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Modeling the survival times of the COVID-19 patients with a new statistical model: A case study from China.
PLOS ONE ( IF 2.9 ) Pub Date : 2021-07-26 , DOI: 10.1371/journal.pone.0254999 Xiaofeng Liu 1 , Zubair Ahmad 2 , Ahmed M Gemeay 3 , Alanazi Talal Abdulrahman 4 , E H Hafez 5 , N Khalil 6
PLOS ONE ( IF 2.9 ) Pub Date : 2021-07-26 , DOI: 10.1371/journal.pone.0254999 Xiaofeng Liu 1 , Zubair Ahmad 2 , Ahmed M Gemeay 3 , Alanazi Talal Abdulrahman 4 , E H Hafez 5 , N Khalil 6
Affiliation
Over the past few months, the spread of the current COVID-19 epidemic has caused tremendous damage worldwide, and unstable many countries economically. Detailed scientific analysis of this event is currently underway to come. However, it is very important to have the right facts and figures to take all possible actions that are needed to avoid COVID-19. In the practice and application of big data sciences, it is always of interest to provide the best description of the data under consideration. The recent studies have shown the potential of statistical distributions in modeling data in applied sciences, especially in medical science. In this article, we continue to carry this area of research, and introduce a new statistical model called the arcsine modified Weibull distribution. The proposed model is introduced using the modified Weibull distribution with the arcsine-X approach which is based on the trigonometric strategy. The maximum likelihood estimators of the parameters of the new model are obtained and the performance these estimators are assessed by conducting a Monte Carlo simulation study. Finally, the effectiveness and utility of the arcsine modified Weibull distribution are demonstrated by modeling COVID-19 patients data. The data set represents the survival times of fifty-three patients taken from a hospital in China. The practical application shows that the proposed model out-classed the competitive models and can be chosen as a good candidate distribution for modeling COVID-19, and other related data sets.
更新日期：2021-07-26
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