Modelling time to elimination for Tuberculosis in Nigeria

Abstract

Tuberculosis (TB) continues to pose a major public health threat in Nigeria, with high incidence rates, significant drug resistance, and strong association with HIV. This study aimed to model the timeframe for TB elimination in Nigeria under current and enhanced intervention scenarios using a deterministic compartmental model. Secondary data from 2023 to 2024 were used, with parameters sourced from literature and national estimates. The model included seven compartments: susceptible, vaccinated, exposed, infectious (drug-susceptible and resistant), isolated, treatment, and recovered. The effective reproductive number (RE) was calculated as 3.6, indicating that, even with ongoing interventions, one infected individual could infect an average of three others. Descriptive analysis showed a mean TB case count of 499,000, BCG vaccination coverage of 72.1%, treatment coverage at 75%, and diagnostic coverage at 48% in 2023. Sensitivity analysis revealed that the infection rate and treatment success significantly impacted the RE value, while drug resistance and natural death moderately influenced transmission. Scenario simulations demonstrated that increasing BCG coverage, enhancing case detection through mass screening, and improving treatment success—especially for resistant strains—can drastically reduce the TB burden. A combined intervention strategy showed the greatest potential for accelerating progress toward elimination. These findings shows the importance of integrated and intensified interventions, backed by sufficient funding and policy support, to achieve the national and global TB elimination targets.

Keywords: Tuberculosis Elimination, Mathematical Modelling,  Drug-Resistant TB,  Public Health Interventions