Sensitivity Analysis of HIV and Tuberculosis Co-Infection with Interventions (Published)
Human Immunodeficiency Virus (HIV) and Tuberculosis (TB) represent a synergistic syndemic where immunocompromising HIV infection significantly escalates the risk of active TB reactivation and progression. We developed and analysis a co-infection compartmental model which incorporates features such as treatment, vaccination, disease progression, and modification parameters to capture the interaction between single and coinfections.The basic reproduction numbers for the HIV and TB sub-models are derived using the next generation matrix method, and stability analysis shows that the disease-free equilibrium is stable when the reproduction number is less than unity. Sensitivity analysis identifies transmission rates and modification parameters as key drivers of disease spread, while treatment and control measures reduce infection levels. Numerical simulations confirm the analytical results and demonstrate that the interaction between HIV and TB significantly increases the overall disease burden. The findings highlight the importance of integrated intervention strategies for effective disease control.
Keywords: HIV, Intervention, TB, basic reproduction., sensitivity
Cluster Analysis of the Incidences on HIV in Nigeria (Published)
Data clustering is a vital tool when it comes to understanding data items with similar characteristics in a data set for the sake of grouping. Clustering may be for understanding or utility. Clustering for understanding, which is the focus of this work deals with grouping items with common characteristics in order to better understand a dataset and to identify possible or pre-interest sub-groups that could be formed from such data. The HIV prevalence statistics in Nigeria is measured bi-annually across 36 states and FCT which were zoned under 6 geo-political zones happens to be a suitable data to implement this subject matter. Cluster Analysis was implemented through the general methods of Hierarchical (agglomerative nesting) and Partitioning methods (K-Means). These techniques where implemented on the platform of R (Statistical Computing Language) to cluster HIV prevalence rate in Nigeria so as to find out states that could be considered same category and to investigate the concentration of the disease in respect to geo-political zones. Relative type of validation was used for cluster validation (a mechanism for evaluating the correctness of clustering).
Keywords: Clustering analysis, HIV, Nigeria, Pregnant Women, data