Gonçalves Alan D S, Fernandes Leonardo H S, Nascimento Abraão D C
Departamento de Estatssca, Universidade Federal de Pernambuco, Recife, PE, 50670-901, Brazil.
Department of Economics and Informatics, Federal Rural University of Pernambuco, Serra Talhada, PE, 56909-535, Brazil.
Chaos Solitons Fractals. 2022 Nov;164:112634. doi: 10.1016/j.chaos.2022.112634. Epub 2022 Sep 12.
The pandemic COVID-19 brings with it the need for studies and tools to help those in charge make decisions. Working with classical time series methods such as ARIMA and SARIMA has shown promising results in the first studies of COVID-19. We advance in this branch by proposing a risk factor map induced by the well-known Pearson diagram based on multivariate kurtosis and skewness measures to analyze the dynamics of deaths from COVID-19. In particular, we combine bootstrap for time series with SARIMA modeling in a new paradigm to construct a map on which one can analyze the dynamics of a set of time series. The proposed map allows a risk analysis of multiple countries in the four different periods of the pandemic COVID-19 in 55 countries. Our empirical evidence suggests a direct relationship between the multivariate skewness and kurtosis. We observe that the multivariate kurtosis increase leads to the rise of the multivariate skewness. Our findings reveal that the countries with high risk from the behavior of the number of deaths tend to have pronounced skewness and kurtosis values.
新冠疫情使得有必要开展研究并开发工具，以帮助决策者做出决策。在新冠疫情的首批研究中，运用诸如自回归积分移动平均模型（ARIMA）和季节性自回归积分移动平均模型（SARIMA）等经典时间序列方法已显示出有前景的结果。我们在这一领域取得进展，提出了一种基于多元峰度和偏度度量的、由著名的皮尔逊图诱导的风险因素图，用于分析新冠疫情死亡病例的动态变化。具体而言，我们在一种新的范式中将时间序列的自助法与SARIMA建模相结合，以构建一张能够分析一组时间序列动态变化的图。所提出的图允许对55个国家在新冠疫情四个不同时期的多个国家进行风险分析。我们的实证证据表明多元偏度和峰度之间存在直接关系。我们观察到多元峰度增加会导致多元偏度上升。我们的研究结果表明，因死亡人数行为而面临高风险的国家往往具有明显的偏度和峰度值。
