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In the absence of widespread access to individualized laboratory monitoring, which forms an integral part of HIV patient management in resource-rich settings, the roll-out of highly active antiretroviral therapy (HAART) in resource-limited settings has adopted a public health approach based on standard HAART protocols and clinical/immunological definitions of therapy failure. The cost-effectiveness of HIV-1 viral load monitoring at the individual level in such settings has been debated, and questions remain over the long-term and population-level impact of managing HAART without it. Computational models that accurately predict virological response to HAART using baseline data including CD4 count, viral load and genotypic resistance profile, as developed by the Resistance Database Initiative, have significant potential as an aid to treatment selection and optimization. Recently developed models have shown good predictive performance without the need for genotypic data, with viral load emerging as by far the most important variable. This finding provides further, indirect support for the use of viral load monitoring for the long-term optimization of HAART in resource-limited settings.