COVID index

  • 文章类型: Journal Article
    关于不同国家如何应对COVID-19大流行,一直存在激烈的辩论。为了确保公共安全,韩国在个人隐私风险下积极使用个人信息,而法国在公共安全风险下鼓励自愿合作。在这篇文章中,在与法国进行了上下文差异的简短比较之后,我们关注韩国的流行病学调查方法。为了评估与个人隐私和公共卫生有关的问题,我们检查原始数据的使用模式,去识别数据,和加密的数据。我们的具体建议讨论了COVID指数,考虑到集体感染,爆发强度,医疗基础设施的可用性,和死亡率。最后,我们总结了未来研究的发现和教训以及政策含义。
    There has been vigorous debate on how different countries responded to the COVID-19 pandemic. To secure public safety, South Korea actively used personal information at the risk of personal privacy whereas France encouraged voluntary cooperation at the risk of public safety. In this article, after a brief comparison of contextual differences with France, we focus on South Korea\'s approaches to epidemiological investigations. To evaluate the issues pertaining to personal privacy and public health, we examine the usage patterns of original data, de-identification data, and encrypted data. Our specific proposal discusses the COVID index, which considers collective infection, outbreak intensity, availability of medical infrastructure, and the death rate. Finally, we summarize the findings and lessons for future research and the policy implications.
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  • 文章类型: Journal Article
    评估3种新的基于肺部超声(LUS)的参数的有效性:肺炎评分和肺分期肺炎分期和COVID指数,表明SARS-CoV-2感染的可能性。
    急诊收治的有潜在肺炎相关症状的成年患者,对健康志愿者和来自在线可访问数据库的临床病例进行了评估.患者接受了临床流行病学调查问卷和LUS采集,遵循14区协议。对于每个区域,根据识别出的影像学征象,算法和专家操作员(对算法结果保持盲态)将肺炎得分为0~4分,并得出患者肺部分期作为观察到的最高得分.操作员的输出被认为是基础事实。该算法还通过将自动识别的LUS标记与问卷答案相结合来计算COVID指数,并与鼻咽拭子结果进行比较。
    总的来说,对556例患者进行了分析。观察到算法分配和专家操作员评估之间的高度一致性,肺炎评分和肺分期,后者在区分健康/患病患者和轻度/重度肺炎患者之间具有超过92%的敏感性和特异性。关于COVID指数,对于有/没有SARS-CoV-2的患者的分类,曲线下面积为0.826。
    所提出的方法可以高精度地识别和分期患有肺炎的患者。此外,它提供了被SARS-CoV-2感染的可能性。
    To assess the effectiveness of 3 novel lung ultrasound (LUS)-based parameters: Pneumonia Score and Lung Staging for pneumonia staging and COVID Index, indicating the probability of SARS-CoV-2 infection.
    Adult patients admitted to the emergency department with symptoms potentially related to pneumonia, healthy volunteers and clinical cases from online accessible databases were evaluated. The patients underwent a clinical-epidemiological questionnaire and a LUS acquisition, following a 14-zone protocol. For each zone, a Pneumonia score from 0 to 4 was assigned by the algorithm and by an expert operator (kept blind with respect to the algorithm results) on the basis of the identified imaging signs and the patient Lung Staging was derived as the highest observed score. The output of the operator was considered as the ground truth. The algorithm calculated also the COVID Index by combining the automatically identified LUS markers with the questionnaire answers and compared with the nasopharyngeal swab results.
    Overall, 556 patients were analysed. A high agreement between the algorithm assignments and the expert operator evaluations was observed, both for Pneumonia Score and Lung Staging, with the latter having sensitivity and specificity over 92% both in the discrimination between healthy/sick patients and between sick patients with mild/severe pneumonia. Regarding the COVID Index, an area under the curve of 0.826 was observed for the classification of patients with/without SARS-CoV-2.
    The proposed methodology allowed the identification and staging of patients suffering from pneumonia with high accuracy. Moreover, it provided the probability of being infected by SARS-CoV-2.
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