Orozco Acosta, ErickRiebler, AndreaAdin Urtasun, AritzUgarte Martínez, María Dolores2024-05-082024-05-082023Orozco-Acosta, E., Riebler, A., Adin, A., Ugarte, M. D. (2023) A scalable approach for short-term disease forecasting in high spatial resolution areal data. Biometrical Journal, 65(8), 1-8. https://doi.org/10.1002/bimj.202300096.0323-384710.1002/bimj.202300096https://academica-e.unavarra.es/handle/2454/48078Short-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.application/pdfapplication/zipeng© 2023 The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.cancer projectionsdisease mappinghigh-dimensional dataintegrated nested Laplace approximationArtículo / Artikulua2024-05-08Acceso abierto / Sarbide irekiainfo:eu-repo/semantics/openAccess