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Mathematical Analysis and Modeling of Ebola Virus Dynamics via Optimal Control and Neural Network Paradigms

Noor Muhammad,M. Alam,Zhang Shiqing School of Mathematics,Sichuan University,Chengdu,China,D. Mathematics,Pabna University of ScienceTechnology,Pabna-6600,Bangladesh

Published 2025 in Unknown venue

ABSTRACT

Ebola virus disease is a severe hemorrhagic fever with rapid transmission through infected fluids and surfaces. We develop a fractional-order model using Caputo derivatives to capture memory effects in disease dynamics. An eight-compartment structure distinguishes symptomatic, asymptomatic, and post-mortem transmission pathways. We prove global well-posedness, derive the basic reproduction number $\mathcal{R}_0$, and establish stability theorems. Sensitivity analysis shows $\mathcal{R}_0$ is most sensitive to transmission rate, incubation period, and deceased infectivity. Treatment-safe burial synergy achieves 86.5\% morbidity-mortality control, with safe burial being most effective. Our disease-informed neural network achieves near-perfect predictive accuracy ($R^2$: 0.991-0.999, 99.1-99.9\% accuracy), closely matching real epidemic behavior.

PUBLICATION RECORD

  • Publication year

    2025

  • Venue

    Unknown venue

  • Publication date

    2025-11-09

  • Fields of study

    Computer Science, Mathematics, Engineering, Environmental Science, Medicine

  • Identifiers
    arXiv 2511.06303
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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