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Application of empirical data assimilation method in trend analysis of COVID-19

  • 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
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  • 1. Department of Engineering Physics, Tsinghua University, Beijing 100094, China;
    2. Environmental Safety Business Division, Beijing Safety Technology, Co., Ltd., Beijing 100094, China

Received date: 2020-02-29

  Revised date: 2020-03-18

  Online published: 2020-05-11

Abstract

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.

Cite this article

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 . Application of empirical data assimilation method in trend analysis of COVID-19[J]. Science & Technology Review, 2020 , 38(6) : 83 -89 . DOI: 10.3981/j.issn.1000-7857.2020.06.012

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