All Times ET
Keywords: Disease mapping, High-dimensional data, INLA, Smoothing
Spatio-temporal disease mapping studies the geographical distribution of a disease in space and its evolution in time. Many statistical techniques have been proposed during the last years for analyzing disease risks, most of them including spatial and temporal random effects to smooth risks borrowing information from neighbouring regions and time periods. Despite the enormous expansion of modern computers and the development of new software and estimation techniques to make fully Bayesian inference, dealing with massive data is still computationally challenging. In this work, we propose a scalable Bayesian modeling approach to smooth mortality or incidence risks in high-dimensional spatio-temporal disease mapping context. The method is based on the well-known "divide and conquer" approach, so that local models can be fitted simultaneously reducing the computational time substantially. Model fitting and inference is carried out using the integrated nested Laplace (INLA) approximation technique. We illustrate the models's behaviour by estimating lung cancer mortality risks in almost 8000 municipalities of Spain during the period 1991-2015. A simulation study is also conducted to evaluate the performance of this new scalable modeling approach in comparison with usual spatio-temporal models in disease mapping.