Article(id=1242138223674209105, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242138198848118976, articleNumber=null, orderNo=14, doi=10.3981/j.issn.1000-7857.2020.06.012, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1582905600000, receivedDateStr=2020-02-29, revisedDate=1584460800000, revisedDateStr=2020-03-18, acceptedDate=null, acceptedDateStr=null, onlineDate=1589178926887, onlineDateStr=2020-05-11, pubDate=1585324800000, pubDateStr=2020-03-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1589178926887, onlineIssueDateStr=2020-05-11, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774078430332, creator=sys-migrate, updateTime=1774078430332, updator=sys-migrate, issue=Issue{id=1242138198848118976, tenantId=1146029695717560320, journalId=1146031591421210625, year='2020', volume='38', issue='6', pageStart='1', pageEnd='152', issueExtLink='null', onlineDate='null', pubDate='1585324800000', pubDateStr='2020-03-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774078424415, creator='sys-migrate', updateTime=1774078424415, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=83, endPage=89, ext={EN=ArticleExt(id=1242138228317299003, articleId=1242138223674209105, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Application of empirical data assimilation method in trend analysis of COVID-19, columnId=1150494644690366681, journalTitle=Science & Technology Review, columnName=Papers, runingTitle=null, highlight=null, articleAbstract=The method of "empirical data assimilation for the SARS epidemic trend model" is used to assimilate the model parameters based on the new crown epidemic data released by the Health Committee of each city, and our research team's recent work is presented, including the multiple epidemic trend analysis and the decision-making suggestions for the whole country (except Hubei), Hubei Province (except Wuhan) and Wuhan city from February 3 to February 28, 2020. Three prediction curves are shown as the guideline of the epidemic prevention and control to predict the development of the epidemic. The epidemic peak line of the model can be used as a standard line to evaluate whether the current epidemic prevention measures are appropriate and be used for the early warning of the epidemic trend in various cities, to guide the proper taking of the epidemic prevention measures, to provide the decision support for the scheduling of medical resources and emergency supplies for life, and to play a role in stabilizing the public mood., authors=YUAN Hongyong¹, LIANG Manchun¹, HUANG Quanyi¹, SU Guofeng¹, CHEN Tao¹, CHEN Jianguo¹, SUN Zhanhui¹, YANG Sihang¹, DENG Lizheng¹, LI Ke¹, QIN Zesheng², YU Miaomiao¹, CHENG Ming¹, LI Kaiyuan¹, LIU Gang¹, XIAO Xinxin², LI Wenzhang², authorsList=YUAN Hongyong, LIANG Manchun, HUANG Quanyi, SU Guofeng, CHEN Tao, CHEN Jianguo, SUN Zhanhui, YANG Sihang, DENG Lizheng, LI Ke, QIN Zesheng, YU Miaomiao, CHENG Ming, LI Kaiyuan, LIU Gang, XIAO Xinxin, LI Wenzhang, authorCompany=1. Department of Engineering Physics, Tsinghua University, Beijing 100094, China;
2. Environmental Safety Business Division, Beijing Safety Technology, Co., Ltd., Beijing 100094, China, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=E0JER4ltFMFBb00FGZkMnQ==, pdfFileSize=5284912, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1242138227017069407, articleId=1242138223674209105, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=实证数据同化案例方法在新冠肺炎疫情分析中的应用, columnId=1146540929516700224, journalTitle=科技导报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=采用“实证数据同化SARS案例疫情趋势模型”方法，以各地市卫生健康委员会发布的新型冠状病毒肺炎疫情数据为基础同化模型参数，总结研究团队2020年2月3—28日对全国（除湖北）、湖北省（除武汉）、武汉市的多次疫情趋势分析和决策建议，并提供3条预测曲线作为疫情防控指导线，预测疫情发展。该模型的疫情峰值线可作为评估当前防疫措施是否得当的标准线，用于各地市疫情趋势预警，指导防疫措施的制定，为医疗资源、生活应急物资的调度提供决策支持，为稳定民众情绪发挥作用。, authors=袁宏永¹, 梁漫春¹, 黄全义¹, 苏国锋¹, 陈涛¹, 陈建国¹, 孙占辉¹, 杨思航¹, 邓李政¹, 黎岢¹, 秦泽生², 于淼淼¹, 程明¹, 李开远¹, 刘罡¹, 肖鑫鑫², 李文章², authorsList=袁宏永, 梁漫春, 黄全义, 苏国锋, 陈涛, 陈建国, 孙占辉, 杨思航, 邓李政, 黎岢, 秦泽生, 于淼淼, 程明, 李开远, 刘罡, 肖鑫鑫, 李文章, authorCompany=1. 清华大学公共安全研究院, 北京 100094;
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