Incorporating Hierarchical Structure Into Dynamic Systems: An Application Of Estimating HIV Epidemics At Sub-National And Sub-Population Level

TL;DR

This paper introduces a novel method to incorporate hierarchical information into HIV dynamic models using auxiliary data, enhancing estimates at sub-national and sub-population levels without added computational complexity.

Contribution

It proposes an innovative, efficient approach to include hierarchical data in dynamic HIV models, improving estimates where data are sparse.

Findings

Enhanced predictive accuracy in data-sparse regions

Efficient use of auxiliary data without increasing computational load

Significant improvements in sub-population estimates

Abstract

Dynamic models have been successfully used in producing estimates of HIV epidemics at national level, due to their epidemiological nature and their ability to simultaneously estimate prevalence, incidence, and mortality rates. Recently, HIV interventions and policies have required more information at sub-national and sub-population levels to support local planning, decision making and resource allocation. Unfortunately, many areas and high-risk groups lack sufficient data for deriving stable and reliable results, and this is a critical technical barrier to more stratified estimates. One solution is to borrow information from other areas and groups within the same country. However, directly assuming hierarchical structures within the HIV dynamic models is complicated and computationally time consuming. In this paper, we propose a simple and innovative way to incorporate the hierarchical information into the dynamic systems by using auxiliary data. The proposed method efficiently uses information from multiple areas and risk groups within each country without increasing the computational burden. As a result, the new model improves predictive ability in general with especially significant improvement in areas and risk groups with sparse data.