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Dynamic models have been successfully used in producing estimates of HIVepidemics at national level, due to their epidemiological nature and theirability to simultaneously estimate prevalence, incidence, and mortality rates.Recently, HIV interventions and policies have required more information atsub-national and sub-population levels to support local planning, decisionmaking and resource allocation. Unfortunately, many areas and high-risk groupslack sufficient data for deriving stable and reliable results, and this is acritical technical barrier to more stratified estimates. One solution is toborrow information from other areas and groups within the same country.However, directly assuming hierarchical structures within the HIV dynamicmodels is complicated and computationally time consuming. In this paper, wepropose a simple and innovative way to incorporate the hierarchical informationinto the dynamic systems by using auxiliary data. The proposed methodefficiently uses information from multiple areas and risk groups within eachcountry without increasing the computational burden. As a result, the new modelimproves predictive ability in general with especially significant improvementin areas and risk groups with sparse data.

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