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Most COVID-19 studies commonly report figures of the overall infection at astate- or county-level. This aggregation tends to miss out on fine details ofvirus propagation. In this paper, we analyze a high-resolution COVID-19 datasetin Cali, Colombia, that records the precise time and location of everyconfirmed case. We develop a non-stationary spatio-temporal point processequipped with a neural network-based kernel to capture the heterogeneouscorrelations among COVID-19 cases. The kernel is carefully crafted to enhanceexpressiveness while maintaining model interpretability. We also incorporatesome exogenous influences imposed by city landmarks. Our approach outperformsthe state-of-the-art in forecasting new COVID-19 cases with the capability tooffer vital insights into the spatio-temporal interaction between individualsconcerning the disease spread in a metropolis.

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