A fractional order SITR mathematical model for forecasting of transmission of COVID-19 of India with lockdown effect
In this paper, we consider a mathematical model to explain, understanding, and to forecast the outbreaks of COVID-19 in India. The model has four components leading to a system of fractional order differential equations incorporating the refuge concept to study the lockdown effect in controlling COV...
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Published in | Results in physics Vol. 24; p. 104067 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Netherlands
Elsevier B.V
01.05.2021
The Author(s). Published by Elsevier B.V Elsevier |
Subjects | |
Online Access | Get full text |
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Summary: | In this paper, we consider a mathematical model to explain, understanding, and to forecast the outbreaks of COVID-19 in India. The model has four components leading to a system of fractional order differential equations incorporating the refuge concept to study the lockdown effect in controlling COVID-19 spread in India. We investigate the model using the concept of Caputo fractional-order derivative. The goal of this model is to estimate the number of total infected, active cases, deaths, as well as recoveries from COVID-19 to control or minimize the above issues in India. The existence, uniqueness, non-negativity, and boundedness of the solutions are established. In addition, the local and global asymptotic stability of the equilibrium points of the fractional-order system and the basic reproduction number are studied for understanding and prediction of the transmission of COVID-19 in India. The next step is to carry out sensitivity analysis to find out which parameter is the most dominant to affect the disease’s endemicity. The results reveal that the parameters η,μ and ρ are the most dominant sensitivity indices towards the basic reproductive number. A numerical illustration is presented via computer simulations using MATLAB to show a realistic point of view. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 2211-3797 2211-3797 |
DOI: | 10.1016/j.rinp.2021.104067 |