Mathematical modeling of HIV-HCV co-infection model: Impact of parameters on reproduction number

Oluwakemi E Abiodun1, Olukayode Adebimpe2, James A Ndako1

  • 1Physical Sciences, Landmark University, Omu Aran, State, 251101, Nigeria.

F1000Research
|January 16, 2023
PubMed

Insights

Mathematical modeling of HIV/HCV co-infection reveals that treating Hepatitis C Virus (HCV) first in co-infected individuals can reduce the co-infection reproduction number and liver cancer risk. Public health interventions are crucial for reducing mono- and co-infections.

Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Public Health

Background:

  • Hepatitis C Virus (HCV) and Human Immunodeficiency Virus (HIV) are blood-borne viruses with significant global prevalence, particularly among people who inject drugs (PWID).
  • HIV co-infection exacerbates HCV progression, leading to increased viral load, fibrosis, and end-stage liver disease.
  • Understanding the dynamics of HIV/HCV co-infection is critical for developing effective control strategies.

Purpose of the Study:

  • To develop and analyze a mathematical model simulating the dynamics of HIV/HCV co-infection.
  • To incorporate factors such as dual therapy, vertical HIV transmission, HIV awareness status, treatment adherence, and condom use into the model.
  • To evaluate the impact of different intervention strategies on disease transmission and progression.

Main Methods:

  • The study employed mathematical modeling techniques to analyze the dynamical behavior of HIV/HCV co-infection.
  • Positivity, boundedness, and equilibria of the model were established using established mathematical theorems.
  • Reproduction numbers were calculated using the next-generation matrix approach, and stability analysis was performed using linearization and Bendixson-Dulac criteria.

Main Results:

  • Increased HIV treatment dropout was associated with reduced HIV treatment adherence and increased prevalence in other compartments.
  • Prioritizing HCV treatment in dually infected individuals was shown to decrease the co-infection reproduction number (R_c), thereby lowering the risk of liver cancer.
  • The model highlighted the impact of various parameters on disease dynamics.

Conclusions:

  • Public health policies should focus on comprehensive strategies, including awareness campaigns about risks associated with multiple sexual partners and promoting consistent condom use.
  • Continued treatment for chronic HCV and AIDS is essential for managing co-infections.
  • Implementing these interventions can effectively reduce the burden of mono- and co-infections in the population.