Non-stationary spatio-temporal point process modeling for high-resolution COVID-19 data

2021/09/18 by Dong, Zheng, Zhu, Shixiang, Xie, Yao +2 · 1 citation
#Applications (stat.AP) #FOS: Computer and information sciences

paper · doi:10.48550/arxiv.2109.09029

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.

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