Estimating HIV Epidemics for Sub-National Areas

TL;DR

This paper introduces a mixture model approach to improve HIV epidemic estimates at sub-national levels by leveraging parameter dependence across areas, enhancing predictive accuracy especially where data are sparse.

Contribution

It proposes a novel mixture model that links parameters across sub-national areas, improving estimates without refitting data or software.

Findings

Mixture model outperforms independent models in predictive accuracy.

Applicable to multiple countries in Sub-Saharan Africa.

Enhances epidemic estimation with limited data.

Abstract

As the global HIV pandemic enters its fourth decade, increasing numbers of surveillance sites have been established which allows countries to look into the epidemics at a finer scale, e.g. at sub-national levels. Currently, the epidemic models have been applied independently to the sub-national areas within countries. However, the availability and quality of the data vary widely, which leads to biased and unreliable estimates for areas with very few data. We propose to overcome this issue by introducing the dependence of the parameters across areas in a mixture model. The joint distribution of the parameters in multiple areas can be approximated directly from the results of independent fits without needing to refit the data or unpack the software. As a result, the mixture model has better predictive ability than the independent model as shown in examples of multiple countries in Sub-Saharan Africa.