Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/114834
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dc.contributor.authorAlvarez, Luisen_US
dc.contributor.authorMorel, Jean Daviden_US
dc.contributor.authorMorel, Jean Michelen_US
dc.date.accessioned2022-05-23T14:44:54Z-
dc.date.available2022-05-23T14:44:54Z-
dc.date.issued2022en_US
dc.identifier.issn2079-7737en_US
dc.identifier.urihttp://hdl.handle.net/10553/114834-
dc.description.abstractThe sanitary crisis of the past two years has focused the public’s attention on quantitative indicators of the spread of the COVID-19 pandemic. The daily reproduction number Rt, defined by the average number of new infections caused by a single infected individual at time t, is one of the best metrics for estimating the epidemic trend. In this paper, we provide a complete observation model for sampled epidemiological incidence signals obtained through periodic administrative measurements. The model is governed by the classic renewal equation using an empirical reproduction kernel, and subject to two perturbations: a time-varying gain with a weekly period and a white observation noise. We estimate this noise model and its parameters by extending a variational inversion of the model recovering its main driving variable Rt . Using Rt, a restored incidence curve, corrected of the weekly and festive day bias, can be deduced through the renewal equation. We verify experimentally on many countries that, once the weekly and festive days bias have been corrected, the difference between the incidence curve and its expected value is well approximated by an exponential distributed white noise multiplied by a power of the magnitude of the restored incidence curve.en_US
dc.languageengen_US
dc.relation.ispartofBiologyen_US
dc.sourceBiology [ISSN 2079-7737], n. 11 (4), 540en_US
dc.subject120601 Construcción de algoritmosen_US
dc.subject3202 Epidemologiaen_US
dc.subject.otherIncidence curveen_US
dc.subject.otherPandemicen_US
dc.subject.otherCOVID-19en_US
dc.subject.otherReproduction kernelen_US
dc.subject.otherTime dependent reproduction numberen_US
dc.subject.otherAdministrative noiseen_US
dc.subject.otherExponential distributionen_US
dc.subject.otherRenewal equationen_US
dc.subject.otherVariational inversion methoden_US
dc.titleModeling COVID-19 incidence by the renewal equation after removal of administrative bias and noiseen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typearticleen_US
dc.identifier.doi10.3390/biology11040540en_US
dc.identifier.scopus2-s2.0-85128263806-
dc.identifier.isiWOS:000785544800001-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.identifier.issue4-
dc.relation.volume11en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.description.numberofpages22en_US
dc.utils.revisionen_US
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-INFen_US
dc.description.sjr0,779
dc.description.jcr4,2
dc.description.sjrqQ1
dc.description.jcrqQ2
dc.description.scieSCIE
dc.description.miaricds10,5
item.fulltextCon texto completo-
item.grantfulltextopen-
crisitem.author.deptGIR Modelos Matemáticos-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.orcid0000-0002-6953-9587-
crisitem.author.parentorgDepartamento de Informática y Sistemas-
crisitem.author.fullNameÁlvarez León, Luis Miguel-
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