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A stochastic Bayesian bootstrapping model for COVID-19 data
dc.contributor.author | Calatayud, Julia | |
dc.contributor.author | Jornet, Marc | |
dc.contributor.author | Mateu, Jorge | |
dc.date.accessioned | 2022-04-11T11:02:22Z | |
dc.date.available | 2022-04-11T11:02:22Z | |
dc.date.issued | 2022-01-11 | |
dc.identifier.citation | Calatayud, J., Jornet, M. & Mateu, J. A stochastic Bayesian bootstrapping model for COVID-19 data. Stoch Environ Res Risk Assess (2022). https://doi.org/10.1007/s00477-022-02170-w | ca_CA |
dc.identifier.uri | http://hdl.handle.net/10234/197309 | |
dc.description.abstract | We provide a stochastic modeling framework for the incidence of COVID-19 in Castilla-Leon (Spain) for the period March 1, 2020 to February 12, 2021, which encompasses four waves. Each wave is appropriately described by a generalized logistic growth curve. Accordingly, the four waves are modeled through a sum of four generalized logistic growth curves. Pointwise values of the twenty input parameters are fitted by a least-squares optimization procedure. Taking into account the significant variability in the daily reported cases, the input parameters and the errors are regarded as random variables on an abstract probability space. Their probability distributions are inferred from a Bayesian bootstrap procedure. This framework is shown to offer a more accurate estimation of the COVID-19 reported cases than the deterministic formulation. | ca_CA |
dc.format.extent | 11 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Springer | ca_CA |
dc.relation.isPartOf | Stochastic Environmental Research and Risk Assessment (2022) | ca_CA |
dc.rights | © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | ca_CA |
dc.subject | Bayesian bootstrap | ca_CA |
dc.subject | COVID-19 reported infections and waves | ca_CA |
dc.subject | deterministic and stochastic modeling | ca_CA |
dc.subject | least-squares fitting | ca_CA |
dc.subject | multiple generalized logistic growth curves | ca_CA |
dc.subject | random parameters and errors | ca_CA |
dc.title | A stochastic Bayesian bootstrapping model for COVID-19 data | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.1007/s00477-022-02170-w | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.type.version | info:eu-repo/semantics/acceptedVersion | ca_CA |
project.funder.name | Ministerio de Ciencia, Innovación y Universidades, Spain | ca_CA |
project.funder.name | Generalitat Valenciana | ca_CA |
project.funder.name | Agencia Estatal de Investigación (MCIN/AEI/10.13039/501100011033), Spain | ca_CA |
oaire.awardNumber | ID2019-107392RB-I00 | ca_CA |
oaire.awardNumber | AICO/2019/198 | ca_CA |
oaire.awardNumber | PID2020-115270GB-I00 | ca_CA |
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