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.

翻译：大多数COVID-19研究通常以州或县级别的整体感染数据作为报告指标。这种聚合方式往往会遗漏病毒传播的精细细节。本文分析了哥伦比亚卡利市的高分辨率COVID-19数据集，该数据集记录了每例确诊病例的精确时间与地点。我们开发了一种配备神经网络驱动核函数的非平稳时空点过程，以捕捉COVID-19病例间的异质性相关性。该核函数经过精心设计，在增强表达力的同时保持模型可解释性。我们还融入了城市地标施加的部分外生影响。与现有最优方法相比，我们的方法在预测新发COVID-19病例方面表现更优，并能就大城市中疾病传播所涉及的个体间时空交互作用提供重要洞见。