School of Mathematical Sciences, Sunway University, Selangor, Malaysia.
School of Engineering and Technology, Sunway University, Selangor, Malaysia.
PLoS One. 2021 May 28;16(5):e0252273. doi: 10.1371/journal.pone.0252273. eCollection 2021.
The aim of the study was to visualize the global spread of the COVID-19 pandemic over the first 90 days, through the principal component analysis approach of dimensionality reduction.
This study used data from the Global COVID-19 Index provided by PEMANDU Associates. The sample, representing 161 countries, comprised the number of confirmed cases, deaths, stringency indices, population density and GNI per capita (USD). Correlation matrices were computed to reveal the association between the variables at three time points: day-30, day-60 and day-90. Three separate principal component analyses were computed for similar time points, and several standardized plots were produced.
Confirmed cases and deaths due to COVID-19 showed positive but weak correlation with stringency and GNI per capita. Through principal component analysis, the first two principal components captured close to 70% of the variance of the data. The first component can be viewed as the severity of the COVID-19 surge in countries, whereas the second component largely corresponded to population density, followed by GNI per capita of countries. Multivariate visualization of the two dominating principal components provided a standardized comparison of the situation in the161 countries, performed on day-30, day-60 and day-90 since the first confirmed cases in countries worldwide.
Visualization of the global spread of COVID-19 showed the unequal severity of the pandemic across continents and over time. Distinct patterns in clusters of countries, which separated many European countries from those in Africa, suggested a contrast in terms of stringency measures and wealth of a country. The African continent appeared to fare better in terms of the COVID-19 pandemic and the burden of mortality in the first 90 days. A noticeable worsening trend was observed in several countries in the same relative time frame of the disease's first 90 days, especially in the United States of America.
本研究旨在通过降维主成分分析方法，直观呈现 COVID-19 大流行在最初 90 天内的全球传播情况。
本研究使用了 PEMANDU 协会提供的全球 COVID-19 指数数据。该样本代表了 161 个国家，包含确诊病例、死亡人数、严格程度指数、人口密度和人均国民总收入（美元）。通过计算相关矩阵，揭示了三个时间点（第 30 天、第 60 天和第 90 天）变量之间的相关性。对类似的时间点进行了三次独立的主成分分析，并生成了多个标准化图。
COVID-19 的确诊病例和死亡人数与严格程度和人均国民总收入呈正相关，但相关性较弱。通过主成分分析，前两个主成分捕获了数据方差的近 70%。第一个主成分可以看作是各国 COVID-19 疫情严重程度，而第二个主成分主要对应于国家的人口密度，其次是人均国民总收入。对两个主要成分进行多元可视化，提供了自全球各国首例确诊病例以来的第 30、60 和 90 天，对 161 个国家的情况进行了标准化比较。
COVID-19 的全球传播情况显示，大流行在各大洲和不同时间的严重程度存在差异。国家集群中存在明显的模式，将许多欧洲国家与非洲国家区分开来，表明国家的严格程度措施和财富存在差异。非洲大陆在 COVID-19 大流行和前 90 天内的死亡率方面表现较好。在疾病的前 90 天的同一相对时间框架内，观察到一些国家的趋势明显恶化，尤其是美国。
