Visualizing omicron: COVID-19 deaths vs. cases over time.
Visualizing omicron: COVID-19 deaths vs. cases over time.
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DOI：
10.1371/journal.pone.0265233
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发表时间：
2022
期刊：
影响因子：
3.7
通讯作者：
中科院分区：
文献类型：
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作者：
在COVID-19大流行的大部分时间里，每日关注的重点是病例数，其次是死亡人数。最近的一波是由omicron变异引起的，该变异于2021年底首次发现，并在2022年上半年成为主导变异。南非是最早经历和报告omicron（变异体21.K）数据的国家之一，报告的死亡人数少得多，尽管报告的病例数迅速超过了以前的高峰。然而，随着omicron波的发展，时间序列显示它与以前的波有明显的不同。为了更直观地显示病例和死亡的动态，很自然地将每百万人的死亡率与每百万人的病例数相比较。与病例或死亡的时间序列图不同，在大流行期间，病例或死亡的时间序列图已成为大流行更新的每日特征，时间作为x轴，在死亡与病例的图中，时间是隐含的，并与起点相关。在这里，我们将介绍并简要分析来自一些国家和整个世界的这些图表，说明它们如何总结大流行的特征，说明在大多数地方，omicron波与以前的大流行有多大的不同。用于生成任何国家的这些图的代码在自动更新的GitHub存储库中提供。
For most of the COVID-19 pandemic, the daily focus has been on the number of cases, and secondarily, deaths. The most recent wave was caused by the omicron variant, first identified at the end of 2021 and the dominant variant through the first part of 2022. South Africa, one of the first countries to experience and report data regarding omicron (variant 21.K), reported far fewer deaths, even as the number of reported cases rapidly eclipsed previous peaks. However, as the omicron wave has progressed, time series show that it has been markedly different from prior waves. To more readily visualize the dynamics of cases and deaths, it is natural to plot deaths per million against cases per million. Unlike the time-series plots of cases or deaths that have become daily features of pandemic updates during the pandemic, which have time as the x-axis, in a plot of deaths vs. cases, time is implicit, and is indicated in relation to the starting point. Here we present and briefly examine such plots from a number of countries and from the world as a whole, illustrating how they summarize features of the pandemic in ways that illustrate how, in most places, the omicron wave is very different from those that came before. Code for generating these plots for any country is provided in an automatically updating GitHub repository.
影响因子：
3.5
作者：
Papanikolaou V;Chrysovergis A;Ragos V;Tsiambas E;Katsinis S;Manoli A;Papouliakos S;Roukas D;Mastronikolis S;Peschos D;Batistatou A;Kyrodimos E;Mastronikolis N
通讯作者：
Mastronikolis N
影响因子：
64.8
作者：
Hui, Kenrie P. Y.;Ho, John C. W.;Chan, Michael C. W.
通讯作者：
Chan, Michael C. W.
影响因子：
12.7
作者：
通讯作者：
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