Non-stationary spatio-temporal point process modeling for high-resolution COVID-19 data
Ver/ Abrir
Impacto
Scholar |
Otros documentos de la autoría: Dong, Zheng; Zhu, Shixiang; Xie, Yao; Mateu, Jorge; Rodríguez-Cortés, Francisco Javier
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
comunitat-uji-handle4:
INVESTIGACIONMetadatos
Título
Non-stationary spatio-temporal point process modeling for high-resolution COVID-19 dataFecha de publicación
2023-03-28Editor
Royal Statistical Society; Oxford University PressISSN
0035-9254; 1467-9876Cita bibliográfica
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/qlad013Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/submittedVersionPalabras clave / Materias
Resumen
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 ... [+]
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. [-]
Publicado en
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/qlad013Entidad financiadora
National Science Foundation | Universitat Jaume I | Universidad Nacional de Colombia
Código del proyecto o subvención
CCF-1650913 | CMMI-2015787 | DMS1938106 | DMS-1830210 | Grant/Award Number: 51279
Derechos de acceso
© (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)
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/openAccess
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/openAccess
Aparece en las colecciones
- MAT_Articles [762]