Identifying Spatio-Temporal Clustering of the COVID-19 Patterns Using Spatial Statistics

Abstract

An outbreak of the COVID-19 pandemic caused by the SARS CoV 2 has profoundly affected the world. This study aimed to identify the spatio-temporal clustering of COVID-19 patterns using spatial statistics. Local Moran’s I spatial statistic and Moran scatterplot were first used to identify high-high and low-low clusters and low-high and high-low outliers of COVID-19 cases. Getis-Ord’s〖 G〗_i^* statistic was then applied to detect hotspots and coldspots. We finally illustrated the used method by using a dataset of 10,742 locally transmitted cases in four COVID-19 waves in 63 prefecture-level cities/provinces in Vietnam. The results showed that significant low-high spatial outliers of COVID-19 cases were first detected in the north-eastern region in the first wave and in the central region in the second wave. Whereas, spatial clustering of high-high, low-high and high-low was mainly found in the north-eastern region in the last two waves. It can be concluded that spatial statistics are of great help in understanding the spatial clustering of COVID-19 patterns.