ESTIMATING HIV EPIDEMICS FOR SUBNATIONAL AREAS

As the global HIV pandemic enters its fifth decade, increasing numbers of countries use routine HIV testing among pregnant women to monitor their epidemics, allowing governments to look into the epidemics at a finer scale, for example, at subnational levels. Currently, the epidemic model that de-scribes the dynamics of the spread of HIV consists of a set of differential equations and is applied independently to each subnational area. However, the availability of the data varies widely which leads to biased and unreli-able estimates for areas with very few data points. We propose to overcome this issue by introducing dependence in the parameters across areas. The proposed method better reconstructs the epidemic trajectories than the indepen-dent model as shown in multiple countries in Sub-Saharan Africa. We also offer an approximate method for parameter estimation that is much less com-putationally burdensome than direct parameter estimation. Compared to direct parameter estimation from the dependent model, the approximate method provides competitive parameter estimation in simulations and the application of HIV subepidemic estimation.