《MedRxiv,3月10日,Data-driven discovery of clinical routes for severity detection in COVID-19 pediatric cases》

  • 来源专题:COVID-19科研动态监测
  • 编译者: zhangmin
  • 发布时间:2020-03-11
  • Data-driven discovery of clinical routes for severity detection in COVID-19 pediatric cases

    Hui Yu, Jianbo Shao, Yuqi Guo, Yun Xiang, Chuan Sun, Hai-Tao Zhang, Ye Yuan

    doi: https://doi.org/10.1101/2020.03.09.20032219

    Abstract

    The outbreak of COVID-19 epidemic has caused worldwide health concerns since Nov., 2019. A previous study described the demographic, epidemiologic, and clinical features for infected infants. However, compared with adult cases, little attention has been paid to the infected pediatric cases. Severity detection is challenging for children since most of children patients have mild symptoms no matter they are moderately or critically ill therein.

    *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/10.1101/2020.03.09.20032219v1
相关报告
  • 《MedRxiv,3月10日,Prediction of COVID-19 Spreading Profiles in South Korea, Italy and Iran by Data-Driven Coding》

    • 来源专题:COVID-19科研动态监测
    • 编译者:zhangmin
    • 发布时间:2020-03-11
    • Prediction of COVID-19 Spreading Profiles in South Korea, Italy and Iran by Data-Driven Coding Choujun Zhan, Chi K. Tse, Zhikang Lai, Tianyong Hao, Jingjing Su doi: https://doi.org/10.1101/2020.03.08.20032847 Abstract This work applies a data-driven coding method for prediction of the COVID-19 spreading profile in any given population that shows an initial phase of epidemic progression. Based on the historical data collected for COVID-19 spreading in 367 cities in China and the set of parameters of the augmented Susceptible-Exposed-Infected-Removed (SEIR) model obtained for each city, a set of profile codes representing a variety of transmission mechanisms and contact topologies is formed. By comparing the data of an early outbreak of a given population with the complete set of historical profiles, the best fit profiles are selected and the corresponding sets of profile codes are used for prediction of the future progression of the epidemic in that population. *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.
  • 《MedRxiv,3月10日,Clinical Characteristics of 2019 Coronavirus Pneumonia (COVID-19): An Updated Systematic Review》

    • 来源专题:COVID-19科研动态监测
    • 编译者:zhangmin
    • 发布时间:2020-03-11
    • Clinical Characteristics of 2019 Coronavirus Pneumonia (COVID-19): An Updated Systematic Review Zhangfu Fang, Fang Yi, Kang Wu, Kefang Lai, Xizhuo Sun, Nanshan Zhong, Zhigang Liu doi: https://doi.org/10.1101/2020.03.07.20032573 Abstract OBJECTIVE Clinical characteristics of novel coronavirus pneumonia (COVID-19) have been described in numerous studies but yielded varying results. We aimed to conduct a systematic review on scientific literatures and to synthesize critical data on clinical traits of COVID-19 from its initial outbreak to pandemic. METHODS Systematic searches were conducted to identify retrospective observational study that contained clinical characteristics on COVID-19 through multiple databases. Two reviewers independently evaluated eligible publications. Data on clinical characteristics of COVID-19 were extracted and analyzed. *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.