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Activity Number: 520 - SPEED: Infectious Diseases, Spatial Modeling and Environmental Exposures, Speed 2
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 11:15 AM
Sponsor: Section on Statistics in Epidemiology
Abstract #307903
Title: Small Area Estimation of HIV Incidence Using Bayesian Hierarchical Model
Author(s): Ben Sheng* and Le Bao and Ray Shiraishi and Steven Gutreuter and Jeffrey Eaton
Companies: Penn State University and Pennsylvania State University and CDC and CDC and Imperial College London
Keywords: Small area estimation; HIV incidence; Bayesian hierarchical model; Incidence assay; Parameter estimation; Generalized linear model

Small area estimation of HIV incidence is challenging because sample surveys are designed to provide accurate estimates for “large areas” (e.g., national or regional) instead of “small areas” (i.e., subnational or subpopulation). In this presentation, we propose a generalized linear model in a Bayesian hierarchical framework to estimate small-area incidence as a parameter. For the model, we insert an informative prior based on an independent estimate of HIV incidence at large-area level, and use a likelihood based on incidence assay recency data. We first test our model on simulated data, and then apply it to the Malawi PHIA survey. Finally, we propose model evaluation techniques in this case where external cross-validation is impractical.

Authors who are presenting talks have a * after their name.

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