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.
@article{arxiv.2109.09029,
title = {Non-stationary spatio-temporal point process modeling for high-resolution COVID-19 data},
author = {Zheng Dong and Shixiang Zhu and Yao Xie and Jorge Mateu and Francisco J. Rodríguez-Cortés},
journal= {arXiv preprint arXiv:2109.09029},
year = {2023}
}