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

Orozco Acosta, Erick; Riebler, Andrea; Adin Urtasun, Aritz; Ugarte Martínez, María Dolores

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

dc.contributor.author	Orozco Acosta, Erick
dc.contributor.author	Riebler, Andrea
dc.contributor.author	Adin Urtasun, Aritz
dc.contributor.author	Ugarte Martínez, María Dolores
dc.contributor.department	Estadística, Informática y Matemáticas	es_ES
dc.contributor.department	Estatistika, Informatika eta Matematika	eu
dc.contributor.department	Institute for Advanced Materials and Mathematics - INAMAT2	en
dc.contributor.funder	Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa	es
dc.date.accessioned	2024-05-08T07:54:20Z
dc.date.available	2024-05-08T07:54:20Z
dc.date.issued	2023
dc.date.updated	2024-05-08T07:39:29Z
dc.description.abstract	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 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.	en
dc.description.sponsorship	This research has been supported by the project PID2020-113125RB- I00/MCIN/AEI/10.13039/501100011033 (principal investigator: M.Dolores Ugarte). Open access funding provided by Universidad Pública de Navarra.	en
dc.format.mimetype	application/pdf	en
dc.format.mimetype	application/zip	en
dc.identifier.citation	Orozco-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.	es_ES
dc.identifier.doi	10.1002/bimj.202300096	es_ES
dc.identifier.issn	0323-3847	es_ES
dc.identifier.uri	https://academica-e.unavarra.es/handle/2454/48078
dc.language.iso	eng	en
dc.publisher	Wiley-VCH	en
dc.relation.ispartof	Biometrical Journal (2023), vol. 65(8)	es_ES
dc.relation.projectID	info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113125RB-I00/ES/	en
dc.relation.publisherversion	https://doi.org/10.1002/bimj.202300096
dc.rights	© 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.	en
dc.rights.accessRights	Acceso abierto / Sarbide irekia	es
dc.rights.accessRights	info:eu-repo/semantics/openAccess	en
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject	cancer projections	en
dc.subject	disease mapping	en
dc.subject	high-dimensional data	en
dc.subject	integrated nested Laplace approximation	en
dc.title	A scalable approach for short-term disease forecasting in high spatial resolution areal data	en
dc.type	info:eu-repo/semantics/article
dc.type.version	Versión publicada / Argitaratu den bertsioa	es
dc.type.version	info:eu-repo/semantics/publishedVersion	en
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