Strategic Control of Drug-Resistant HIV: Multi-Strain Modeling with Diagnosis, Adherence, and Treatment Switching

TL;DR

This study develops a comprehensive HIV transmission model incorporating drug resistance, diagnosis, and treatment switching, and uses optimal control to identify strategies that balance treatment expansion and adherence to meet global targets.

Contribution

It introduces a multi-strain compartmental model with dynamic control strategies, providing new insights into balancing treatment coverage and adherence to control drug-resistant HIV.

Findings

Long-term control depends on adherence-focused interventions.

Optimal strategies can improve resource allocation for treatment and diagnosis.

Model identifies key parameters influencing drug resistance dynamics.

Abstract

A central challenge in Human Immunodeficiency Virus (HIV) public health policy lies in determining whether to universally expand treatment access, despite the risk of sub-optimal adherence and consequent drug resistance, or to adopt a more strategic allocation of resources that balances treatment coverage with adherence support. This dilemma is further complicated by the need for timely switching to second-line therapy, which is critical for managing treatment failure but imposes additional burdens on limited healthcare resources. In this study, we develop and analyze a compartmental model of HIV transmission that incorporates both drug-sensitive and drug-resistant strains, diagnosis status, and treatment progression, including switching to second-line therapy upon detection of resistance. Basic reproduction numbers for both strains are derived, and equilibrium analysis reveals the existence of a disease-free state and two endemic states, where the drug-sensitive strain may be eliminated while the drug-resistant strain persists. Local and global sensitivity analyses are performed, using partial rank correlation coefficient (PRCC) and Sobol methods, to identify key parameters influencing different model outcomes. We extend the model using optimal control theory to assess multiple intervention strategies targeting diagnosis, treatment initiation, and adherence. A novel dynamic control framework is proposed to achieve the UNAIDS 95-95-95 targets through efficient resource allocation. Numerical simulations validate the analytical results and compare the effectiveness and cost-efficiency of control strategies. Our findings highlight that long-term HIV epidemic control depends critically on prioritizing adherence-focused interventions alongside efforts to expand first-line treatment coverage.