Abstract
Tuberculosis (TB) continues to be a major global health challenge, with millions of new cases and deaths each year despite the massive efforts and funding put in the fight against the disease. In this paper, we develop a mathematical model to evaluate the impact of TB-funded prevention programs on the transmission dynamics of TB. The model incorporates stages of TB infection (latent and active), and accounts for the effects of treatment, funding and TB-funded prevention programs. Our analysis shows that increased funding and enhanced prevention programs reduce the number of active TB cases, thereby decreasing the reproduction number and TB endemicity. Specifically, higher funding rates lead to improved prevention and treatment outcomes, resulting in the lowering of the effective reproduction number (R0) and reduced transmission. The model's steady states are determined and it is shown that the model has a disease-free equilibrium that is locally asymptotically stable whenever R0<1 and multiple endemic equilibria for R0c<R0<1 and a unique endemic equilibrium for R0>1. The model is shown to exhibit a backward bifurcation that vanishes as the funding for TB is increased. The paper also highlights that treatment alone, while beneficial, is less effective than a combined strategy involving funding and prevention. Numerical simulations are carried out and the influences of various parameters on the effective reproduction number are investigated. The implications of TB-funded prevention programs on TB dynamics and control of TB are discussed and valuable insights for policymakers in designing effective TB control programs are highlighted.
Original language | English |
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Pages (from-to) | 1037-1054 |
Number of pages | 18 |
Journal | Infectious Disease Modelling |
Volume | 10 |
Issue number | 4 |
DOIs | |
Publication status | Published - Dec 2025 |
Keywords
- Backward bifurcation
- Modelling
- Prevention programs
- Simulations
- Stability analysis
- TB-Funding
- Tuberculosis
ASJC Scopus subject areas
- Health Policy
- Infectious Diseases
- Applied Mathematics