Article(id=1242139611892360019, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242139591197664016, articleNumber=null, orderNo=17, doi=10.3981/j.issn.1000-7857.2021.24.011, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1630512000000, receivedDateStr=2021-09-02, revisedDate=1632931200000, revisedDateStr=2021-09-30, acceptedDate=null, acceptedDateStr=null, onlineDate=1641631392150, onlineDateStr=2022-01-08, pubDate=1640620800000, pubDateStr=2021-12-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1641631392150, onlineIssueDateStr=2022-01-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774078761309, creator=sys-migrate, updateTime=1774078761309, updator=sys-migrate, issue=Issue{id=1242139591197664016, tenantId=1146029695717560320, journalId=1146031591421210625, year='2021', volume='39', issue='24', pageStart='0', pageEnd='157', issueExtLink='null', onlineDate='null', pubDate='1640620800000', pubDateStr='2021-12-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774078756376, creator='sys-migrate', updateTime=1774078756376, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=96, endPage=107, ext={EN=ArticleExt(id=1242139616841642251, articleId=1242139611892360019, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Cross-region smart COVID-19 pandemic management and control based on federated learning: With Shanghai as an example, columnId=1150494644690366681, journalTitle=Science & Technology Review, columnName=Papers, runingTitle=null, highlight=null, articleAbstract=SARS-CoV-2 and its variants, the viruses that cause the COVID-19 pandemic, have some new characteristics, such as the high transmissibility, the long incubation period, the sweeping susceptible population, and the high environmental endurance, so a key question of the pandemic management and control is monitoring the asymptomatic transmission in daily life and socioeconomic activities, especially, the wide-range of cross-region spread. These features pose a great challenge to the traditional pandemic management and control methods and the global public health surveillance and control system. An effective way to tackle this challenge is making full use of the digital technology in the pandemic management and control, and building an accurate regular epidemiological surveillance and smart pandemic management and control system. By analyzing the essential factors of a smart pandemic management and control system, the critical role of the federated learning in the practice of the cross-region and cross-department smart pandemic management and control is shown. According to the classifications of the cross-region pandemic-related data and the pandemic management and control requirements, the technology and the application of the cross-region smart pandemic management and control based on the federated learning are explored. This method is successfully applied in the COVID-19 pandemic management and control in Shanghai and Yangtze River Delta, providing a new pathway for the integrated decision-making and targeted pandemic management and control in China. This method is also instrumental for other countries in the pandemic management and control., authors=QIAN Xuesheng1,2,3 , WU Huanyu4 , CHEN Cheng5 , HUANG Xiaoyan4 , TONG Qing5 , DAI Weihui1,2 , authorsList=QIAN Xuesheng, WU Huanyu, CHEN Cheng, HUANG Xiaoyan, TONG Qing, DAI Weihui, authorCompany=1. Smart City Research Center, Fudan University, Shanghai 200433, China;1,2,3 , 吴寰宇4 , 陈诚5 , 黄晓燕4 , 童庆5 , 戴伟辉1,2 , authorsList=钱学胜, 吴寰宇, 陈诚, 黄晓燕, 童庆, 戴伟辉, authorCompany=1. 复旦大学智慧城市研究中心, 上海 200433;
科技导报
| 研究论文 2021, 39(24): 96-107
基于联邦学习的新冠肺炎疫情跨区域智慧防控技术——以上海市实践为例
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钱学胜1,2,3 , 吴寰宇4 , 陈诚5 , 黄晓燕4 , 童庆5 , 戴伟辉1,2
作者信息
1. 复旦大学智慧城市研究中心, 上海 200433;
通讯作者:
吴寰宇（通信作者），主任医师，研究方向为传染病防治与公共卫生管理，电子信箱：wuhuanyu@scdc.sh.cn
Cross-region smart COVID-19 pandemic management and control based on federated learning: With Shanghai as an example
Affiliations
出版时间: 2021-12-28
doi: 10.3981/j.issn.1000-7857.2021.24.011
文章导航
新冠肺炎病毒不断变异的毒株的系列新特征，给传统的传染病防控方法与公共卫生防控体系带来了巨大的挑战。充分发挥数字技术在抗疫过程中的关键赋能价值，全面构建精准的常态化监测预警及智慧防控体系，系应对上述挑战的有效途径。通过对疫情智慧防控体系构成要素的剖析，揭示联邦学习对建立跨区域、跨部门的智慧疫情防控体系的关键路径作用。并基于跨区域涉疫数据分类及防控工作要点，研究设计了基于联邦学习的跨区域疫情智慧防控技术及其平台应用。该应用已在上海市以及长三角区域的抗疫实践中取得了显著成效。
疫情联防联控
/
智慧防控
/
智慧城市
/
城市数字化转型
SARS-CoV-2 and its variants, the viruses that cause the COVID-19 pandemic, have some new characteristics, such as the high transmissibility, the long incubation period, the sweeping susceptible population, and the high environmental endurance, so a key question of the pandemic management and control is monitoring the asymptomatic transmission in daily life and socioeconomic activities, especially, the wide-range of cross-region spread. These features pose a great challenge to the traditional pandemic management and control methods and the global public health surveillance and control system. An effective way to tackle this challenge is making full use of the digital technology in the pandemic management and control, and building an accurate regular epidemiological surveillance and smart pandemic management and control system. By analyzing the essential factors of a smart pandemic management and control system, the critical role of the federated learning in the practice of the cross-region and cross-department smart pandemic management and control is shown. According to the classifications of the cross-region pandemic-related data and the pandemic management and control requirements, the technology and the application of the cross-region smart pandemic management and control based on the federated learning are explored. This method is successfully applied in the COVID-19 pandemic management and control in Shanghai and Yangtze River Delta, providing a new pathway for the integrated decision-making and targeted pandemic management and control in China. This method is also instrumental for other countries in the pandemic management and control.
joint pandemic prevention and control
/
smart pandemic management and control
/
smart city
/
urban digital transformation
钱学胜, 吴寰宇, 陈诚, 黄晓燕, 童庆, 戴伟辉.
基于联邦学习的新冠肺炎疫情跨区域智慧防控技术——以上海市实践为例.
科技导报,
2021
, 39
(24)
: 96
-107
.
DOI: 10.3981/j.issn.1000-7857.2021.24.011
QIAN Xuesheng, WU Huanyu, CHEN Cheng, HUANG Xiaoyan, TONG Qing, DAI Weihui.
Cross-region smart COVID-19 pandemic management and control based on federated learning: With Shanghai as an example[J].
Science & Technology Review ,
2021
, 39
(24)
: 96
-107
.
DOI: 10.3981/j.issn.1000-7857.2021.24.011
2021年第39卷第24期
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文章信息
doi: 10.3981/j.issn.1000-7857.2021.24.011
接收时间：2021-09-02
首发时间：2022-01-08
出版时间：2021-12-28
收稿日期：2021-09-02
修回日期：2021-09-30
通讯作者:
吴寰宇（通信作者），主任医师，研究方向为传染病防治与公共卫生管理，电子信箱：wuhuanyu@scdc.sh.cn
https://castjournals.cast.org.cn/joweb/kjdb/CN/10.3981/j.issn.1000-7857.2021.24.011
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2种不同金属材料的力学参数
科 属数 种数 占总种数比例 属 种数 占总种数比例 鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78 小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39 多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39 红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87 小菇属 Mycena 11 5.26 光柄菇属 Pluteus 5 2.39 红菇属 Russula 17 8.13 栓菌属 Trametes 5 2.39
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