Identifying Spatio-Temporal Clustering of the COVID-19 Patterns Using Spatial Statistics: Case Studies of Four Waves in Vietnam

Identifying Spatio-Temporal Clustering of the COVID-19 Patterns Using Spatial Statistics: Case Studies of Four Waves in Vietnam

Anh-huy Hoang, Tien-thanh Nguyen
Copyright: © 2022 |Volume: 13 |Issue: 1 |Pages: 15
ISSN: 1947-9654|EISSN: 1947-9662|EISBN13: 9781683181347|DOI: 10.4018/IJAGR.297517
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MLA

Hoang, Anh-huy, and Tien-thanh Nguyen. "Identifying Spatio-Temporal Clustering of the COVID-19 Patterns Using Spatial Statistics: Case Studies of Four Waves in Vietnam." IJAGR vol.13, no.1 2022: pp.1-15. http://doi.org/10.4018/IJAGR.297517

APA

Hoang, A. & Nguyen, T. (2022). Identifying Spatio-Temporal Clustering of the COVID-19 Patterns Using Spatial Statistics: Case Studies of Four Waves in Vietnam. International Journal of Applied Geospatial Research (IJAGR), 13(1), 1-15. http://doi.org/10.4018/IJAGR.297517

Chicago

Hoang, Anh-huy, and Tien-thanh Nguyen. "Identifying Spatio-Temporal Clustering of the COVID-19 Patterns Using Spatial Statistics: Case Studies of Four Waves in Vietnam," International Journal of Applied Geospatial Research (IJAGR) 13, no.1: 1-15. http://doi.org/10.4018/IJAGR.297517

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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.

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