arXiv subjects

E. Orozco-Acosta

Publications and source records attributed to E. Orozco-Acosta.

3 recordsLinked to original sources

arXiv2023

A scalable approach for short-term disease forecasting in high spatial resolution areal data

Short-term disease forecasting at specific discrete spatial resolutions has become a high-impact decision-support tool in health planning. However, when the number of areas is very large obtaining predictions can be computationally intensive or even unfeasible using standard spatio-temporal models. The purpose of this paper is to provide a method for short-term predictions in high-dimensional areal data based on a newly proposed ``divide-and-conquer" approach. We assess the predictive performance of this method and other classical spatio-temporal models in a validation study that uses cancer mortality data for the 7907 municipalities of continental Spain. The new proposal outperforms traditional models in terms of mean absolute error, root mean square error and interval score when forecasting cancer mortality one, two and three years ahead. Models are implemented in a fully Bayesian framework using the well-known integrated nested Laplace (INLA) estimation technique.

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arXiv2022

Big problems in spatio-temporal disease mapping: methods and software

Fitting spatio-temporal models for areal data is crucial in many fields such as cancer epidemiology. However, when data sets are very large, many issues arise. The main objective of this paper is to propose a general procedure to analyze high-dimensional spatio-temporal count data, with special emphasis on mortality/incidence relative risk estimation. We present a pragmatic and simple idea that permits to fit hierarchical spatio-temporal models when the number of small areas is very large. Model fitting is carried out using integrated nested Laplace approximations over a partition of the spatial domain. We also use parallel and distributed strategies to speed up computations in a setting where Bayesian model fitting is generally prohibitively time-consuming and even unfeasible. Using simulated and real data, we show that our method outperforms classical global models. We implement the methods and algorithms that we develop in the open-source R package bigDM where specific vignettes have been included to facilitate the use of the methodology for non-expert users. Our scalable methodology proposal provides reliable risk estimates when fitting Bayesian hierarchical spatio-temporal models for high-dimensional data.

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arXiv2020

Scalable Bayesian modeling for smoothing disease risks in large spatial data sets

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. The method is implemented in the R add-on package bigDM. Model fitting 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.

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