Study of the Factors Affecting the Incidence of COVID-19 Infection Using an Accelerrated Weibull Regression Model
7th International Conference on Contemporary Information Technology and Mathematics, ICCITM 2021
; : 322-327, 2021.
Article
in English
| Scopus | ID: covidwho-1730931
ABSTRACT
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.
Hazard function; parametric regression; Survival analysis; Weibull regression model; Maximum likelihood estimation; Parameter estimation; Regression analysis; Viruses; Maximum likelihood estimation method; Probability: distributions; R softwares; Regression coefficient; Regression modelling; Regression parameters; Weibull regression models; Weibull distribution
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Observational study
Language:
English
Journal:
7th International Conference on Contemporary Information Technology and Mathematics, ICCITM 2021
Year:
2021
Document Type:
Article
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