Fractional-Order Ebola-Malaria Coinfection Model with a Focus on Detection and Treatment Rate
| dc.contributor.author | Zhang, Lingling | |
| dc.contributor.author | Addai, Emmanuel | |
| dc.contributor.author | Ackora-Prah, Joseph | |
| dc.contributor.author | Dissou Arthur, Yarhands | |
| dc.contributor.author | Asamoah, Joshua Kiddy K. | |
| dc.contributor.orcid | 0000-0002-7066-246X | |
| dc.date.accessioned | 2024-11-20T11:48:02Z | |
| dc.date.available | 2024-11-20T11:48:02Z | |
| dc.date.issued | 2022-09 | |
| dc.description | This article is published by Hindawi 2022 and is also available at https://doi.org/10.1155/2022/6502598 | |
| dc.description.abstract | Coinfection of Ebola virus and malaria is widespread, particularly in impoverished areas where malaria is already ubiquitous. Epidemics of Ebola virus disease arise on a sporadic basis in African nations with a high malaria burden. An observational study discovered that patients in Sierra Leone’s Ebola treatment centers were routinely infected with malaria parasites, increasing the risk of death. In this paper, we study Ebola-malaria coinfections under the generalized Mittag-Leffler kernel fractional derivative. The Banach fixed point theorem and the Krasnoselskii type are used to analyse the model’s existence and uniqueness. We discuss the model stability using the Hyers-Ulam functional analysis. The numerical scheme for the Ebolamalaria coinfections using Lagrange interpolation is presented. The numerical trajectories show that the prevalence of Ebolamalaria coinfections ranged from low to moderate depending on memory. This means that controlling the disease requires adequate knowledge of the past history of the dynamics of both malaria and Ebola. The graphical dynamics of the detection rate indicate that a variation in the detection rate only affects the following compartments: individuals that are latently infected with the Ebola, Ebola virus afflicted people who went unnoticed, individuals who have been infected with the Ebola virus and have been diagnosed with the disease, and persons undergoing Ebola virus therapy. | |
| dc.description.sponsorship | KNUST | |
| dc.identifier.citation | Hindawi Computational and Mathematical Methods in Medicine Volume 2022, Article ID 6502598, 19 pages | |
| dc.identifier.uri | https://doi.org/10.1155/2022/6502598 | |
| dc.identifier.uri | https://ir.knust.edu.gh/handle/123456789/15953 | |
| dc.language.iso | en | |
| dc.publisher | Hindawi | |
| dc.title | Fractional-Order Ebola-Malaria Coinfection Model with a Focus on Detection and Treatment Rate | |
| dc.type | Article |