A Caputo–Fabrizio fractional-order model with MCMC estimation for rabies transmission dynamics in a multi-host population

Rabies is a fatal zoonotic disease that remains a major public health concern, especially in low- and middle-income countries where control measures are often inadequate. Despite vaccination campaigns, the persistence of rabies is exacerbated by complex transmission involving humans, domestic animal...

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Main Authors: Jufren Zakayo Ndendya, Joshua A. Mwasunda, Stephen Edward, Nyimvua Shaban
Format: Article
Language:English
Published: Elsevier 2025-09-01
Series:Scientific African
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Online Access:http://www.sciencedirect.com/science/article/pii/S2468227625003552
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author Jufren Zakayo Ndendya
Joshua A. Mwasunda
Stephen Edward
Nyimvua Shaban
author_facet Jufren Zakayo Ndendya
Joshua A. Mwasunda
Stephen Edward
Nyimvua Shaban
author_sort Jufren Zakayo Ndendya
collection DOAJ
description Rabies is a fatal zoonotic disease that remains a major public health concern, especially in low- and middle-income countries where control measures are often inadequate. Despite vaccination campaigns, the persistence of rabies is exacerbated by complex transmission involving humans, domestic animals, and wildlife. This study proposes a Caputo–Fabrizio fractional-order model to capture the memory-dependent nature of rabies transmission in a multi-host setting. Mathematical analysis is conducted to establish the existence, uniqueness, and stability of solutions using fixed-point theory and the Routh–Hurwitz criterion. The effective reproduction number is derived through a graph-theoretic approach. Parameter estimation is performed using Markov Chain Monte Carlo methods applied to real and synthetic data. Numerical simulations reveal that memory effects introduced by the fractional-order operator significantly influence the dynamics of disease transmission. The results show that incorporating fractional dynamics leads to more realistic predictions compared to classical models, particularly in evaluating the timing and intensity of outbreaks. Moreover, targeted control strategies, especially vaccination and culling, substantially reduce infection levels under fractional-order dynamics. These findings underscore the importance of accounting for memory and non-local effects in infectious disease modeling.
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spelling doaj-art-c7063473a16d430898ff081f3990bb392025-08-20T05:07:20ZengElsevierScientific African2468-22762025-09-0129e0288510.1016/j.sciaf.2025.e02885A Caputo–Fabrizio fractional-order model with MCMC estimation for rabies transmission dynamics in a multi-host populationJufren Zakayo Ndendya0Joshua A. Mwasunda1Stephen Edward2Nyimvua Shaban3Department of Mathematics, University of Dar es Salaam, P.O. Box 35062, Dar es Salaam, Tanzania; Environmental Health and Ecological Sciences Department, Ifakara Health Institute, P.O. Box 78373, Dar es Salaam, Tanzania; Corresponding author at: Department of Mathematics, University of Dar es Salaam, P.O. Box 35062, Dar es Salaam, Tanzania.Department of Mathematics, Physics and Informatics, Mkwawa University College of Education, P.O. Box 2513, Iringa, TanzaniaDepartment of Mathematics and Statistics, University of Dodoma, P.O. Box 338, Dodoma, TanzaniaDepartment of Mathematics, University of Dar es Salaam, P.O. Box 35062, Dar es Salaam, TanzaniaRabies is a fatal zoonotic disease that remains a major public health concern, especially in low- and middle-income countries where control measures are often inadequate. Despite vaccination campaigns, the persistence of rabies is exacerbated by complex transmission involving humans, domestic animals, and wildlife. This study proposes a Caputo–Fabrizio fractional-order model to capture the memory-dependent nature of rabies transmission in a multi-host setting. Mathematical analysis is conducted to establish the existence, uniqueness, and stability of solutions using fixed-point theory and the Routh–Hurwitz criterion. The effective reproduction number is derived through a graph-theoretic approach. Parameter estimation is performed using Markov Chain Monte Carlo methods applied to real and synthetic data. Numerical simulations reveal that memory effects introduced by the fractional-order operator significantly influence the dynamics of disease transmission. The results show that incorporating fractional dynamics leads to more realistic predictions compared to classical models, particularly in evaluating the timing and intensity of outbreaks. Moreover, targeted control strategies, especially vaccination and culling, substantially reduce infection levels under fractional-order dynamics. These findings underscore the importance of accounting for memory and non-local effects in infectious disease modeling.http://www.sciencedirect.com/science/article/pii/S2468227625003552Zoonotic disease modelingMemory effectNon-local operatorsGraph theoryBayesian parameter estimation
spellingShingle Jufren Zakayo Ndendya
Joshua A. Mwasunda
Stephen Edward
Nyimvua Shaban
A Caputo–Fabrizio fractional-order model with MCMC estimation for rabies transmission dynamics in a multi-host population
Scientific African
Zoonotic disease modeling
Memory effect
Non-local operators
Graph theory
Bayesian parameter estimation
title A Caputo–Fabrizio fractional-order model with MCMC estimation for rabies transmission dynamics in a multi-host population
title_full A Caputo–Fabrizio fractional-order model with MCMC estimation for rabies transmission dynamics in a multi-host population
title_fullStr A Caputo–Fabrizio fractional-order model with MCMC estimation for rabies transmission dynamics in a multi-host population
title_full_unstemmed A Caputo–Fabrizio fractional-order model with MCMC estimation for rabies transmission dynamics in a multi-host population
title_short A Caputo–Fabrizio fractional-order model with MCMC estimation for rabies transmission dynamics in a multi-host population
title_sort caputo fabrizio fractional order model with mcmc estimation for rabies transmission dynamics in a multi host population
topic Zoonotic disease modeling
Memory effect
Non-local operators
Graph theory
Bayesian parameter estimation
url http://www.sciencedirect.com/science/article/pii/S2468227625003552
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