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.
CITATION STYLE
Dong, Z., Zhu, S., Xie, Y., Mateu, J., & Rodríguez-Cortés, F. J. (2023). Non-stationary spatio-temporal point process modeling for high-resolution COVID-19 data. Journal of the Royal Statistical Society. Series C: Applied Statistics, 72(2), 368–386. https://doi.org/10.1093/jrsssc/qlad013
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