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Research Article | Open Access

Sensitivity analysis of cassava mosaic disease with saturation incidence rate model

Sireepatch Sangsawang1Usa Wannasingha Humphries1( )Amir Khan2Puntani Pongsumpun3
Department of Mathematics, Faculty of Science, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand
Department of Mathematics and Statistics, University of Swat, Swat 19120, Khyber Pakhtunkhwa, Pakistan
Department of Mathematics, School of Science, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand
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Abstract

Cassava mosaic disease (CMD) is caused by a virus transmitted by the whitefly. This disease can destroy cassava at any stage of its growth and it resulted in lower cassava yields. In this paper, we developed a mathematical model for the epidemic of cassava mosaic disease with a deterministic model which has saturation incidence rates. This model aims to explain the effect of vectors on cassava disease outbreaks. First, this model was analyzed using standard dynamic methods to determine the behavior of the solution. We found the existence and condition of disease-free and endemic steady state. The basic reproductive number ( R 0 ) is obtained by using the next-generation method which R 0 helps assess the ability to spread infectious diseases. Second, the stability of the steady state was analyzed, then we obtain the condition of existence of local stability and global stability at each steady state of this model. Third, analysis of the sensitivity indices in the threshold number to determine the effect of the various parameters. Finally, the results of the theoretical model were validated by numerical simulations. It is represented by various graphs converging at a steady state and stable.

CLC number: 34D20, 34D23

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AIMS Mathematics
Pages 6233-6254

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Cite this article:
Sangsawang S, Humphries UW, Khan A, et al. Sensitivity analysis of cassava mosaic disease with saturation incidence rate model. AIMS Mathematics, 2023, 8(3): 6233-6254. https://doi.org/10.3934/math.2023315

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Received: 08 October 2022
Revised: 26 November 2022
Accepted: 13 December 2022
Published: 15 March 2023
©2023 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)