Regime shifts in the COVID-19 case fatality rate dynamics: A Markov-switching autoregressive model analysis

被引：5

作者：

Shiferaw Y.A. ^{[1
]}

机构：

[1] Department of Statistics, University of Johannesburg, Auckland Park Kingsway Campus, PO Box 524, Johannesburg

来源：

Chaos, Solitons and Fractals: X | 2021年 / 6卷

关键词：

Case fatality rate; Coronavirus; COVID-19; Global; Markov-switching autoregressive approach;

D O I：

10.1016/j.csfx.2021.100059

中图分类号：

学科分类号：

摘要：

The 2019 novel coronavirus disease (COVID-19) has spread rapidly to many countries around the world from Wuhan, the capital of China's Hubei province since December 2019. It has now a huge effect on the global economy. As of 13 September 2020, more than 28, 802, 775, and 920, 931 people are infected and dead, respectively. The mortality of COVID-19 infections is increasing as the number of infections increase. Many countries published control measures to contain its spread. Even though there are many drugs and vaccines under trial by pharmaceutical companies and research groups, no specific vaccine or drug has yet been found. Therefore, it is necessary to explain the behaviour of the case fatality rate (CFR) of COVID-19 using the most updated COVID-19 epidemiological data before 13 September 2020. The dynamics in the CFR were analyzed using the Markov-switching autoregressive (MSAR) models. Results showed that the two-regime and three-regime MSAR approach better captured the non-linear dynamics in the CFR time series data for each of the top heavily infected countries including the world. The results also showed that rises in CFRs are more volatile than drops. We believe that this information can be useful for the government to establish appropriate policies in a timely manner. © 2021 The Author

引用

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