COVID-19的Kaplan-Meier生存曲线：一种基于健康数据的决策工具
Kaplan-Meier type survival curves for COVID-19: a health data based decision-making tool
摘要：
各国正在使用不同的方法记录有关COVID-19全球传播的健康信息，有时几天后就会改变规则。他们都公布了新感染、治愈和死亡的人数，以及一些补充数据。这些数字的记录方式往往不统一，与这些变量的标准定义不符。然而，在本文中，我们表明，用它们计算的Kaplan-Meier曲线可以提供有关不同国家疾病动态的有用信息。我们的目的是提出一个强大的和简单的模型来显示动态过程的演变的某些特征，显示国家之间的演变的差异反映在相应的Kaplan-Meier型曲线。我们比较了迄今为止受影响最严重的国家获得的曲线，提出了区分它们的属性的可能解释。
英文摘要：
Countries are recording health information on the global spread of COVID-19 using different methods, sometimes changing the rules after a few days. They are all publishing the number of new individuals infected, cured and dead, along with some supplementary data. These figures are often recorded in a non-uniform manner and do not match the standard definitions of these variables. However, in this paper we show that the Kaplan-Meier curves calculated with them could provide useful information about the dynamics of the disease in different countries. Our aim is to present a robust and simple model to show certain characteristics of the evolution of the dynamic process, showing that the differences of evolution among the countries is reflected in the corresponding Kaplan-Meier-type curves. We compare the curves obtained for the most affected countries so far, proposing possible interpretations of the properties that distinguish them.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
