Non-stationary spatio-temporal point process modeling for high-resolution COVID-19 data
针对哥伦比亚卡利市高分辨率COVID-19数据，提出一种带神经网络核的非平稳时空点过程模型，捕捉病例间的异质相关性并融入地标影响，在预测新病例上优于现有方法，同时保持模型可解释性。
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.