A Moran’s I autocorrelation and spatial cluster analysis for identifying Coronavirus disease COVID-19 in Iraq using GIS approach

Abstract

Iraq is one of the states in the world, affected with coronavirus. Mapping spatial patterns analysis distribution of disease incidence and danger can be assist as a suitable tool for detecting exposures of public health concern. A geographical information system (GIS)-based methodology to examine the relationship between the reported incidence of coronavirus and spatial patterns analysis in eighteen provinces of Iraq was analyzed in 2020. So, the study was applying spatial statistics to inspect the spatial patterns and areas of clustering detection to describe the pattern of coronavirus in Iraq. In this study, local Moran's I has been applied to measure spatial distribution of coronavirus in the study area and examined how provinces were spread or clustered. Spatial patterns statistics were used to apply Moran’s I test and it estimated considerable negative spatial autocorrelation of coronavirus disease incidences from 24/02/2020 to 06/04/2020. The results described spatially random clustered and spatial pattern of this disease in the study area. The study determined that the coronavirus cases were increased in the northeastern- and southwestern-side provinces of Iraq.

Keywords

• coronavirus
• cluster analysis
• moran’s i
• spatial statistics, iraq