Statistical distribution fitting to the number of COVID-19 deaths in South Africa

Background: COVID-19 first detected in Wuhan; China in December 2019, is a disease caused by the coronavirus SARS-CoV-2. It has presented the greatest public health challenges globally since the 1918 influenza which was called the “mother” of all pandemics. South Africa and the rest of Africa are yet to experience the devastating effects of COVID-19. Methods: In this paper, the reported COVID-19 number of deaths in South Africa, for the period 27 March 2020 to 20 May 2020, is modeled using four statistical distributions which can be grouped under the Generalised Gamma distribution. This exploratory study also uses simple additive models to capture the underlying COVID-19 death rate. Results: Empirical results show that the Gamma distribution gives the best fit to the data. The hazard rate is still increasing, and the peak number of deaths has not been reached yet despite the lockdown and other measures to try and slow down the progression of the disease. Conclusions: The exploratory data analysis done in this study is simple and meant to complement the detailed and complex modelling done which is useful in informing policy and decision making.

Statistical distribution fitting to the number of COVID-19 deaths in South Africa

Background: COVID-19 first detected in Wuhan; China in December 2019, is a disease caused by the coronavirus SARS-CoV-2. It has presented the greatest public health challenges globally since the 1918 influenza which was called the “mother” of all pandemics. South Africa and the rest of Africa are yet to experience the devastating effects of COVID-19. Methods: In this paper, the reported COVID-19 number of deaths in South Africa, for the period 27 March 2020 to 20 May 2020, is modeled using four statistical distributions which can be grouped under the Generalised Gamma distribution. This exploratory study also uses simple additive models to capture the underlying COVID-19 death rate. Results: Empirical results show that the Gamma distribution gives the best fit to the data. The hazard rate is still increasing, and the peak number of deaths has not been reached yet despite the lockdown and other measures to try and slow down the progression of the disease. Conclusions: The exploratory data analysis done in this study is simple and meant to complement the detailed and complex modelling done which is useful in informing policy and decision making.

Quantile-Quantile plot for the generalised gamma distribution

The aim of the paper is to propose a new method of creating a Q-Q plot using the density function of order statistics as well as to compare it with the classical methods.The most popular distributions for statisticians are those flexible ones which have easily estimated parameters. In the pre-computer era Quantile-Quantile plot (Q-Q plot) can be constructed only for distributions of reversible cumulative distribution functions (CDF) such as the exponential distribution and the Weibull distribution. The CDF of generalised gamma distribution (GGD) is not only analytically irreversible, but also has no analytical form. However, at present, owing to advanced computer technology, this problem can be solved. The CDF of GGD can be inverted by using different computing environment, i.e. Microsoft Excel, Mathcad, R language.