《LANCET,2月19日,An interactive web-based dashboard to track COVID-19 in real time》

  • 来源专题:COVID-19科研动态监测
  • 编译者: zhangmin
  • 发布时间:2020-02-20
  • An interactive web-based dashboard to track COVID-19 in real time

    Ensheng Dong, Hongru Du, Lauren Gardner

    Published:February 19, 2020DOI:https://doi.org/10.1016/S1473-3099(20)30120-1

    In December, 2019, a local outbreak of pneumonia of initially unknown cause was detected in Wuhan (Hubei, China), and was quickly determined to be caused by a novel coronavirus,1 namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak has since spread to every province of mainland China as well as 27 other countries and regions, with more than 70?000 confirmed cases as of Feb 17, 2020.2 In response to this ongoing public health emergency, we developed an online interactive dashboard, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA, to visualise and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds. All data collected and displayed are made freely available, initially through Google Sheets and now through a GitHub repository, along with the feature layers of the dashboard, which are now included in the Esri Living Atlas.

  • 原文来源:https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30120-1/fulltext
相关报告
  • 《JAMIA,4月25日,An Interactive Online Dashboard for Tracking COVID-19 in U.S. Counties, Cities, and States in Real Time》

    • 来源专题:COVID-19科研动态监测
    • 编译者:xuwenwhlib
    • 发布时间:2020-04-26
    • ACCEPTED MANUSCRIPT An Interactive Online Dashboard for Tracking COVID-19 in U.S. Counties, Cities, and States in Real Time Benjamin D Wissel, BS, P J Van Camp, MD, Michal Kouril, PhD, Chad Weis, MS, Tracy A Glauser, MD, Peter S White, PhD, Isaac S Kohane, MD, PhD, Judith W Dexheimer, PhD Journal of the American Medical Informatics Association, ocaa071, https://doi.org/10.1093/jamia/ocaa071 Abstract Objective To create an online resource that informs the public of COVID-19 outbreaks in their area. Materials and Methods This R Shiny application aggregates data from multiple resources that track COVID-19 and visualizes them through an interactive, online dashboard. Results The web resource, called the COVID-19 Watcher, can be accessed at https://covid19watcher.research.cchmc.org/. It displays COVID-19 data from every county and 188 metropolitan areas in the U.S. Features include rankings of the worst affected areas and auto-generating plots that depict temporal changes in testing capacity, cases, and deaths.
  • 《MedRxiv,2月23日,Generalized anxiety disorder, depressive symptoms and sleep quality during COVID-19 epidemic in China: a web-based cross-sectional survey》

    • 来源专题:COVID-19科研动态监测
    • 编译者:xuwenwhlib
    • 发布时间:2020-02-24
    • Generalized anxiety disorder, depressive symptoms and sleep quality during COVID-19 epidemic in China: a web-based cross-sectional survey Yeen Huang, Ning Zhao doi: https://doi.org/10.1101/2020.02.19.20025395 Abstract Background: China has been severely affected by COVID-19 (Corona Virus Disease 2019) since December, 2019. This study aimed to assess the population mental health burden during the epidemic, and to explore the potential influence factors. Methods: Using a web-based cross-sectional survey, we collected data from 603 self-selected volunteers assessed with demographic information, COVID-19 related knowledge, Generalized Anxiety Disorder-7 (GAD-7), Center for Epidemiology Scale for Depression (CES-D), and Pittsburgh Sleep Quality Index (PSQI). Logistic regression were used to identify influence factors associated with mental health problem. Results: Of the total sample analyzed, the overall prevalence of GAD, depressive symptoms, and sleep quality were 34.0%, 18.1%, and 18.1%, respectively. Young people reported a higher prevalence of depressive symptoms than older people (P=0.024). Compared with other occupational group, healthcare workers have the highest rate of poor sleep quality (P=0.045). Multivariate logistic regression showed that age (< 35 years) and times to focus on the COVID-19 (≥ 3 hours per day) were associated with GAD, and healthcare workers were associated with poor sleep quality. Conclusions: Our study identified a major mental health burden of the public during COVID-19 epidemic in China. Young people, people who spent too much time on the epidemic, and healthcare workers were at high risk for mental illness. Continuous surveillance and monitoring of the psychological consequences for outbreaks should become routine as part of preparedness efforts worldwide. *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.