Person: Orozco Acosta, Erick
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Orozco Acosta
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Erick
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0000-0002-1170-667X
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Publication Open Access Big problems in spatio-temporal disease mapping: methods and software(Elsevier, 2023) Orozco Acosta, Erick; Adin Urtasun, Aritz; Ugarte Martínez, María Dolores; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa, PJUPNA20001Background and objective: 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 areal data, with special emphasis on mortality/incidence relative risk estimation. Methods: We present a pragmatic and simple idea that permits hierarchical spatio-temporal models to be fitted 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 or even unfeasible. Results: 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. Conclusions: Our scalable methodology proposal provides reliable risk estimates when fitting Bayesian hierarchical spatio-temporal models for high-dimensional data.Publication Open Access A scalable approach for short-term disease forecasting in high spatial resolution areal data(Wiley-VCH, 2023) Orozco Acosta, Erick; Riebler, Andrea; Adin Urtasun, Aritz; Ugarte Martínez, María Dolores; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Universidad Pública de Navarra / Nafarroako Unibertsitate PublikoaShort-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 spatiotemporal 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 spatiotemporal 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 1, 2, and 3 years ahead. Models are implemented in a fully Bayesian framework using the well-known integrated nested Laplace estimation technique.Publication Open Access Scalable Bayesian modeling for smoothing disease mapping risks in large spatial data sets using INLA(Elsevier, 2021) Orozco Acosta, Erick; Adin Urtasun, Aritz; Ugarte Martínez, María Dolores; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2Several methods have been proposed in the spatial statistics literature to analyse big data sets in continuous domains. However, new methods for analysing high-dimensional areal data are still scarce. Here, we propose a scalable Bayesian modelling 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 and it is based on the idea of “divide and conquer“. Although this proposal could possibly be implemented using any Bayesian fitting technique, we use INLA here (integrated nested Laplace approximations) as it is now a well-known technique, computationally efficient, and easy for practitioners to handle. We analyse the proposal’s empirical performance in a comprehensive simulation study that considers two model-free settings. Finally, the methodology is applied to analyse 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.Publication Open Access Bayesian modeling approach in Big Data contexts: an application in spatial epidemiology(IEEE, 2020) Orozco Acosta, Erick; Adin Urtasun, Aritz; Ugarte Martínez, María Dolores; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística, Informática y MatemáticasIn this work we propose a novel scalable Bayesian modeling approach to smooth mortality risks borrowing information from neighbouring regions in high-dimensional spatial disease mapping contexts. The method is based on the well-known divide and conquer approach, so that the spatial domain is divided into D subregions where local spatial models can be fitted simultaneously. Model fitting and inference has been carried out using the integrated nested Laplace approximation (INLA) technique. Male colorectal cancer mortality data in the municipalities of continental Spain have been analyzed using the new model proposals. Results show that the new modeling approach is very competitive in terms of model fitting criteria when compared with a global spatial model, and it is computationally much more efficient.