Modeling the survival times of the COVID-19 patients with a new statistical model: A case study from China

Abstract

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.

Short-Term Prediction of COVID-19 Using Novel Hybrid Ensemble Empirical Mode Decomposition and Error Trend Seasonal Model

“…Numerous efforts have been made on solving the problem of mode mixing and thus EEMD is one such alternative approach (8). This method has the flexibility to handle very complex signals without the mode mixing problem.…”

“…In recent times, the prediction of the current pandemic of COVID-19 outbreak is a test for data experts as inadequate information is available on the initial growing curve, and the epidemiological properties of the virus to be fully elucidated. There has been a renewed interest in using time series models to predict the epidemics, namely, SARS, Ebola, influenza, and dengue (3)(4)(5)(6)(7)(8)(9)(10). These studies have shown an increasing curiosity in applying time series models as valuable tools in estimating and predicting epidemics.…”

Weighted power Maxwell distribution: Statistical inference and COVID-19 applications

“…In other words, we blended existing distributions together to create the new distribution. For more information, see [1][2][3][4][5][6][7][8][9]. One of the primary objectives of statistics is to develop applicable statistical models for occurrences in the real world that can be modelled using already existing probability distributions.…”

A superior extension for the Lomax distribution with application to Covid-19 infections real data

“…Al-Babtain et al [24] analysis the Covid-19 spread in KSA. For more studied about COVID-19 see [34] , [35] , [22] , [14] , [18] .…”