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

Abstract

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.

The transition of the global financial markets' connectedness during the COVID-19 pandemic

“…After a bust due to the pandemic, the USD market could recover before the REIT market which took a longer time to resume its normality.

Figure 1 The boom and bust cycle of the considered financial markets in the period 2019/1 - 2021/6 (with the red dashed line corresponding to January 30 2020, the date the WHO declared COVID-19 a pandemic ( Shiferaw, 2021 ; and Fernandez-Perez et al, 2021 ).

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“… The boom and bust cycle of the considered financial markets in the period 2019/1 - 2021/6 (with the red dashed line corresponding to January 30 2020, the date the WHO declared COVID-19 a pandemic ( Shiferaw, 2021 ; and Fernandez-Perez et al, 2021 ). …”

“…The overall analysis suggested that most financial markets entered the downtrend after January 30, 2020, corresponding to the date the World Health Organization (WHO) declared the COVID-19 pandemic as a Public Health Emergency of International Concern( Shiferaw, 2021 ; and Fernandez-Perez et al, 2021 ). Thus, it is reasonable to use this date as the break date in our comovement and connectedness analysis.…”

Estimation and prediction of the multiply exponentially decaying daily case fatality rate of COVID-19

“…Therefore, numerous researchers have attempted to estimate CFR using various approaches [ 6 , 7 ]. There are CFR estimation models using statistical models [ 8 , 9 ] or machine learning techniques [ 10 ]. Generally, the CFR depends on numerous factors, such as the quality of patient care [ 11 ].…”

“…Hoffmann and Wolf [ 18 ] assessed the correlation between the proportion of elderly individuals among the confirmed cases and country-specific CFR. In [ 9 ], Shiferaw estimated the dynamics of CFR using Markov switching autoregressive (MSAR) models. Yang et al [ 19 ] applied simple linear and exponential regression methods to compute the CFR of the COVID-19 pandemic outbreak in China.…”