Identification Of Locally Transmitted COVID-19 Spatial Clusters And Hotspots

Abstract

Background: The coronavirus disease 2019 (COVID-19) is an emerging and rapidly evolving profound pandemic, which causes severe acute respiratory syndrome and results in significant case fatality around the world. We conducted this study to identify locally transmitted COVID-19 spatial clusters and hotspots in this phrase of the fourth wave in Vietnam. Data used and Methods: A total of 9,192 locally transmitted cases confirmed in this phrase in the fourth wave were used in study. Global and local Moran’s I and Getis-Ord’s G_i^* statistics were employed to identify spatial autocorrelation and hotspots of COVID-19 cases. Results: It was found that global Moran’s I statistic indicates a robust spatial autocorrelation of COVID-19 cases. Local Moran’s I statistic successfully identified three high-high spatial clusters of COVID-19 cases in Bac Giang (5,083 cases), Bac Ninh (1,407 cases), and Hanoi (464 cases). In addition, hotspots of COVID-19 cases were mainly detected in Bac Giang (5,083 cases), Bac Ninh (1,470 cases), Hanoi (464 cases), Hai Duong (51 cases), and Thai Nguyen (7 cases). Conclusion: The results of this work offer new perspectives on the geostatistical analysis of COVID-19 clusters and hotspots, which could help policy planners anticipate the dynamics of spatiotemporal transmission and develop critical control measures for SARS-CoV-2 in Vietnam. Future pandemics and epidemics can be avoided and controlled with the help of geospatial analysis techniques.