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Title: Non-stationary spatio-temporal point process modeling for high-resolution COVID-19 data
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.

 
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Award ID(s):
2134037
NSF-PAR ID:
10413061
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
Oxford University Press
Date Published:
Journal Name:
Journal of the Royal Statistical Society Series C: Applied Statistics
Volume:
72
Issue:
2
ISSN:
0035-9254
Page Range / eLocation ID:
p. 368-386
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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