ArticlePloS one2021
Modeling the survival times of the COVID-19 patients with a new statistical model: A case study from China.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- A novel extension of the power lindley distribution with statistical properties and application to COVID-19 data.Scientific reports · 2025Article
- Development of a novel extension of Rayleigh distribution with application to COVID-19 data.Scientific reports · 2025Article
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- A new probabilistic model: Theory, simulation and applications to sports and failure times data.Heliyon · 2024Article
- An analysis of the waning effect of COVID-19 vaccinations.Genomics & informatics · 2023Article
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- Validation framework for epidemiological models with application to COVID-19 models.PLoS computational biology · 2023Article
- Weighted power Maxwell distribution: Statistical inference and COVID-19 applications.PloS one · 2023Article
- The Gull Alpha Power Lomax distributions: Properties, simulation, and applications to modeling COVID-19 mortality rates.PloS one · 2023Article
- General two-parameter distribution: Statistical properties, estimation, and application on COVID-19.PloS one · 2023Article
- Generalization of the Lindley distribution with application to COVID-19 data.International journal of data science and analytics · 2022Article
- Mathematical modeling of the impact of Omicron variant on the COVID-19 situation in South Korea.Genomics & informatics · 2022Article
- A new versatile modification of the Rayleigh distribution for modeling COVID-19 mortality rates.Results in physics · 2022Article
- The new discrete distribution with application to COVID-19 Data.Results in physics · 2022Article
- Short-Term Prediction of COVID-19 Using Novel Hybrid Ensemble Empirical Mode Decomposition and Error Trend Seasonal Model.Frontiers in public health · 2022Article
- Modelling the COVID-19 Mortality Rate with a New Versatile Modification of the Log-Logistic Distribution.Computational intelligence and neuroscience · 2021Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
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.
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