A Risk-Structured Model for the Transmission Dynamics of Anthrax Disease

Kazeem Babatunde Akande, Samuel Tosin Akinyemi, Nneka O. Iheonu, Alogla Monday Audu, Folashade Mistura Jimoh, Atede Anne Ojoma, Victoria Iyabode Okeowo, Abdulrahaman Lawal Suleiman and Kayode Oshinubi ()
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Kazeem Babatunde Akande: Department of Mathematical Sciences, Baze University, Abuja 900108, FCT, Nigeria
Samuel Tosin Akinyemi: Department of Mathematics, Sikiru Adetona College of Education, Science and Technology, Ijebu-Ode 2128, Ogun State, Nigeria
Nneka O. Iheonu: Department of Mathematics, Federal University of Technology, Owerri 1526, Imo State, Nigeria
Alogla Monday Audu: Department of Mathematics, Nasarawa State University, Keffi 1022, Nasarawa State, Nigeria
Folashade Mistura Jimoh: Department of Physical Sciences, Al-Hikmah University, Ilorin 240001, Kwara State, Nigeria
Atede Anne Ojoma: Department of Mathematics, Federal University of Technology, Owerri 1526, Imo State, Nigeria
Victoria Iyabode Okeowo: Department of Mathematical Sciences, Stellenbosch University, Stellenbosch 7602, South Africa
Abdulrahaman Lawal Suleiman: Department of Mathematical Sciences, Stellenbosch University, Stellenbosch 7602, South Africa
Kayode Oshinubi: School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ 86011, USA

Mathematics, 2024, vol. 12, issue 7, 1-26

Abstract: Anthrax, a zoonotic disease with serious public health consequences, has been the subject of rigorous mathematical and statistical modeling to better understand its dynamics and to devise effective control techniques. In this study, we propose a novel mathematical risk-structured model for anthrax disease spread that includes both qualitative and quantitative evaluations. Our research focuses on the complex interplay between host–anthrax interactions and zoonotic transmission. Our mathematical approach incorporates bifurcation analysis and stability considerations. We investigate the dynamic behavior of the proposed model under various settings, shedding light on the important parameters that determine anthrax transmission and persistence. The normalized forward sensitivity analysis method is used to determine the parameters that are relevant to reducing R c and, by extension, disease spread. Through scenario simulation of our model, we identify intervention techniques, such as enlightenment of the populace, that will effectively minimize disease transmission. Our findings provide insights into anthrax epidemiology and emphasize the importance of effective disease management. Bifurcation investigations reveal the existence and stability of numerous equilibria, allowing for a better understanding of the behavior of the system under various scenarios. This study adds to the field of anthrax modeling by providing a foundation for informed decision-making regarding public health measures. The use of a mathematical modeling approach improves our ability to anticipate and control anthrax epidemics, ultimately helping to protect both human and animal populations.

Keywords: anthrax; zoonotic disease; sensitivity analysis; bifurcation theory; stability analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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