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http://hdl.handle.net/2445/175437
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DC Field | Value | Language |
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dc.contributor.author | Fernández Fontelo, Amanda | - |
dc.contributor.author | Moriña, David | - |
dc.contributor.author | Cabaña, Alejandra | - |
dc.contributor.author | Arratia, Argimiro | - |
dc.contributor.author | Puig i Casado, Pere | - |
dc.date.accessioned | 2021-03-19T13:02:51Z | - |
dc.date.available | 2021-03-19T13:02:51Z | - |
dc.date.issued | 2020-12-03 | - |
dc.identifier.issn | 1932-6203 | - |
dc.identifier.uri | http://hdl.handle.net/2445/175437 | - |
dc.description.abstract | The present paper introduces a new model used to study and analyse the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) epidemic-reported-data from Spain. This is a Hidden Markov Model whose hidden layer is a regeneration process with Poisson immigration, Po-INAR(1), together with a mechanism that allows the estimation of the under-reporting in non-stationary count time series. A novelty of the model is that the expectation of the unobserved process's innovations is a time-dependent function defined in such a way that information about the spread of an epidemic, as modelled through a Susceptible-Infectious-Removed dynamical system, is incorporated into the model. In addition, the parameter controlling the intensity of the under-reporting is also made to vary with time to adjust to possible seasonality or trend in the data. Maximum likelihood methods are used to estimate the parameters of the model. | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | eng | - |
dc.publisher | Public Library of Science (PLoS) | - |
dc.relation.isformatof | Reproducció del document publicat a: https://doi.org/10.1371/journal.pone.0242956 | - |
dc.relation.ispartof | PLoS One, 2020, vol. 15, num. 12, p. e0242956 | - |
dc.relation.uri | https://doi.org/10.1371/journal.pone.0242956 | - |
dc.rights | cc-by (c) Fernández Fontelo, Amanda et al., 2020 | - |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es | - |
dc.source | Articles publicats en revistes (Econometria, Estadística i Economia Aplicada) | - |
dc.subject.classification | SARS-CoV-2 | - |
dc.subject.classification | Epidèmies | - |
dc.subject.classification | Processos de Markov | - |
dc.subject.other | SARS-CoV-2 | - |
dc.subject.other | Epidemics | - |
dc.subject.other | Markov processes | - |
dc.title | Estimating the real burden of disease under a pandemic situation: The SARS-CoV2 case | - |
dc.type | info:eu-repo/semantics/article | - |
dc.type | info:eu-repo/semantics/publishedVersion | - |
dc.identifier.idgrec | 709151 | - |
dc.date.updated | 2021-03-19T13:02:51Z | - |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | - |
dc.identifier.pmid | 33270713 | - |
Appears in Collections: | Articles publicats en revistes (Econometria, Estadística i Economia Aplicada) |
Files in This Item:
File | Description | Size | Format | |
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709151.pdf | 2.73 MB | Adobe PDF | View/Open |
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