From Transmission to Disability: A Fractional Perspective on HBV Dynamics
Published in Microbiology, Computational Sciences, and Biomedical Research
What if infectious disease models systematically underestimate long-term human suffering?
Infectious disease modeling should not stop at transmission dynamics. It must also capture the long-term health consequences that shape real-world impact.
In our recent study published in BMC Infectious Diseases, we developed a fractional-order mathematical framework to model hepatitis B virus transmission across heterogeneous contact structures while explicitly incorporating disability burden into the system.
Unlike classical integer-order approaches, the model integrates memory effects using a Mittag–Leffler kernel, enabling a more realistic representation of disease progression and persistence over time.
A key contribution of this work is the introduction of disability-aware parameters that quantify long-term functional impairment among infected individuals. By incorporating metrics such as Years Lived with Disability and Disability-Adjusted Life Years, the model connects epidemiological dynamics with measurable health outcomes.
Through analytical and numerical investigations, including reproduction number analysis and sensitivity assessment, the results highlight the critical role of vaccination, transmission behavior, and carrier dynamics in shaping both infection spread and long-term disability burden.
This work contributes to a growing research direction that integrates mathematical modeling, artificial intelligence, and public health to support predictive, data-driven healthcare strategies.
Bridging engineering, artificial intelligence, and health systems, Prof. Kamel Guedri develops advanced computational frameworks to model complex physical and biological phenomena with real-world impact.
Prof. Kamel Guedri is a Full Professor of Mechanical Engineering at Umm Al-Qura University, Saudi Arabia. His research lies at the intersection of advanced heat transfer, multi-component nanofluids, and AI-assisted computational modeling for complex thermal and biomedical systems.
He specializes in the thermophysical characterization and numerical modeling of hybrid, tri-hybrid, and tetra-hybrid nanofluids, with a particular focus on entropy generation, non-Newtonian fluid behavior, and energy-efficient thermal management. His work integrates high-fidelity simulations with machine learning techniques to enhance prediction accuracy and optimize system performance.
A defining direction of his recent work lies in extending mathematical modeling beyond traditional boundaries. It incorporates memory-driven fractional dynamics and disability-aware epidemiological frameworks to better quantify long-term health outcomes. His contributions include integrating disability burden metrics such as DALYs and YLDs into infectious disease models, enabling a more comprehensive understanding of disease impact and supporting predictive, data-driven healthcare strategies.
Prof. Guedri leads and contributes to interdisciplinary research addressing real-world challenges in energy systems, biomedical engineering, and smart environments, including high-density and extreme scenarios such as Hajj and crowd-related systems.
With over 300 peer-reviewed publications and an h-index of 48, he actively collaborates on projects that bridge engineering, artificial intelligence, and societal applications. His research aims to advance sustainable, intelligent, and high-performance systems for next-generation engineering and health technologies.
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