{"ID":2844324,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.06303","arxiv_id":"2511.06303","title":"Mathematical Analysis and Modeling of Ebola Virus Dynamics via Optimal Control and Neural Network Paradigms","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.","short_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...","url_abs":"https://arxiv.org/abs/2511.06303","url_pdf":"https://arxiv.org/pdf/2511.06303v3","authors":"[\"Noor Muhammad\",\"Md. Nur Alam\",\"Zhang Shiqing\"]","published":"2025-11-09T09:55:41Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[]","has_code":false}