Progress and challenges in modelling country-level HIV/AIDS epidemics: the UNAIDS Estimation and Projection Package 2007

Abstract

The UNAIDS Estimation and Projection Package (EPP) was developed to aid in country-level estimation and short-term projection of HIV/AIDS epidemics. This paper describes advances reflected in the most recent update of this tool (EPP 2007), and identifies key issues that remain to be addressed in future versions. The major change to EPP 2007 is the addition of uncertainty estimation for generalised epidemics using the technique of Bayesian melding, but many additional changes have been made to improve the user interface and efficiency of the package. This paper describes the interface for uncertainty analysis, changes to the user interface for calibration procedures and other user interface changes to improve EPP's utility in different settings. While formal uncertainty assessment remains an unresolved challenge in low-level and concentrated epidemics, the Bayesian melding approach has been applied to provide analysts in these settings with a visual depiction of the range of models that may be consistent with their data. In fitting the model to countries with longer-running epidemics in sub-Saharan Africa, a number of limitations have been identified in the current model with respect to accommodating behaviour change and accurately replicating certain observed epidemic patterns. This paper discusses these issues along with their implications for future changes to EPP and to the underlying UNAIDS Reference Group model.

Figure 1. (A) The 250 best fitting curves for the surveillance data points shown in black from a sample of 25 000 randomly generated combinations of the parameters r, f₀, t₀ and φ in the UNAIDS Reference Group model. (B) Of the original sample of 25 000 curves, the 20 shown include the best-fitting curve (defined by the maximum likelihood) and those that are statistically indistinguishable from the best fit. This example illustrates how multiple combinations of the Reference Group model parameters may fit a given data set with approximately equal validity.

Figure 2. The EPP 2007 uncertainty analysis interface, showing the results for rural areas in Namibia.

Figure 3. The display of the parameter values selected by EPP 2007, showing the preferred values of the Reference Group parameters in curves that were better fits to the data. The red lines represent the initial distribution sampled to generate trial values.

Figure 4. Advanced options display. This gives the user the ability to change the ranges on the UNAIDS Reference Group model parameters before generating parameter sets for trial curves. It also gives the user the ability to eliminate unrealistic curves with constraints on prevalence.

Figure 5. An example of combining the uncertainty from multiple projections to give an estimate of the overall uncertainty in the national projection.