article · Tanzania Journal of Science
A mathematical framework combining the Laplace transformation with the Caputo fractional-order derivative models the co-dynamics of Ebola and malaria in Sub-Saharan Africa. The model incorporates socioeconomic factors that influence disease spread and evaluates transmission dynamics using the basic reproduction number. Numerical simulations conducted in Maple 18 illustrate how fractional-order derivatives affect disease progression. Furthermore, applying the Laplace-Adomian decomposition method simplifies the underlying nonlinear equations and helps generate viable control solutions. The findings highlight the value of translating mathematical insights into practical intervention strategies, urging proactive collaboration among health stakeholders. By adopting these measures, public health systems can formulate more resilient management programmes to address simultaneous infectious disease emergencies across affected regions.
Managing overlapping outbreaks of Ebola and malaria requires understanding both biological spread and socioeconomic realities. Using advanced mathematical modelling helps identify critical transmission factors and develop targeted control strategies. Providing clearer projections of co-infection dynamics allows public health authorities and community leaders to design more resilient, proactive responses to major epidemics in Sub-Saharan Africa.
The model provides an analytical foundation for epidemic decision-support tools and public health planning software. Primary users would include epidemiological modellers, regional public health agencies, and policy planners designing disease intervention programmes. Because the findings are derived from numerical simulations using Maple 18, the research is at an early conceptual stage and requires operational adaptation and real-world data validation before deployment.
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The study presents a novel mathematical framework for addressing Ebola and malaria concerns in Sub-Saharan Africa that combines Laplace transformation with Caputo fractional order derivative. It takes into account socioeconomic aspects that influence disease dynamics and uses the basic reproduction number to quantify transmission dynamics. Extensive numerical simulations using Maple 18 software are used to investigate the effect of fractional order derivatives on disease dynamics. It shows how the Laplace-Adomian decomposition approach simplifies nonlinear equations and generates control solutions. It emphasizes the necessity of turning discoveries into concrete plans and encourages stakeholders to be proactive in implementing them. Overall, the study emphasizes the importance of proactive disease management measures and the promise of novel approaches to treating infectious diseases. Stakeholders may create a more resilient response to these health emergencies by working together to adopt these measures.
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DOI: 10.4314/tjs.v50i2.5
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