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Non-stationary spatio-temporal point process modeling for high-resolution COVID-19 data
dc.contributor.author | Dong, Zheng | |
dc.contributor.author | Zhu, Shixiang | |
dc.contributor.author | Xie, Yao | |
dc.contributor.author | Mateu, Jorge | |
dc.contributor.author | Rodríguez-Cortés, Francisco Javier | |
dc.date.accessioned | 2023-06-16T08:25:44Z | |
dc.date.available | 2023-06-16T08:25:44Z | |
dc.date.issued | 2023-03-28 | |
dc.identifier.citation | Zheng 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/qlad013 | ca_CA |
dc.identifier.issn | 0035-9254 | |
dc.identifier.issn | 1467-9876 | |
dc.identifier.uri | http://hdl.handle.net/10234/202868 | |
dc.description.abstract | Most 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.extent | 30 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Royal Statistical Society | ca_CA |
dc.publisher | Oxford University Press | ca_CA |
dc.relation.isPartOf | 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/qlad013 | ca_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.uri | http://rightsstatements.org/vocab/InC/1.0/ | ca_CA |
dc.subject | spatio-temporal point processes | ca_CA |
dc.subject | self-exciting models | ca_CA |
dc.subject | nonstationary kernel | ca_CA |
dc.subject | neural networks | ca_CA |
dc.subject | COVID-19 | ca_CA |
dc.title | Non-stationary spatio-temporal point process modeling for high-resolution COVID-19 data | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.1093/jrsssc/qlad013 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.type.version | info:eu-repo/semantics/submittedVersion | ca_CA |
project.funder.name | National Science Foundation | ca_CA |
project.funder.name | Universitat Jaume I | ca_CA |
project.funder.name | Universidad Nacional de Colombia | ca_CA |
oaire.awardNumber | CCF-1650913 | ca_CA |
oaire.awardNumber | CMMI-2015787 | ca_CA |
oaire.awardNumber | DMS1938106 | ca_CA |
oaire.awardNumber | DMS-1830210 | ca_CA |
oaire.awardNumber | Grant/Award Number: 51279 | ca_CA |
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