The utility of antiretroviral therapy (ART) in the management of human immunodeficiency virus (HIV) is being challenged by growing HIV drug resistance (HIVDR). Although simulation modelling is useful for understanding complex problems, the extent to which it is used in HIVDR is unknown. This review aimed to determine how modelling has been used to inform HIVDR interventions and how its use can align with the World Health Organization (WHO) Global Action Plan (GAP) for HIVDR 2017-2021. This review involved a literature search across PubMed, Scopus, Web of Science, and Embase databases. Articles published after the introduction of ART, 1997 to 28th November 2025, were considered. Relevant information, including metadata, model descriptions, interventions, and their outcomes, was extracted. Findings from included papers were categorized according to their area of focus within the five strategic objectives of the WHO GAP for HIVDR 2017–2021. A total of 2346 articles were screened, and 17 articles were included in the final analysis. Most studies modelled HIVDR in sub-Saharan Africa (n = 13). Acquired resistance (n = 15) was assessed in most of the studies, followed by transmitted resistance (n = 7) and pretreatment resistance (n = 3). Most of the models were stochastic models(n = 11), with about one third of them analyzing cost effectiveness(n = 6). Ten models focused on the WHO GAP HIVDR strategic objective of prevention and response, four aligned with the objective of monitoring and surveillance, while the remaining three assessed a combination of the two objectives. None of the models assessed the remaining three objectives: research and innovation, laboratory capacity, or governance and enabling mechanisms. This review identifies a need for more cross-cutting analyses of multiple strategic objectives for HIVDR, including system-wide models to provide holistic insights into the complexity surrounding HIVDR. Employing patient and public involvement (PPI) in model development and intervention design would further strengthen model validity and transition to real-world settings (PROSPERO ID: CRD42024553557).
Aiko et al. (Thu,) studied this question.