The aim of this paper is to review the recent literature on HIV mathematical models that evaluate the effect of antiretroviral treatment on mortality, morbidity, HIV and other key outcomes. The focus of our attention is models which explicitly model specific effects of individual antiretroviral drugs.
The number of studies that use mathematical models to evaluate the impact of antiretroviral drugs as a treatment or prevention strategy is increasing.
Many mathematical models are deterministic compartmental models, and do not have the level of detail of specific effects of individual drugs. However, models that include specific antiretroviral drugs have been increasingly employed to assess the cost–effectiveness of prevention interventions, to evaluate benefits and harms (toxicities, side-effects, resistance development) of different regimens and different intervention timing and to predict long-term outcomes of randomized controlled trials (RCTs) that are not usually measured in the time frame of a trial.
The number of models that consider specific antiretroviral drugs, with their own peculiarities, is limited. This factor is particularly the case for dynamic individual-based stochastic models. In order to address some research questions it is necessary to accurately take into consideration implications of toxicities, side-effects, and resistance acquisition, and hence to model specific drugs or at least specific drug classes.
HIV Epidemiology & Biostatistics Group, Research Department of Infection & Population Health, UCL Medical School, London, UK
Correspondence to Valentina Cambiano, HIV Epidemiology & Biostatistics Group, Research Department of Infection & Population Health, UCL Medical School, Royal Free Campus, Rowland Hill Street, London NW3 2PF, UK E-mail: firstname.lastname@example.org