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Research Article | Open Access

Spatial heterogeneity and nonlinear transmission drive tuberculosis hotspots: a reaction–diffusion modeling study

Olumuyiwa James Peter1,2( ), Azhar Iqbal Kashif Butt3( ), Sharifah Sakinah Syed Ahmad4, Fatemah H. H. Al Mukahal3
Department of Mathematics, Saveetha School of Engineering, SIMATS, Saveetha University, Chennai, Tamil Nadu, India
Department of Mathematical and Computer Sciences, University of Medical Sciences, Ondo, Ondo State, Nigeria
Department of Mathematics and Statistics, College of Science, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Faculty of Artificial Intelligence and Cyber Security, Universiti Teknikal Malaysia Melaka, Malaysia
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Abstract

Tuberculosis remains a major public health challenge because of its long latent period, incomplete vaccine protection, and spatially heterogeneous transmission. This study develops a reaction–diffusion model incorporating vaccination, latent infection, active disease, treatment, recovery, nonlinear transmission, and population movement. Analytical and numerical investigations examine disease persistence, spatial spread, pattern formation, and control strategies. Results show that spatial heterogeneity and mobility can increase disease persistence and generate localized hotspots, transmission corridors, and clustered outbreaks. Numerical simulations demonstrate that interventions targeted at high-transmission areas reduce disease burden more effectively than uniformly distributed controls under the same resource constraints. These findings highlight the importance of spatial structure in tuberculosis dynamics and support geographically targeted vaccination and treatment strategies for improved disease control.

CLC number: 26A33, 34A08, 34A23, 92A25

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AIMS Mathematics
Pages 19400-19439

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Cite this article:
Peter OJ, Butt AIK, Ahmad SSS, et al. Spatial heterogeneity and nonlinear transmission drive tuberculosis hotspots: a reaction–diffusion modeling study. AIMS Mathematics, 2026, 11(7): 19400-19439. https://doi.org/10.3934/math.2026788

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Received: 23 April 2026
Revised: 10 June 2026
Accepted: 11 June 2026
Published: 15 July 2026
©2026 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)