Ghosh D, Santra P K, Mahapatra G S, Elsonbaty Amr, Elsadany A A
Department of Mathematics, National Institute of Technology Puducherry, Karaikal, 609609 India.
Maulana Abul Kalam Azad University of Technology, Kolkata, 700064 India.
Eur Phys J Spec Top. 2022;231(18-20):3461-3470. doi: 10.1140/epjs/s11734-022-00537-2. Epub 2022 Mar 16.
The forecasting of the nature and dynamics of emerging coronavirus (COVID-19) pandemic has gained a great concern for health care organizations and governments. The efforts aim to to suppress the rapid and global spread of its tentacles and also control the infection with the limited available resources. The aim of this work is to employ real data set to propose and analyze a compartmental discrete time COVID-19 pandemic model with non-linear incidence and hence predict and control its outbreak through dynamical research. The Basic Reproduction Number ( ) is calculated analytically to study the disease-free steady state ( ), and also the permanency case ( ) of the disease. Numerical results show that the transmission rates and are quite effective in reducing the COVID-19 infections in India or any country. The fitting and predictive capability of the proposed discrete-time system are presented for relishing the effect of disease through stability analysis using real data sets.
新型冠状病毒（COVID-19）大流行的性质和动态预测已引起医疗保健组织和政府的高度关注。这些努力旨在抑制其触角的迅速全球传播，并利用有限的可用资源控制感染。这项工作的目的是利用真实数据集提出并分析一个具有非线性发病率的离散时间COVID-19大流行模型，从而通过动力学研究预测和控制其爆发。通过解析计算基本再生数（ ）来研究无病稳态（ ）以及疾病的持久情况（ ）。数值结果表明，传播率 和 在减少印度或任何国家的COVID-19感染方面相当有效。通过使用真实数据集进行稳定性分析，展示了所提出的离散时间系统的拟合和预测能力，以体现疾病的影响。
