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The COVID-19 pandemic, caused by the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been described as an unprecedented global health and socio-economic crisis (Kassema, 2020). The latest data show that, globally, as of 18 June 2021, the COVID-19 pandemic has resulted in more than 177.1 million confirmed cases and more than 3.8 million deaths (WHO, 2021). The pandemic will create socio-economic burdens differently in developed and developing counties of the World due to the loss of human resources (United Nations, 2020). It is, therefore, the use of modern techniques, especially geographical approaches, is of great help in the fight against the COVID-19 pandemic in general, and in the understanding of the spatial distribution and managing the COVID-19 infection in particular.
COVID-19-related data such as the geographical locations of (visited) COVID-19 cases which have a spatial and geographic dimension can be considered a type of spatial object and can be studied with the help of a Geographic Information System (GIS) and spatial statistics. The clustering phenomenon of these spatial objects in general and COVID-19 infection in particular, itself is tied to Tobler’s First Law of Geography, which offers that while all objects are related in space - objects nearer to one another are more related (Sadler & Furr-Holden, 2019, Tobler, 2004). It is, therefore, geographical approaches are fundamental to keep infectious diseases and their geographical distribution under control (Cicalò & Valentino, 2019). Following the idea of Tobler (2004), commonly used statistics for spatial auto-correlation analysis such as global spatial statistics (Moran's I, Getis-Ord G* and Geary’s c) and local indicators of spatial association (LISA) have been successfully applied in epidemiological studies (see Robinson (2000) for a detailed review) in general and in the study of COVID-19 pandemic (see the following review) in particular. Among these spatial statistics, Getis-Ord G* and Moran's I have been widely used in the study of the COVID-19 pandemic. The Getis-Ord G* statistic has proven its effectiveness in hotspot detection in epidemiology studies, thus, it has been widely applied to detect the COVID-19 hotspots at global, regional, country, and province-levels.
At a global level, with the combination of the global, local Moran’s I and Getis-Ord G* statistics, Fatima et al. (2021) successfully detected the spatial clustering and hotspots of COVID incidence in 2020. Later, when performing spatio-temporal analysis and hotspots detection of COVID-19 using geographic information system (GIS) with new confirmed COVID-19 cases collected at the end of March and April 2020, Shariati et al. (2020) also revealed that hotspot analysis coupled with local Moran's I provide a scrupulous and objective approach to determine the locations of statistically significant clusters of COVID-19 cases.