登录
 机构网站
  1. 首页
  2. 情报产品
  3. 快讯详情

《MedRxiv，3月17日，A Cybernetics-based Dynamic Infection Model for Analyzing SARS-COV-2 Infection Stability and Predicting Uncontrollable Risks》

  • 来源专题：COVID-19科研动态监测
  • 编译者： zhangmin
  • 发布时间：2020-03-18

  • A Cybernetics-based Dynamic Infection Model for Analyzing SARS-COV-2 Infection Stability and Predicting Uncontrollable Risks

    Wenlei Xiao, Qiang Liu, J Huan, Pengpeng Sun, Liuquan Wang, Chenxin Zang, Sanying Zhu, Liansheng Gao

    doi: https://doi.org/10.1101/2020.03.13.20034082

    Abstract

    Since December 2019, COVID-19 has raged in Wuhan and subsequently all over China and the world. We propose a Cybernetics-based Dynamic Infection Model (CDIM) to the dynamic infection process with a probability distributed incubation delay and feedback principle. Reproductive trends and the stability of the SARS-COV-2 infection in a city can then be analyzed, and the uncontrollable risks can be forecasted before they really happen. The infection mechanism of a city is depicted using the philosophy of cybernetics and approaches of the control engineering.

    *注，本文为预印本论文手稿，是未经同行评审的初步报告，其观点仅供科研同行交流，并不是结论性内容，请使用者谨慎使用.

  • 原文来源：https://www.medrxiv.org/content/10.1101/2020.03.13.20034082v1
16浏览量
原文链接
相关报告

  • 《MedRixv，3月3日，Machine learning-based CT radiomics model for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: A multicenter study》
    • 来源专题：COVID-19科研动态监测
    • 编译者：zhangmin
    • 发布时间：2020-03-04
    • Machine learning-based CT radiomics model for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: A multicenter study Xiaolong Qi, Zicheng Jiang, QIAN YU, Chuxiao Shao, Hongguang Zhang, Hongmei Yue, Baoyi Ma, Yuancheng Wang, Chuan Liu, Xiangpan Meng, Shan Huang, Jitao Wang, Dan Xu, Junqiang Lei, Guanghang Xie, Huihong Huang, Jie Yang, Jiansong Ji, Hongqiu Pan, Shengqiang Zou, Shenghong Ju doi: https://doi.org/10.1101/2020.02.29.20029603 Abstract Abstract Objectives To develop and test machine learning-based CT radiomics models for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection. Design Cross-sectional Setting Multicenter Participants A total of 52 patients with laboratory-confirmed SARS-CoV-2 infection and their initial CT images were enrolled from 5 designated hospitals in Ankang, Lishui, Zhenjiang, Lanzhou, and Linxia between January 23, 2020 and February 8, 2020. *注，本文为预印本论文手稿，是未经同行评审的初步报告，其观点仅供科研同行交流，并不是结论性内容，请使用者谨慎使用.
    12浏览量
    原文链接

  • 《LANCET，3月17日，Prevention of SARS-CoV-2 infection in patients with decompensated cirrhosis》
    • 来源专题：COVID-19科研动态监测
    • 编译者：zhangmin
    • 发布时间：2020-03-18
    • Prevention of SARS-CoV-2 infection in patients with decompensated cirrhosis Yong Xiao Hong Pan Qian She Fen Wang Mingkai Chen Published:March 17, 2020DOI:https://doi.org/10.1016/S2468-1253(20)30080-7 We read the Comment by Chao Zhang and colleagues1 in The Lancet Gastroenterology & Hepatology on liver injury in coronavirus disease 2019 (COVID-19) with great interest. Given that patients with decompensated cirrhosis have a higher risk of, and mortality from, infection, preventing infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in this patient population is a challenging task.2 We provide our experience of COVID-19 prevention in patients with decompensated cirrhosis in Wuhan, China.
    26浏览量
    原文链接