Estimating HIV epidemics for subnational areas
Le Bao, Xiaoyue Niu, Mary Mahy, Peter D. Ghys
The Annals of Applied Statistics · 2023 · Volume 17 · Issue 3
Abstract
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 describes 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 unreliable 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 independent model as shown in multiple countries in Sub-Saharan Africa. We also offer an approximate method for parameter estimation that is much less computationally 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.
Keywords
correlated model, dynamic system, HIV epidemics, importance sampling
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