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dc.contributor.authorDong, Zheng
dc.contributor.authorZhu, Shixiang
dc.contributor.authorXie, Yao
dc.contributor.authorMateu, Jorge
dc.contributor.authorRodríguez-Cortés, Francisco Javier
dc.date.accessioned2023-06-16T08:25:44Z
dc.date.available2023-06-16T08:25:44Z
dc.date.issued2023-03-28
dc.identifier.citationZheng Dong and others, Non-stationary spatio-temporal point process modeling for high-resolution COVID-19 data, Journal of the Royal Statistical Society Series C: Applied Statistics, Volume 72, Issue 2, May 2023, Pages 368–386, https://doi.org/10.1093/jrsssc/qlad013ca_CA
dc.identifier.issn0035-9254
dc.identifier.issn1467-9876
dc.identifier.urihttp://hdl.handle.net/10234/202868
dc.description.abstractMost COVID-19 studies commonly report figures of the overall infection at a state- or county-level. This aggregation tends to miss out on fine details of virus propagation. In this paper, we analyze a high-resolution COVID-19 dataset in Cali, Colombia, that records the precise time and location of every confirmed case. We develop a non-stationary spatio-temporal point process equipped with a neural network-based kernel to capture the heterogeneous correlations among COVID-19 cases. The kernel is carefully crafted to enhance expressiveness while maintaining model interpretability. We also incorporate some exogenous influences imposed by city landmarks. Our approach outperforms the state-of-the-art in forecasting new COVID-19 cases with the capability to offer vital insights into the spatio-temporal interaction between individuals concerning the disease spread in a metropolis.ca_CA
dc.format.extent30 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherRoyal Statistical Societyca_CA
dc.publisherOxford University Pressca_CA
dc.relation.isPartOfJournal of the Royal Statistical Society Series C: Applied Statistics, Volume 72, Issue 2, May 2023, Pages 368–386, https://doi.org/10.1093/jrsssc/qlad013ca_CA
dc.rights© (RSS) Royal Statistical Society 2023. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/pages/standard-publication-reuse-rights)ca_CA
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/ca_CA
dc.subjectspatio-temporal point processesca_CA
dc.subjectself-exciting modelsca_CA
dc.subjectnonstationary kernelca_CA
dc.subjectneural networksca_CA
dc.subjectCOVID-19ca_CA
dc.titleNon-stationary spatio-temporal point process modeling for high-resolution COVID-19 dataca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttps://doi.org/10.1093/jrsssc/qlad013
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.type.versioninfo:eu-repo/semantics/submittedVersionca_CA
project.funder.nameNational Science Foundationca_CA
project.funder.nameUniversitat Jaume Ica_CA
project.funder.nameUniversidad Nacional de Colombiaca_CA
oaire.awardNumberCCF-1650913ca_CA
oaire.awardNumberCMMI-2015787ca_CA
oaire.awardNumberDMS1938106ca_CA
oaire.awardNumberDMS-1830210ca_CA
oaire.awardNumberGrant/Award Number: 51279ca_CA


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