Publication: Modeling latent spatio-temporal disease incidence using penalized composite link models
dc.affiliation.dpto | UC3M. Departamento de Estadística | es |
dc.contributor.author | Lee Hwang, Dae-Jin | |
dc.contributor.author | Durbán Reguera, María Luz | |
dc.contributor.author | Ayma Anza, Diego Armando | |
dc.contributor.author | Van De Kassteele, Jan | |
dc.contributor.funder | Agencia Estatal de Investigación (España) | es |
dc.date.accessioned | 2024-01-15T18:24:50Z | |
dc.date.available | 2024-01-15T18:24:50Z | |
dc.date.issued | 2022-03-10 | |
dc.description.abstract | Epidemiological data are frequently recorded at coarse spatio-temporal resolutions to protect confidential information or to summarize it in a compact manner. However, the detailed patterns followed by the source data, which may be of interest to researchers and public health officials, are overlooked. We propose to use the penalized composite link model (Eilers PCH (2007)), combined with spatio-temporal P-splines methodology (Lee D.-J., Durban M (2011)) to estimate the underlying trend within data that have been aggregated not only in space, but also in time. Model estimation is carried out within a generalized linear mixed model framework, and sophisticated algorithms are used to speed up computations that otherwise would be unfeasible. The model is then used to analyze data obtained during the largest outbreak of Q-fever in the Netherlands. | en |
dc.description.sponsorship | This study was supported in the form of funding by Ministerio de Ciencia, Innovación y Universidades (Grant Nos. SEV 2017-0718 and MTM2017-82379-R) awarded to DJL, (Grant No. PID2019-10490RB-100) awarded to MD, by Ministerio de Economía, Industria y Competitividad, Gobierno de España (Grant No. MTM2014-52184-P) awarded to DJL, MD, and DA, and by Agencia Estatal de Investigación(Grant No. PID2020-115882RB-I00) awarded to DJL. | en |
dc.identifier.bibliographicCitation | Lee D-J, Durba´n M, Ayma D, Van de Kassteele J (2022) Modeling latent spatio-temporal disease incidence using penalized composite link models. PLoS ONE 17(3): e0263711 | en |
dc.identifier.doi | https://doi.org/10.1371/journal.pone.0263711 | |
dc.identifier.issn | 1932-6203 | |
dc.identifier.publicationfirstpage | e0263711 | es |
dc.identifier.publicationissue | 3 | es |
dc.identifier.publicationtitle | PLoS One | es |
dc.identifier.publicationvolume | 17 | es |
dc.identifier.uri | https://hdl.handle.net/10016/39258 | |
dc.identifier.uxxi | AR/0000030790 | |
dc.language.iso | eng | es |
dc.publisher | Indiana University | es |
dc.relation.projectID | Gobierno de España. MTM2014-52184-P | es |
dc.relation.projectID | Gobierno de España. SEV 2017-0718 | es |
dc.relation.projectID | Gobierno de España. MTM2017-82379-R | es |
dc.relation.projectID | Gobierno de España. PID2019-10490RB-100 | es |
dc.relation.projectID | Gobierno de España. PID2020-115882RB-I00 | es |
dc.rights | © 2022 Lee et al. | es |
dc.rights | Atribución 3.0 España | * |
dc.rights.accessRights | open access | en |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject.eciencia | Estadística | es |
dc.title | Modeling latent spatio-temporal disease incidence using penalized composite link models | en |
dc.type | research article | en |
dc.type.hasVersion | VoR | es |
dspace.entity.type | Publication |
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