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April 8, 2026Journal of Applied Mathematics0 citationsOpen Access

Analysis of a Mathematical Model on the Dynamics of TB–HCV Coinfection With Medication

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KMKizito MohammedHNHasifa NampalaPAPius Ariho

Key Points

  • The aim is to analyze the dynamics of TB and HCV coinfection and the role of medication in managing it.
  • Employs a deterministic model to describe TB-HCV dynamics
  • Conducts analytical and numerical analyses
  • Performs sensitivity analysis on transmission rates
  • Runs numerical simulations to assess treatment effects
  • Stable equilibrium observed when the effective reproduction number is less than one
  • Increased endemicity of infections with higher human-to-human contact rates
  • Numerical simulations show treatment rate reduction leads to lower infection prevalence

Abstract

This study employs a deterministic model that describes the combined dynamics of TB and HCV aimed at determining the impact of medication in the management of TB–HCV coinfection. The analytical and numerical analyses indicate that if the TB–HCV coinfection effective reproduction number is less than unity, the absence of both TB and HCV infections constitutes a stable equilibrium. Additionally, the stability analysis reveals that when the TB–HCV coinfection effective reproduction number is greater than one, the endemic equilibria become stable attractors, thus, a long‐term coexistence of both infections. Sensitivity analysis shows that an increase in the rate of human‐to‐human contact between the TB (or HCV)‐infected individuals and the susceptible population and the likelihood of a contact resulting in TB (or HCV) transmission results in a higher endemicity of the infection. Numerical simulations indicate that increasing the rate at which humans with coinfection are treated decreases the widespread presence of TB and HCV in the population. We therefore recommend that health facilities be stocked with antibiotics for TB and antiviral medications for HCV for easy access by individuals who test positive to the respective infections.

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Cite This Study

Mohammed et al. (2026) studied this question.

synapsesocial.com/papers/69d5f14b74eaea4b11a7ae79https://doi.org/10.1155/jama/8033235
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