Spatial distribution and mapping of COVID-19 pandemic in Afghanistan using GIS technique

Geographic information science (GIS) has emerged as a unique tool that is extremely valuable in various research which involves spatial–temporal aspects. The geographical distribution of the epidemic is considered a significant characteristic that can be analyzed using GIS and spatial statistics. Proper knowledge can assist in controlling, mitigating, and mapping factors for detecting the transmission as well as the disease dynamics, and it provides geographical information of the outbreak and it can also give a glimpse of the disease trend and hotspots as well as provide ways to further evaluate the associated risk. This study analyzed the countries’ total confirmed cases, total death cases, and the total recovered cases using an (IDW) geospatial technique which is an inherent tool used in ArcMap for spatial analysis. In order to identify the hotspots for COVID-19 cases, the Getis-Ord Gi* statistic method was applied with a confidence level of 95% in Herat and 90% for Kabul, Kapisa, and Logar provinces. The data considered in this research ranged from the period of 23rd July 2020 to 24th February 2021. All the COVID-19 confirmed, recovered, and death cases were correlated with provincial population density using the Pearson Correlation coefficient. Among the total cases 54,487, 32% cases were reported in the capital of the country (Kabul), and the mortality rate was 31% followed by Herat (18% deaths), Balkh (7% deaths), and Nangarhar (6% deaths). Most of the recoveries were observed in Kabul with (30%) followed by Herat (16%), Bamyan (10%), Balkh (5%), and Kandahar (5%). The results for Global Moran’s I showed that the incidence rate of the total COVID-19 cases was in the random pattern, with the Moran Index of − 0.14. Given the z-score of − 1.62, the pattern does not appear to be significantly different than random. There was a strong correlation between the COVID-19 variables and population density [with r(33) = 0.827], [r(33) = 0.819] and [r(33) = 0.817] for the total cases, death cases, and recovered cases, respectively. Even though GIS has limited applicability in detecting the type and its spatial pattern of the epidemic, there is a high potential to use these tools in managing and controlling the pandemic. Moreover, GIS helps us better in comprehending the epidemic and assists us in addressing those fractions of the population and communities which are underserved during the disease outbreak. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022.