A discrete-time epidemic model for the analysis of transmission of COVID19 based upon data of epidemiological parameters

D Ghosh; P K Santra; G S Mahapatra; Amr Elsonbaty; A A Elsadany

doi:10.1140/epjs/s11734-022-00537-2

A discrete-time epidemic model for the analysis of transmission of COVID19 based upon data of epidemiological parameters

Eur Phys J Spec Top. 2022;231(18-20):3461-3470. doi: 10.1140/epjs/s11734-022-00537-2. Epub 2022 Mar 16.

Authors

D Ghosh¹, P K Santra², G S Mahapatra¹, Amr Elsonbaty^{3

4}, A A Elsadany^{3

5}

Affiliations

¹ Department of Mathematics, National Institute of Technology Puducherry, Karaikal, 609609 India.
² Maulana Abul Kalam Azad University of Technology, Kolkata, 700064 India.
³ Department of Mathematics, College of Science and Humanities in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942 Saudi Arabia.
⁴ Department of Engineering Mathematics and Physics, Faculty of Engineering, Mansoura University, Mansoura, 35516 Egypt.
⁵ Department of Basic Science, Faculty of Computers and Informatics, Suez Canal University, Ismailia, 41522 Egypt.

Abstract

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 ( $R_{0}$ ) is calculated analytically to study the disease-free steady state ( $R_{0} < 1$ ), and also the permanency case ( $R_{0} > 1$ ) of the disease. Numerical results show that the transmission rates $α (> 0)$ and $β (> 0)$ 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.