Using Geographic Information Systems (GIS) to Model the Clustering Effect of COVID-19 in South-Western Zimbabwe

Abstract

The purpose of the study was to model the clustering of COVID-19 in Bulawayo, Zimbabwe. A cross-sectional study design was used to provide a snapshot of the occurrence of COVID-19 in Bulawayo at a particular time. About 246 COVID-19 cases were randomly selected from the list of cases that occurred in Bulawayo as of 1 August 2020. The data was analyzed in ArcGIS using spatial autocorrelation and hotspot analysis. From the observed pattern, the results demonstrated a significant overall spatial autocorrelation and clustering of COVID-19 cases in Bulawayo. The hotspot analysis showed hotspot localities around the Western Suburbs such as Nkulumane, Cowdry Park, and Luveve. These are high-density suburbs, endorsing that pattern of COVID-19 infections is related to the population density pattern in Bulawayo. In conclusion, hotspot areas detected in this study can help identify future infectious disease surveillance.