Mathematical Modelling of Tuberculosis Outbreak in an East African Country Incorporating Vaccination and Treatment
Kayode Oshinubi, Olumuyiwa James Peter, Emmanuel Addai, Enock Mwizerwa, Oluwatosin Babasola, Ifeoma Veronica Nwabufo, Ibrahima Sane, Umar Muhammad Adam, Adejimi Adeniji, Janet O. Agbaje
Source record
Source: Crossref
Published: Jul 17, 2023
DOI: 10.3390/computation11070143
Open original source ↗Source abstract
In this paper, we develop a deterministic mathematical epidemic model for tuberculosis outbreaks in order to study the disease’s impact in a given population. We develop a qualitative analysis of the model by showing that the solution of the model is positive and bounded. The global stability analysis of the model uses Lyapunov functions and the threshold quantity of the model, which is the basic reproduction number is estimated. The existence and uniqueness analysis for Caputo fractional tuberculosis outbreak model is presented by transforming the deterministic model to a Caputo sense model. The deterministic model is used to predict real data from Uganda and Rwanda to see how well our model captured the dynamics of the disease in the countries considered. Furthermore, the sensitivity analysis of the parameters according to R0 was considered in this study. The normalised forward sensitivity index is used to determine the most sensitive variables that are important for infection control. We simulate the Caputo fractional tuberculosis outbreak model using the Adams–Bashforth–Moulton approach to investigate the impact of treatment and vaccine rates, as well as the disease trajectory. Overall, our findings imply that increasing vaccination and especially treatment availability for infected people can reduce the prevalence and burden of tuberculosis on the human population.
Evidence graph
No public relationships recorded yet.
Integrity note: This page is a factual metadata record created by deterministic ingestion. It is not a claim that the work moves a mathematical frontier or has been independently verified.