Spatial Variation and Hotspot Detection of COVID-19 Cases in Kazakhstan, 2020

Abstract Background COVID-19 is the life-threatening infectious disease of zoonotic origin that has epidemic spread in Kazakhstan. The use of geoepidemiological techniques to detect territories of risk (hotspots) is essential for implementing control measures in the target area. This study aims to conduct spatial analysis of the COVID-19 epidemic in Kazakhstan to increase understanding of the current features of the virus distribution and to explore its geographical patterns, especially its spatial clustering. Methods We used geographic information software (QGIS, GeoDa) to perform spatial analysis (Nearest Neighbour Analysis, Global Moran's I, Getis-Ord Gi*, LISA) and to detect COVID-19 risk clusters in the entire territory of Kazakhstan. Results Clusters of COVID-19 cases were detected using the Getis-Ord GI* analysis (with first order Queen Continuity matrix) in two oblasts of Kazakhstan: Almaty (Iliyskiy, Karasayskiy, Raiymbekskiy, Talgarskiy rayons and city of Almaty) and Aqmola (Arshalynskiy, Ereymengauskiy, Korgalzhynskiy and Shortandinskiy rayons). LISA defined four High-High clusters of COVID-19 cases in the Almaty oblast (Iliyskiy, Karasayskiy and Talgarskiy rayons) and city of Almaty.