Ahmadini Abdullah Ali H, Elgarhy Mohammed, Shawki A W, Baaqeel Hanan, Bazighifan Omar
Department of Mathematics, Faculty of Science, Jazan University, Jazan, Saudi Arabia.
The Higher Institute of Commercial Sciences, Al Mahalla Al Kubra, 31951, Algarbia, Egypt.
Appl Bionics Biomech. 2022 Feb 23;2022:7104960. doi: 10.1155/2022/7104960. eCollection 2022.
. Currently, the COVID-19 pandemic represents a critical issue all over the world. On May 11, 2020, at 05 : 41 GMT, approximately 0.28 million individuals had perished because of the COVID-19 pandemic, and the figure is continuously growing rapidly. Unfortunately, millions of people have died due to this pandemic. As a result, this issue forced governments and other corresponding organizations to take significant action, such as the lockdown and vaccinations. Furthermore, scientists have developed several vaccinations, and the World Health Organization (WHO) has urged governments and people to get vaccinated to eradicate this pandemic. Consequently, the findings of any scientific research into this phenomenon are highly interesting. . To enhance individual protection, it is now critical to analyze and compare the percentage of people fully vaccinated against COVID-19. It is constantly of interest in the field of big data science and other related disciplines to provide the best analysis and modeling of COVID-19 data. . Through this paper, we aimed to compare individuals who have been completely vaccinated against COVID-19 in two locations: North American countries and Arabian Peninsula countries. Simple techniques for comparing individuals who have been completely vaccinated against COVID-19 have been applied, which may be used to generate the foundation for conclusions. Most significantly, a modern statistical model was created to present the best assessment of individuals completely vaccinated against COVID-19 data in nations in North America and the Arabian Peninsula. Some of the suggested statistical model features were proposed. Furthermore, the estimate of the model parameters was driven using the maximum likelihood estimation method. . The flexibility provided by the proposed statistical model is useful for describing the percentage of the individuals completely vaccinated against COVID-19, which provides a close fit with the COVID-19 data. . The proposed statistical model can be used for statistics and generate new statistical distributions that can be used to compare and predict the process of people's willingness to vaccinate and take the vaccine to try to eliminate COVID-19.
目前,新冠疫情是全球面临的一个关键问题。2020年5月11日格林威治标准时间05:41,约28万人因新冠疫情丧生,且这一数字仍在迅速持续增长。不幸的是,数百万人死于这场疫情。因此,这一问题迫使各国政府和其他相关组织采取重大行动,如封锁和接种疫苗。此外,科学家们研发了多种疫苗,世界卫生组织(WHO)敦促各国政府和民众接种疫苗以根除这场疫情。因此,针对这一现象的任何科学研究结果都极具意义。
为加强个人防护,分析和比较完全接种新冠疫苗的人群比例至关重要。在大数据科学及其他相关学科领域,持续关注的是如何对新冠疫情数据进行最佳分析和建模。
通过本文,我们旨在比较北美国家和阿拉伯半岛国家这两个地区完全接种新冠疫苗的人群。我们应用了简单的方法来比较完全接种新冠疫苗的人群,这可为得出结论奠定基础。最重要的是,创建了一个现代统计模型,以对北美和阿拉伯半岛国家完全接种新冠疫苗的数据进行最佳评估。文中提出了一些该统计模型的特征。此外,使用最大似然估计法来驱动模型参数的估计。
所提出的统计模型的灵活性有助于描述完全接种新冠疫苗的人群比例,与新冠疫情数据拟合度较高。
所提出的统计模型可用于统计,并生成新的统计分布,用于比较和预测人们接种疫苗的意愿以及接种疫苗以消除新冠疫情的过程。