A three-way multivariate data analysis: comparison of EU countries’ COVID-19 incidence trajectories from May 2020 to February 2021

J. Tallon, Paulo Gomes, L. Bacelar-Nicolau, Sérgio Bacelar
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引用次数: 2

Abstract

Introduction: About a year and a half after the declaration of the COVID-19 pandemic, almost the entire planet has been affected by SARS-CoV-2 coronavirus and its variants, with serious public health consequences and other repercussions not yet thoroughly evaluated or foreseen in terms of economic, financial and social disruption throughout communities. Therefore, it is of utmost importance to understand the geography of the evolution of successive pandemic waves. Particularly in European countries, where, in recent decades, more advanced models for cohesion and competitiveness of a whole with more than 400 million inhabitants have been achieved, with ambitious challenges for horizon 2030 regarding this vast territory’s economic, social, and environmental sustainability. Objective: The main objective of this research is to describe the multivariate trajectories of COVID-19 incidence, mortality, hospital admissions, ICU admissions and testing, over three successive waves, covering all European Union (EU) countries with more than two million inhabitants, over 14-days periods before May 4 2020, until February 22 2021. Methods: This research includes 22 European countries representing about 98.8% of the EU population, described by six epidemiological variables over 43 time periods from the ECDC database: the 14-day notification rate Biometrics & Biostatistics International Journal Research Article Open Access of new cases reported for 100,000 inhabitants; the 14-day notification rate of reported deaths per one million inhabitants; the mean and the rate for 100,000 population of hospital occupancy and ICU occupancy; the testing rate per 100,000 population; and the 14-days percentage of test positivity An exploratory data analysis of each epidemiological variable identified a typology of countries profiles evolution. Multivariate exploratory statistical methods, namely a 3-way data analysis (double principal components and rank principal components analyses), were applied with software R version 4.1.0. Results: The multivariate evolution profile of the COVID-19 pandemic in the EU over the studied period highlighted 3 phases: the first phase over 24 time periods, with a relatively low COVID-19 incidence, hitting only part of EU countries; a second phase at the beginning of the second wave, when COVID-19 spread to most countries, with a higher impact on national health systems; lastly, a third phase coincident with the peak of the second wave and the onset of the third wave, a particularly reactive phase from the public authorities, with intensified testing of the population. These results are clear from the principal component analysis of the centres of gravity of the 43 time periods (interstructure). The multivariate statistical analysis of the global dataset of all countries over the 43 time periods additionally provides the main factorial representation of the trajectories of COVID-19 for each country in direct comparison with the global average ranked values reached by the six epidemiological variables over the whole period under study (intrastructure). These trajectories make it possible to identify different country profiles throughout the successive pandemic waves and counter-cyclical behaviours, partly explained by the insufficient harmonisation of public policies to tackle the pandemic within the EU.
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三方多变量数据分析:2020年5月至2021年2月欧盟国家新冠肺炎发病轨迹比较
在宣布COVID-19大流行大约一年半后,几乎整个地球都受到了SARS-CoV-2冠状病毒及其变种的影响,在整个社区的经济、金融和社会中断方面造成了严重的公共卫生后果和其他影响,尚未得到彻底评估或预见。因此,了解连续大流行波演变的地理位置至关重要。特别是在欧洲国家,近几十年来,在拥有4亿多居民的整体凝聚力和竞争力方面,已经实现了更先进的模式,这一广阔领土的经济、社会和环境可持续性在2030年面临着雄心勃勃的挑战。目的:本研究的主要目的是描述在2020年5月4日至2021年2月22日的14天内,连续三波覆盖所有人口超过200万的欧盟(EU)国家的COVID-19发病率、死亡率、住院率、ICU入院率和检测的多变量轨迹。方法:本研究包括22个欧洲国家,约占欧盟人口的98.8%,由ECDC数据库中43个时间段的6个流行病学变量描述:每10万居民报告的新病例的14天通报率;每100万居民报告死亡的14天通报率;10万人口医院入住率和ICU入住率均值和比率;每10万人的检测率;对每个流行病学变量的探索性数据分析确定了国家概况演变的类型。采用多元探索性统计方法,即三向数据分析(双主成分分析和秩主成分分析),软件R版本4.1.0。结果:在研究期间,欧盟COVID-19大流行的多变量演变概况突出了3个阶段:第一阶段超过24个时间段,COVID-19发病率相对较低,仅影响部分欧盟国家;在第二波疫情开始时进入第二阶段,此时COVID-19传播到大多数国家,对国家卫生系统的影响更大;最后,第三阶段恰逢第二波的高峰和第三波的开始,这是公共当局特别积极的阶段,加强了对人口的检测。从43个时间段(基础结构)重心的主成分分析可以清楚地看出这些结果。对43个时间段内所有国家的全球数据集进行的多变量统计分析还提供了每个国家COVID-19轨迹的主要因子表示,直接与整个研究期间(基础设施)六个流行病学变量达到的全球平均排名值进行比较。这些轨迹使我们能够在连续的大流行病浪潮和反周期行为中确定不同的国家概况,部分原因是欧盟内部应对大流行病的公共政策没有充分协调。
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