Modeling COVID-19 Pandemic Data with New Pareto Model
Zakeia A. Al-Saiary *
Department of Mathematics and Statistics, College of Science, University of Jeddah, Jeddah 22252, Saudi Arabia.
Rana A. Bakoban
Department of Mathematics and Statistics, College of Science, University of Jeddah, Jeddah 22252, Saudi Arabia.
Afnan S. Alamoudi
Department of Mathematics and Statistics, College of Science, University of Jeddah, Jeddah 22252, Saudi Arabia.
*Author to whom correspondence should be addressed.
Abstract
This paper aims to find a statistical model for modeling the COVID-19 data. We deduced a robust and effective model for fitting the COVID 19 mortality. This model is a new Extended-Pareto distribution (NE-P). The maximum likelihood method is utilized to obtain the estimator of the parameters. A simulation was carried out using different sample sizes and different values of the parameters. In addition, the goodness of fit test statistics was calculated for proposed model compared with the baseline model to find out that our new model is the best for modeling data COVID-19.
Keywords: A new extended-pareto distribution, COVID 19 mortality, the maximum likelihood method, goodness of fit