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Modeling latent spatio-temporal disease incidence using penalized composite link models

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorLee Hwang, Dae-Jin
dc.contributor.authorDurbán Reguera, María Luz
dc.contributor.authorAyma Anza, Diego Armando
dc.contributor.authorVan De Kassteele, Jan
dc.contributor.funderAgencia Estatal de Investigación (España)es
dc.date.accessioned2024-01-15T18:24:50Z
dc.date.available2024-01-15T18:24:50Z
dc.date.issued2022-03-10
dc.description.abstractEpidemiological 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.sponsorshipThis 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.bibliographicCitationLee 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): e0263711en
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0263711
dc.identifier.issn1932-6203
dc.identifier.publicationfirstpagee0263711es
dc.identifier.publicationissue3es
dc.identifier.publicationtitlePLoS Onees
dc.identifier.publicationvolume17es
dc.identifier.urihttps://hdl.handle.net/10016/39258
dc.identifier.uxxiAR/0000030790
dc.language.isoenges
dc.publisherIndiana Universityes
dc.relation.projectIDGobierno de España. MTM2014-52184-Pes
dc.relation.projectIDGobierno de España. SEV 2017-0718es
dc.relation.projectIDGobierno de España. MTM2017-82379-Res
dc.relation.projectIDGobierno de España. PID2019-10490RB-100es
dc.relation.projectIDGobierno de España. PID2020-115882RB-I00es
dc.rights© 2022 Lee et al.es
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaEstadísticaes
dc.titleModeling latent spatio-temporal disease incidence using penalized composite link modelsen
dc.typeresearch articleen
dc.type.hasVersionVoRes
dspace.entity.typePublication
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