing from 2011 to 2018".SCIENTIFIC REPORTS 10.1(2020):10.