Creativ-Ceutical, ul. Przemysłowa 12, Kraków, 30-701, Poland.
Creativ-Ceutical, Westblaak 90, Rotterdam, 3012KM, Netherlands.
Public Health. 2021 May;194:135-142. doi: 10.1016/j.puhe.2021.02.037. Epub 2021 Mar 20.
The purpose of this study was to determine predictors of the height of coronavirus disease 2019 (COVID-19) daily deaths' peak and time to the peak, to explain their variability across European countries.
For 34 European countries, publicly available data were collected on daily numbers of COVID-19 deaths, population size, healthcare capacity, government restrictions and their timing, tourism and change in mobility during the pandemic.
Univariate and multivariate generalised linear models using different selection algorithms (forward, backward, stepwise and genetic algorithm) were analysed with height of COVID-19 daily deaths' peak and time to the peak as dependent variables.
The proportion of the population living in urban areas, mobility at the day of first reported death and number of infections when borders were closed were assessed as significant predictors of the height of COVID-19 daily deaths' peak. Testing the model with a variety of selection algorithms provided consistent results. Total hospital bed capacity, population size, the number of foreign travellers and the day of border closure were found to be significant predictors of time to COVID-19 daily deaths' peak.
Our analysis demonstrated that countries with higher proportions of the population living in urban areas, countries with lower reduction in mobility at the beginning of the pandemic and countries having more infected people when closing borders experienced a higher peak of COVID-19 deaths. Greater bed capacity, bigger population size and later border closure could result in delaying time to reach the deaths' peak, whereas a high number of foreign travellers could accelerate it.
本研究旨在确定 2019 年冠状病毒病(COVID-19)每日死亡人数峰值及其达到峰值的时间的预测因素,以解释其在欧洲各国之间的差异。
针对 34 个欧洲国家,收集了 COVID-19 每日死亡人数、人口规模、医疗能力、政府限制及其实施时间、旅游业以及大流行期间流动性变化等方面的公开数据。
使用不同选择算法(前向、后向、逐步和遗传算法)对单变量和多变量广义线性模型进行分析,以 COVID-19 每日死亡人数峰值及其达到峰值的时间为因变量。
居住在城市地区的人口比例、首次报告死亡日的流动性以及边境关闭时的感染人数被评估为 COVID-19 每日死亡人数峰值的显著预测因素。使用各种选择算法对模型进行测试,结果一致。总住院床位容量、人口规模、外国旅行者人数和边境关闭日是 COVID-19 每日死亡人数达到峰值时间的显著预测因素。
我们的分析表明,居住在城市地区的人口比例较高、大流行初期流动性降低幅度较低以及在关闭边境时感染人数较多的国家,COVID-19 死亡人数的峰值更高。更大的床位容量、更大的人口规模和较晚的边境关闭可能会延迟达到死亡人数峰值的时间,而大量外国旅行者则可能会加速这一过程。