
PurposeThe primary objective of this paper is to explore the robust determinants influencing the infection rate and case mortality rate of COVID-19 in both developing and developed economies. The analysis is conducted using a dataset encompassing 148 countries.Design/methodology/approachTo achieve this goal, empirical testing utilizes the Sala-i-Martin version of extreme bounds analysis, a method grounded in the cumulative density function. This approach allows for a comprehensive exploration of potential determinants.FindingsThe analysis results reveal that, to a large extent, distinct factors contribute to the infection and mortality rates in developed and developing countries. Notwithstanding these differences, certain common factors emerge, such as the risk environment, the number of tests conducted per million people and the percentage of the population over 65.Originality/valueDespite acknowledging the potential limitations inherent in official data, this study concludes that the presented results offer valuable insights. The identified determinants, both unique and common, contribute to understanding the dynamics of COVID-19 in diverse economic settings. The information gleaned from this research holds significance for decision-makers involved in combating the ongoing pandemic.
Extreme bounds analysis, Infection rate, HF5001-6182, marine_engineering_94, HG1-9999, Cumulative density function, COVID-19, Business, Case mortality rate, Finance
Extreme bounds analysis, Infection rate, HF5001-6182, marine_engineering_94, HG1-9999, Cumulative density function, COVID-19, Business, Case mortality rate, Finance
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
