Journal ArticleNon-stationary spatio-temporal point process modeling for high-resolution COVID-19 dataDong, Zheng; Zhu, Shixiang; Xie, Yao; Mateu, Jorge; Rodríguez-Cortés, Francisco JAbstract 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.Oxford Academic2023-03-2810434378Journal of the Royal Statistical Society Series C: Applied Statistics722368 to 3860035-9254https://doi.org/10.1093/jrsssc/qlad0132134037National Science Foundation