Probabilistic projections of HIV prevalence using Bayesian melding

TL;DR

This paper introduces a Bayesian melding approach to quantify uncertainty in HIV prevalence projections from the UNAIDS EPP model, providing probabilistic estimates and predictive intervals for policy use.

Contribution

It develops a Bayesian framework to incorporate data variability and model uncertainty into HIV prevalence projections, enhancing the assessment of future epidemic trajectories.

Findings

Probabilistic HIV prevalence projections with 95% intervals for Uganda

Bayesian melding effectively quantifies uncertainty in EPP model estimates

Predicted prevalence in 2010 ranges from 2% to 7%

Abstract

The Joint United Nations Programme on HIV/AIDS (UNAIDS) has developed the Estimation and Projection Package (EPP) for making national estimates and short-term projections of HIV prevalence based on observed prevalence trends at antenatal clinics. Assessing the uncertainty about its estimates and projections is important for informed policy decision making, and we propose the use of Bayesian melding for this purpose. Prevalence data and other information about the EPP model's input parameters are used to derive a probabilistic HIV prevalence projection, namely a probability distribution over a set of future prevalence trajectories. We relate antenatal clinic prevalence to population prevalence and account for variability between clinics using a random effects model. Predictive intervals for clinic prevalence are derived for checking the model. We discuss predictions given by the EPP model and the results of the Bayesian melding procedure for Uganda, where prevalence peaked at around 28% in 1990; the 95% prediction interval for 2010 ranges from 2% to 7%.