Cerqueti Roy, Ficcadenti Valerio
Sapienza University of Rome, Department of Social and Economic Sciences, Piazzale Aldo Moro, 5, 00185 Rome, Italy.
London South Bank University, Business School, Borough Road, 103, SE1 0AA London, United Kingdom.
Chaos Solitons Fractals. 2022 May;158:111975. doi: 10.1016/j.chaos.2022.111975. Epub 2022 Mar 11.
This paper deals with the cluster analysis of selected countries based on COVID-19 new deaths per million data. We implement a statistical procedure that combines a rank-size exploration and a -means approach for clustering. Specifically, we first carry out a best-fit exercise on a suitable polynomial rank-size law at an individual country level; then, we cluster the considered countries by adopting a -means clustering procedure based on the calibrated best-fit parameters. The investigated countries are selected considering those with a high value for the Healthcare Access and Quality Index to make a consistent analysis and reduce biases from the data collection phase. Interesting results emerge from the meaningful interpretation of the parameters of the best-fit curves; in particular, we show some relevant properties of the considered countries when dealing with the days with the highest number of new daily deaths per million and waves. Moreover, the exploration of the obtained clusters allows explaining some common countries' features.
本文基于每百万人口的新冠新增死亡数据对选定国家进行聚类分析。我们实施了一种统计程序，该程序结合了位序 - 规模探索和用于聚类的k均值方法。具体而言，我们首先在单个国家层面针对合适的多项式位序 - 规模法则进行最佳拟合操作；然后，我们基于校准后的最佳拟合参数，采用k均值聚类程序对所考虑的国家进行聚类。考虑到医疗保健可及性和质量指数较高的国家，从而进行连贯分析并减少数据收集阶段的偏差，以此选定被调查国家。对最佳拟合曲线参数的有意义解释得出了有趣的结果；特别是，我们展示了所考虑国家在处理每百万人口每日新增死亡人数最多的日子和疫情波时的一些相关特性。此外，对所得聚类的探索有助于解释一些常见的国家特征。
