Parallel and distributed Bayesian modelling for analysing high-dimensional spatio-temporal count data
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This paper proposes a general procedure to analyse high-dimensional spatio-temporal count data, with special emphasis on relative risks estimation in cancer epidemiology. Model fitting is carried out using integrated nested Laplace approximations over a partition of the spatio-temporal domain. This is a simple idea that works very well in this context as the models are defined to borrow strength locally in space and time, providing reliable risk estimates. Parallel and distributed strategies are proposed to speed up computations in a setting where Bayesian model fitting is generally prohibitively time-consuming and even unfeasible. We evaluate the whole procedure in a simulation study with a twofold objective: to estimate risks accurately and to detect extreme risk areas while avoiding false positives/negatives. We show that our method outperforms classical global models. A real data analysis comparing the global models and the new procedure is also presented.
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