Emergence and spread of Covid-19 initiated diversified researches based on spatial analysis in visualization, exploration, and modelling of this infection. This short communication is an attempt to comprehend the geographic distribution and spatial clustering of Covid-19 in year 2020. Main objective is to spatially analyze Covid-19 incidence rates, identification of hotspots and clusters outliers at global level. Monthly data of reported cases were taken from World Health Organization dashboard and situation reports. Incidence rate was calculated for each country for each month. Spatial autocorrelation techniques of Global Moran are I and Anselian Local Moran’s I were used to examine the spatial clustering and outlier’s detection of Covid-19 incidence in all months of the year. Hotspots and Coldspots variations are examined by using Getis-Ord G*. Mapping was executed in ArcGIS Pro environment. Results reveal significant spatial variation of Covid-19 incidence in WHO regions in different months of pandemic year 2020. Hotspots and high clustering of the disease incidence shows a shift from Western pacific towards Europe and Americas from January to April. Eastern Mediterranean countries also became a part of disease hotspots from the month of July leaving Africa as coldspot during whole year. Highest Moran’s I value of 0.32 with highest z-score of 14 reflects the highly clustered pattern of this pandemic incidence in the month of December in contrary to least clustering of the disease with lowest Moran’s I of 0.02 and z-score of 1.8 in June. Statistically significant variations in disease clustering pattern provides an opportunity for epidemiologists to further explore the disease incidence from ecological perspective.
Fatima, Munazza; Arshad, Sana; Butt, Ibtisam; and Arshad, Saba
"Geospatial Clustering and Hot Spot Detection of COVID-19 Incidence in 2020: A Global Analysis,"
International Journal of Geospatial and Environmental Research: Vol. 8:
1, Article 4.
Available at: https://dc.uwm.edu/ijger/vol8/iss1/4