Data_Sheet_1_Kaplan-Meier Type Survival Curves for COVID-19: A Health Data Based Decision-Making Tool.ZIP

Countries are recording health information on the global spread of COVID-19 using different methods, sometimes changing the rules after a few days. All of them are publishing the number of new individuals infected, recovered and dead individuals, along with some supplementary material. These data are often recorded in a non-uniform manner and do not conform the standard definitions of these variables. In this paper we show that, using data from the first wave of the epidemic (February-June), Kaplan-Meier curves calculated with them could provide useful information on the dynamics of the disease in different countries. We developed our scheme based on the cumulative total number of infected, recovered and dead individuals provided by the countries. We present a robust and simple model to show certain characteristics of the evolution of the dynamic process, showing that the differences in evolution between countries are reflected in the corresponding Kaplan-Meier-type curves. We compare the curves obtained for the most affected countries at that time, with the corresponding interpretation of the properties that distinguish them. The model is revealed as a practical tool for countries in the management of the Healthcare System.

各国采用不同方法记录新冠病毒（COVID-19）全球传播的健康相关信息，且有时会在数日内调整统计规则。所有国家均会公布新增感染者、康复者及死亡者人数，并附带部分补充材料。但此类数据往往采用非统一标准记录，且未遵循上述变量的标准定义。本研究表明，利用疫情第一波（2月至6月）的相关数据，基于这些数据绘制的Kaplan-Meier曲线（Kaplan-Meier Curve）能够为不同国家的新冠疫情传播动态提供有价值的参考信息。本研究基于各国公布的累计感染者、康复者及死亡者总数构建了分析框架。我们提出了一种稳健且简洁的模型，用以展现疫情动态演化过程的部分特征，结果表明各国疫情演化的差异可通过对应的类Kaplan-Meier曲线体现。我们对当时疫情最严重的国家所对应的曲线进行了对比，并对区分各国疫情特征的属性做出了相应解读。该模型可作为各国医疗体系管理的实用工具。