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Dynamic models have been successfully used in producing estimates of HIVepidemics at the national level due to their epidemiological nature and theirability to estimate prevalence, incidence, and mortality rates simultaneously.Recently, HIV interventions and policies have required more information atsub-national levels to support local planning, decision making and resourceallocation. Unfortunately, many areas lack sufficient data for deriving stableand reliable results, and this is a critical technical barrier to morestratified estimates. One solution is to borrow information from other areaswithin the same country. However, directly assuming hierarchical structureswithin the HIV dynamic models is complicated and computationallytime-consuming. In this paper, we propose a simple and innovative way toincorporate hierarchical information into the dynamical systems by usingauxiliary data. The proposed method efficiently uses information from multipleareas within each country without increasing the computational burden. As aresult, the new model improves predictive ability and uncertainty assessment.

Dynamic Models Augmented by Hierarchical Data: An Application Of Estimating HIV Epidemics At Sub-National And Sub-Population Level