Modeling the impact of antiretroviral drugs for HIV treatment and prevention in resource-limited settings

Clinical trials show that antiretroviral drugs (ARVs) reduce the risk of HIV transmission when used as treatment (ART) for infected persons or as pre-exposure prophylaxis (PrEP) for uninfected persons. However, there are concerns that widespread ARV use may promote the spread of drug-resistant HIV. We compare two published mathematical models that predict the impact of ARVs used for HIV prevention in resource-limited settings. Both predict that ART and PrEP in combination would prevent more infections than the current practice of ART alone. The first model, which uses several optimistic and simplifying assumptions, predicts that a combination intervention will decrease drug resistance and may eventually eliminate HIV. The second, which incorporates behavioral heterogeneity and less optimistic ARV-related assumptions, predicts that a combination intervention increases drug resistance and will not eliminate HIV. To be useful policy-informing tools, infectious disease models must incorporate realistic structural and parameter assumptions, including variation in human behavior.