Estimating HIV prevalence remains a challenging effort throughout the world.  Despite advancements in our understanding of the epidemiology of HIV and techniques for monitoring the epidemic, HIV incidence levels are still unknown in most parts of the world. To maximise the use of HIV surveillance data the Joint United Nations Programme on AIDS (UNAIDS) and the World Health Organisation (WHO) in collaboration with researchers from a range of organisations have co-ordinated the development of universally applicable methods. All of the ‘tools’ described here use mathematical models to analyse epidemiological data. Mathematical models provide a framework for the analysis of data.

• The UNAIDS Estimation and Projection Package (EPP) provides a framework within which HIV surveillance data can be explored, generating a national representation of the HIV epidemic to date. The model has been used for generalized epidemics to generate estimates of the HIV epidemic and is being further developed to improve its robustness, applicability and capacity.

• A workbook approach has been developed to estimate HIV prevalence for countries with low-level and concentrated epidemics.  The process forces consideration of the epidemiological situation and high and low estimates of both risk groups’ sizes and prevalences, but is fraught with potential errors due to poor quality data and a lack of comparability across populations.

• The advocacy and planning message to be derived from estimates of trends in HIV prevalence requires that the demographic consequences of the epidemic be explored.  The Spectrum Projection Package is currently used for this function. The package uses standard demographic methods representing an age and sex structured population and incorporates rates of fertility and mortality associated with AIDS after distributing incident HIV infections throughout the population over time.

Different information sources and different assumptions are used to create national estimates.  The accuracy of these estimates depends critically on the quantity and quality of HIV prevalence data, as well as the assumptions used to translate these data into national estimates of the number of adults living with HIV, new infections and deaths among adults, and the number of children newly infected with HIV, living with HIV, and child deaths.