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A hierarchical spatio-temporal model to analyze relative risk variations of COVID-19: a focus on Spain, Italy and Germany
dc.contributor.author | Jalilian, Abdollah | |
dc.contributor.author | Mateu, Jorge | |
dc.date.accessioned | 2022-11-25T14:09:35Z | |
dc.date.available | 2022-11-25T14:09:35Z | |
dc.date.issued | 2021-04-23 | |
dc.identifier.citation | Jalilian, A., Mateu, J. A hierarchical spatio-temporal model to analyze relative risk variations of COVID-19: a focus on Spain, Italy and Germany. Stoch Environ Res Risk Assess 35, 797–812 (2021). https://doi.org/10.1007/s00477-021-02003-2 | ca_CA |
dc.identifier.issn | 1436-3259 | |
dc.identifier.issn | 1436-3240 | |
dc.identifier.uri | http://hdl.handle.net/10234/200916 | |
dc.description.abstract | The novel coronavirus disease (COVID-19) has spread rapidly across the world in a short period of time and with a heterogeneous pattern. Understanding the underlying temporal and spatial dynamics in the spread of COVID-19 can result in informed and timely public health policies. In this paper, we use a spatio-temporal stochastic model to explain the temporal and spatial variations in the daily number of new confirmed cases in Spain, Italy and Germany from late February 2020 to mid January 2021. Using a hierarchical Bayesian framework, we found that the temporal trends of the epidemic in the three countries rapidly reached their peaks and slowly started to decline at the beginning of April and then increased and reached their second maximum in the middle of November. However decline and increase of the temporal trend seems to show different patterns in Spain, Italy and Germany. | ca_CA |
dc.format.extent | 16 p. | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Springer-Verlag GmbH Germany, part of Springer Nature | ca_CA |
dc.relation.isPartOf | Stochastic Environmental Research and Risk Assessment, Vol. 35 (2021) | ca_CA |
dc.rights | © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | ca_CA |
dc.subject | autoregressive model | ca_CA |
dc.subject | besag model | ca_CA |
dc.subject | COVID-19 | ca_CA |
dc.subject | disease mapping | ca_CA |
dc.subject | spatio-temporal prediction | ca_CA |
dc.subject | 62M30 | ca_CA |
dc.subject | 62M10 | ca_CA |
dc.subject | 91G70 | ca_CA |
dc.title | A hierarchical spatio-temporal model to analyze relative risk variations of COVID-19: a focus on Spain, Italy and Germany | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.1007/s00477-021-02003-2 | |
dc.rights.accessRights | info:eu-repo/semantics/restrictedAccess | ca_CA |
dc.relation.publisherVersion | https://link.springer.com/article/10.1007/s00477-021-02003-2 | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
project.funder.name | Universitat Jaume I | ca_CA |
project.funder.name | Generalitat Valenciana | ca_CA |
project.funder.name | Ministerio de Ciencia e Innovación | ca_CA |
oaire.awardNumber | UJI-B2018-04 | ca_CA |
oaire.awardNumber | AICO/2019/198 | ca_CA |
oaire.awardNumber | MTM2016-78917-R | ca_CA |
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