SummaryThe Joint United Nations Programme on HIV/AIDS (UNAIDS) has decided to use Bayesian melding as the basis for its probabilistic projections of HIV prevalence in countries with generalized epidemics. This combines a mechanistic epidemiological model, prevalence data and expert opinion. Initially, the posterior distribution was approximated by sampling-importanceresampling, which is simple to implement, easy to interpret, transparent to users and gave acceptable results for most countries. For some countries, however, this is not computationally efficient because the posterior distribution tends to be concentrated around nonlinear ridges and can also be multimodal. We propose instead Incremental Mixture Importance Sampling (IMIS), which iteratively builds up a better importance sampling function. This retains the simplicity and transparency of sampling importance resampling, but is much more efficient computationally. It also leads to a simple estimator of the integrated likelihood that is the basis for Bayesian model comparison and model averaging. In simulation experiments and on real data it outperformed both sampling importance resampling and three publicly available generic Markov chain Monte Carlo algorithms for this kind of problem.

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ObjectiveThe UNAIDS Estimation and Projection Package (EPP) is a tool for country-level estimation and short-term projection of HIV/AIDS epidemics based on fitting observed HIV surveillance data on prevalence. This paper describes the adaptations made in EPP 2009, the latest version of this tool, as new issues have arisen in the global response, in particular the global expansion of antiretroviral therapy (ART).ResultsBy December 2008 over 4 million people globally were receiving ART, substantially improving their survival. EPP 2009 required modifications to correctly adjust for the effects of ART on incidence and the resulting increases in HIV prevalence in populations with high ART coverage. Because changing incidence is a better indicator of program impact, the 2009 series of UNAIDS tools also focuses on calculating incidence alongside prevalence. Other changes made in EPP 2009 include: an improved procedure, incremental mixture importance sampling, for efficiently generating more accurate uncertainty estimates; provisions to vary the urban/rural population ratios in generalised epidemics over time; introduction of a modified epidemic model that accommodates behaviour change in low incidence settings; and improved procedures for calibrating models. This paper describes these changes in detail, and discusses anticipated future changes in the next version of EPP.

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Objective:Describe modifications to the latest version of the Joint United Nations Programme on AIDS (UNAIDS) Estimation and Projection Package component of Spectrum (EPP 2013) to improve prevalence fitting and incidence trend estimation in national epidemics and global estimates of HIV burden.Methods:Key changes made under the guidance of the UNAIDS Reference Group on Estimates, Modelling and Projections include: availability of a range of incidence calculation models and guidance for selecting a model; a shift to reporting the Bayesian median instead of the maximum likelihood estimate; procedures for comparison and validation against reported HIV and AIDS data; incorporation of national surveys as an integral part of the fitting and calibration procedure, allowing survey trends to inform the fit; improved antenatal clinic calibration procedures in countries without surveys; adjustment of national antiretroviral therapy reports used in the fitting to include only those aged 15–49 years; better estimates of mortality among people who inject drugs; and enhancements to speed fitting.Results:The revised models in EPP 2013 allow closer fits to observed prevalence trend data and reflect improving understanding of HIV epidemics and associated data.Conclusion:Spectrum and EPP continue to adapt to make better use of the existing data sources, incorporate new sources of information in their fitting and validation procedures, and correct for quantifiable biases in inputs as they are identified and understood. These adaptations provide countries with better calibrated estimates of incidence and prevalence, which increase epidemic understanding and provide a solid base for program and policy planning.