ABSTRACT
In this paper, we introduce a new class of statistical models to deal with the data sets in the sports and health sectors. The new class is called, a novel exponent power-Y (NovEP-Y) family of distributions. By implementing the NovEP-Y approach, a new model, namely, a novel exponent power-Weibull (NovEP-Weibull) distribution is introduced. Some distributional properties of the NovEP-Y family such as identifiability, order statistics, quantile function, and moments are obtained. The maximum likelihood estimators of the parameters are also derived. Furthermore, a brief Monto Carlo simulation study is conducted to evaluate the performances of the estimators. To show the applicability of the NovEP-Weibull model, two data sets from the sports and health sciences are considered. The first data set represents the time-to-even data collected from different football matches during the period 1964-2018. Whereas, the second data set is taken from the health sector, representing the survival times of the COVID-19 infected patients. Based on some well-known statistical tests, it is observed that the NovEP-Weibull model is a very competitive dis-tribution for modeling the data sets in the sports and health sectors. (c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
ABSTRACT
OBJECTIVE: It is known that the severity of COVID-19 is linked to the prognosis of patients; therefore, an early identification is required for patients who are likely to develop severe or critical COVID-19 disease. The purpose of this study is to propose a statistical method for identifying the severity of COVID-19 disease by using clinical and biochemical laboratory markers. PATIENTS AND METHODS: A total of 48 clinically and laboratory-confirmed cases of COVID-19 were obtained from King Fahad Hospital, Medina (KFHM) between 27th April 2020 to 25th May 2020. The patients' demographics and severity of COVID-19 disease were assessed using 39 clinical and biochemical features. After excluding the demographics, 35 predicting features were included in the analysis (diabetes, chronic disease, viral and bacterial co-infections, PCR cycle number, ICU admission, clot formation, cardiac enzymes elevation, hematology profile, sugar levels in the blood, as well as liver and kidney tests, etc.). Logistic regression, stepwise logistic regression, L-2 logistic regression, L-2 stepwise logistic regression, and L-2 best subset logistic regression were applied to model the features. The consistency index was used with kernel Support-Vector Machines (SVM) for the identification of associated markers. RESULTS: L-2 best subset logistic regression technique outperformed all other fitted models for modeling COVID-19 disease severity by achieving an accuracy of 88% over the test data. Consistency index over L-2 best subset logistic regression identified 14 associated markers that can best predict the COVID-19 severity among COVID-19 patients. CONCLUSIONS: By combining a variety of laboratory markers with L-2 best subset logistic regression, the current study has proposed a highly accurate and clinically interpretable model of predicting COVID-19 severity.