  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.
