In light of those current conditions that humanity is suffering from the outbreak of the Corona epidemic (COVID-19), which has caused an economic crisis for the entire world, and which also causes humanity, economic, and social losses. Which encouraged researchers in all fields to search and explore solutions to this epidemic. This is what prompted statisticians to provide probability distributions to describe this phenomenon, which is important in simulations and giving a certain probability of expected Incidence and deaths. Which helps in decision-making processes appropriate to the current situation. The purpose of this research is to find and classify the modeling of COVID-19 data by determining the optimal statistical modeling to evaluate the regular count of new COVID-19 fatalities, thus requiring discrete distributions. Some discrete models are checked and reviewed. A new discrete inverse Weibull distribution based on the discretization of survival has been reobtained. Probability mass function and the hazard rate is addressed. Discrete models are discussed based on the Maximum Likelihood estimate for parameters. A numerical analysis uses the regular count of new casualties in the countries of Angola, El Salvador, Estonia, and Greece. In-depth, the empirical findings are interpreted.
Almetwally, Ehab M. and Dey, Sanku
"Modelling of COVID-19 Data Using Discrete Distribution,"
Delta University Scientific Journal: Vol. 4
, Article 2.
Available at: https://digitalcommons.aaru.edu.jo/dusj/vol4/iss1/2