Selecting the best probability distribution of infection with MERS-COV in Wasit Governorate

Abstract

This research aims to study the probability distribution of cases infected with Coronavirus in Wasit Governorate for the period from 2020 to 2021 using three types of probability distributions: the logistic distribution, the two-parameter Weibull distribution, and the least-valued Campbell distribution. Maximum, where the parameters of these distributions were estimated using the maximum likelihood method. Applying several criteria to determine the optimal distribution of cases infected with Coronavirus in the governorate, as these criteria were consistent Akaike (CAIC), Bayesian Akaike (BIC), and Akaike (AIC), and the probability distribution with the lowest value for these criteria is considered the best to represent a good model for studying this data. Based on the applied part, the researchers concluded that the most appropriate probability distribution for coronavirus infection data in Wasit Governorate is the two-parameter Weibull distribution. The research is to provide a better understanding of the pattern of spread of the virus in the governorate, and to enable health officials to take more effective measures to limit the spread of infection in the future.