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<p>Africa still faces significant challenges due to malaria, which calls for creative methods to disease modeling and control measures. In order to capture the non-local and memory effects that are inherent in the disease transmission process, we present a novel mathematical framework in this study that integrates treatment and vaccine as control methods for malaria dynamics in Africa. We do this by utilizing the Atangana–Baleanu fractional derivative. With the inclusion of important variables including host-vector interactions, treatment success, vaccine effectiveness, and population heterogeneity, our model offers a thorough depiction of the dynamics of malaria. We quantify their effect on illness outcomes and identify key parameters influencing the spread of malaria through sensitivity analysis. Then, taking into account resource limitations and operational viability, we optimize vaccination and treatment plans using optimal control theory to reduce the overall disease burden. Using a combination of optimum control approaches, sensitivity analysis, and fractional calculus, our methodology provides a strong framework for developing personalized interventions that are adaptive to the specific constraints of malaria management in Africa. The knowledge gained from this research has important ramifications for resource allocation and public health policy, enabling evidence-based decision-making to reduce the prevalence of malaria in the region.</p>

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A novel approach to modeling malaria with treatment and vaccination as control strategies in Africa using the Atangana–Baleanu derivative

  • 期刊论文

  • 发表日期：

    2025年1月31日

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  • Nnaemeka Stanley Aguegboh,
  • Walter Okongo,
  • Diallo Boubacar,
  • Munkaila Dasumani,
  • Kingsley Chinedu Nnamaga,
  • Daniel Ugochukwu Nnaji,
  • Netochukwu Onyiaji,
  • Anthonia Uchenna Nwachukwu

摘要

Africa still faces significant challenges due to malaria, which calls for creative methods to disease modeling and control measures. In order to capture the non-local and memory effects that are inherent in the disease transmission process, we present a novel mathematical framework in this study that integrates treatment and vaccine as control methods for malaria dynamics in Africa. We do this by utilizing the Atangana–Baleanu fractional derivative. With the inclusion of important variables including host-vector interactions, treatment success, vaccine effectiveness, and population heterogeneity, our model offers a thorough depiction of the dynamics of malaria. We quantify their effect on illness outcomes and identify key parameters influencing the spread of malaria through sensitivity analysis. Then, taking into account resource limitations and operational viability, we optimize vaccination and treatment plans using optimal control theory to reduce the overall disease burden. Using a combination of optimum control approaches, sensitivity analysis, and fractional calculus, our methodology provides a strong framework for developing personalized interventions that are adaptive to the specific constraints of malaria management in Africa. The knowledge gained from this research has important ramifications for resource allocation and public health policy, enabling evidence-based decision-making to reduce the prevalence of malaria in the region.