Study of the Factors Affecting the Incidence of COVID-19 Infection Using an Accelerrated Weibull Regression Model

The Weibull regression model is one of the most important parametric regression models. Because of the knowledge of the probability distribution of the response variable following the Weibull distribution, which facilities the possibility of estimating the regression parameters based on the baseline hazard function. It is estimated by estimating the parameters of the Weibull distribution using the maximum likelihood estimation method. The R software was used for the purpose of estimating the regression coefficients and identifying the most significant features that model the outcome. In this paper, we investigate the factors affecting the progression of Corona virus patients from Al-Shiffa Hospital in the city of Mosul. In addition, this study focused on patients who were in a critical condition, and whose cases necessitated their monitoring during their stay under the artificial respiration machine Continues Positive Airway Pressure (CPAP). The six variables were taken as the most influential on the injury case and it was found that the most influential variables were Remdesivir and O2 using some statistical criteria. © 2021 IEEE.

Statistical Analysis of the Dynamics of Coronavirus Cases using Stepwise Switching Regression.

The dynamics of coronavirus cases is proposed to be modeled using switching regression whose switching points are unknown. The stepwise process of constructing the regression in time is described. The dynamics of the number of coronavirus cases in Ukraine is analyzed.