Garg Harish, Nasir Abdul, Jan Naeem, Khan Sami Ullah
School of Mathematics, Thapar Institute of Engineering and Technology, Deemed University Patiala, Patiala, India.
Department of Mathematics, Institute of Numerical Sciences, Gomal University, Dera Ismail Khan, 29050 KPK Pakistan.
Soft comput. 2023;27(6):3477-3491. doi: 10.1007/s00500-021-06133-1. Epub 2021 Aug 28.
The health organizations around the world are currently facing one of the greatest challenges, to overcome the current global pandemic, COVID-19. It erupted in December 2019, in Wuhan City, China. It spreads rapidly throughout the world within couple of months. In this paper, the data of the COVID-19 have been collected, organized, analyzed and interpreted using the discrete-time model of SIR epidemic model. Moreover, results for several countries from different regions of the world have been obtained. Furthermore, comparative study has been carried out for the countries under consideration. The comparison was performed for the data of different countries on same dates of each month. However, the calculations are carried out for thirteen consecutive weeks, to investigate the rate of spread and the control of the disease in these countries. This guides us to some important concepts like factors favoring the spread of virus and those resisting the spread. Different regions are studied and their data have been evaluated to know which regions are the most effected. This study helps to know the important factors about the behavior of the coronavirus in different environments, such as lockdowns, temperatures, humidity and other restrictions. The proposed concepts and equations can be used to project the upcoming behavior of the pandemic.
世界各地的卫生组织目前正面临着最大的挑战之一,即战胜当前的全球大流行疾病——新冠病毒肺炎(COVID-19)。它于2019年12月在中国武汉市爆发,并在短短几个月内迅速蔓延至全球。在本文中,我们使用SIR传染病模型的离散时间模型收集、整理、分析和解释了新冠病毒肺炎的数据。此外,还得出了世界不同地区几个国家的结果。此外,还对所考虑的国家进行了比较研究。比较是针对每个月相同日期的不同国家的数据进行的。然而,连续进行了十三周的计算,以调查这些国家疾病的传播速度和控制情况。这为我们引出了一些重要概念,如有利于病毒传播的因素和阻碍传播的因素。对不同地区进行了研究,并对其数据进行了评估,以了解哪些地区受影响最大。这项研究有助于了解冠状病毒在不同环境下行为的重要因素,如封锁、温度、湿度和其他限制措施。所提出的概念和方程可用于预测大流行疾病未来的发展态势。