Non-stationary spatio-temporal point process modeling for high-resolution COVID-19 data
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Altres documents de l'autoria: Dong, Zheng; Zhu, Shixiang; Xie, Yao; Mateu, Jorge; Rodríguez-Cortés, Francisco Javier
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Títol
Non-stationary spatio-temporal point process modeling for high-resolution COVID-19 dataData de publicació
2023-03-28Editor
Royal Statistical Society; Oxford University PressISSN
0035-9254; 1467-9876Cita bibliogràfica
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/qlad013Tipus de document
info:eu-repo/semantics/articleVersió
info:eu-repo/semantics/submittedVersionParaules clau / Matèries
Resum
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. [-]
Publicat a
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/qlad013Entitat finançadora
National Science Foundation | Universitat Jaume I | Universidad Nacional de Colombia
Codi del projecte o subvenció
CCF-1650913 | CMMI-2015787 | DMS1938106 | DMS-1830210 | Grant/Award Number: 51279
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