A mathematical model for HIV dynamics with multiple infections: implications for immune escape

Area of Science:

• Virology
• Mathematical Biology
• Immunology

Background:

• Viral recombination during multiple infections contributes to diversity.
• Understanding competition between wild and mutant strains under multiple infections is crucial.

Purpose of the Study:

• To develop a mathematical model analyzing viral dynamics with multiple infections.
• To investigate the impact of multiple infections on strain competition and viral loads.
• To assess the role of cytotoxic T lymphocytes (CTLs) and combination antiretroviral therapy (cART) in multiple infection scenarios.

Main Methods:

• Development of a novel mathematical model incorporating two viral strains, two infection modes, and multiple infections.
• Derivation of reproductive numbers and analysis of equilibrium stability using Lyapunov's direct method and limiting systems theory.
• Numerical simulations and sensitivity analysis to evaluate parameter effects on viral loads.

Main Results:

• Viral dynamics show similarities across infection multiplicities, but quadruple infections lead to fiercest strain competition.
• A threshold for CTLs is identified to minimize viral load; weak or strong responses increase viral load.
• Intermediate CTL responses highlight the impact of mutant fitness costs on evolutionary dynamics.

Conclusions:

• Multiple infections can alter viral dynamics, potentially leading to underestimation of viral loads during cART.
• Optimizing CTL responses is critical for viral load control in the context of multiple infections.
• The study provides insights into viral evolution and therapeutic strategies under complex infection scenarios.