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Activity Number: 21 - Bayesian Disease Mapping and Spatial Epidemiology: New Directions and New Frontiers
Type: Topic-Contributed
Date/Time: Sunday, August 8, 2021 : 1:30 PM to 3:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #317060
Title: Dealing with Large Data Problems in Spatial Disease Mapping
Author(s): Maria Dolores Ugarte* and Aritz Adin and Erick Orozco-Acosta
Companies: Universidad Publica de Navarra and Universidad Publica de Navarra and Universidad Publica de Navarra
Keywords: Hierarchical Models; INLA; Mixture models; Spatial Epidemiology

Several methods have been proposed in the spatial statistics literature for the analysis of big data sets in continuous domains. However, new methods for analyzing high-dimensional areal data are still scarce. Here, we propose a scalable Bayesian modeling approach for smoothing mortality (or incidence) risks in high-dimensional data, that is, when the number of small areas is very large. Model ftting and inference is based on the idea of \divide and conquer" and use integrated nested Laplace approximations and numerical integration. We analyze the proposal's empirical performance in a comprehensive simulation study that consider two model-free settings. Finally, the methodology is applied to analyze male colorectal cancer mortality in Spanish municipalities showing its benefits with regard to the standard approach in terms of goodness of fit and computational time.

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

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