ANALYSIS OF PROBABILITY DISTRIBUTION FOR CORONA VIRUS MORTALITY IN IRAQ BEFORE AND AFTER THE DISCOVERY OF THE EPIDEMIOLOGICAL VACCINES
Abbas Gulmurad Beg Murad
Sulamani University, Collage of Administration and Economics, Department of Statistics and Informatics, Email: firstname.lastname@example.org
Sozan Saber Haider Ali
Sulamani University, Collage of Administration and Economics, Department of Statistics and Informatics, Email: email@example.com
Modern times created modern problems and many of those problems have data, In this research, we try to analyze the behavior of the Mortality data distribution resulting from the Corona epidemic probabilistically before and after the discovery of vaccines globally and their distribution in Iraq of different types of vaccines using some statistical tools, which can have a significant impact on increasing immunity and reducing the mortality rate of healthy people and people with infection, and thus changing the shape of the probability distribution of mortality On a daily basis, and reduce the proportion of deaths resulting from this epidemic by knowing the probability distribution with the best fit of the probability represents the data that has wide uses in survival analysis to describe the incidence of deaths before and after the discovery of vaccines with high fitness value using the method of estimating the maximum likelihood of Appropriate parameter estimating by using Anderson-Darling distance to reduce the distance, insides use some other statistical techniques to analyze the differences between mortality during the two years 2020 and 2021 (before and after vaccines discovery) depend upon programming language r and rely on a set of packages, the most important of which is (fitdistrplus package) and Other statistical functions in R language insides easy fit program for Iraqi mortality time series analysis from Corona virus.
Keywords: Corona Virus Mortality, Johnson SB distribution, Beta distribution, Dagum (4p) distribution, Anderson Darling test, fitdistrplus package.