Spatial Structure and Climatic Associations with Covid-19 Cases Across the Globe

  • Olanrewaju Lawal
  • Anyiam Felix Emeka
Keywords: COVID-19, Spatial Clustering, Pandemic, Spatial Autocorrelation, Climatic Variables


The study examined the spatial structure and the association between COVID-19 cases and selected climatic variables. Data on cases, deaths, recovery were obtained from the COVID-19 Resources website of the Environmental Systems Research Institute (ESRI). The climatic variables were selected included Land Surface Temperature (LST) and Water Vapour (WV) and collated from the NASA Earth Observations (NEO). Spatial and inferential statistics were used to examine spatial autocorrelation and associations with these variables. Results show that China, Italy, and Iran have the largest number of confirmed cases, the highest recovery (81%) was recorded in China. Confirmed cases have 7 clusters and 2 outlier locations. There are 21 and 17 spatial outliers for recoveries and deaths respectively. There are 2 natural clusters of the incidences and 98.7% of the locations belong to one of the groups. A weak but statistically significant (P<0.05) associations were observed for the incidence and the climatic variables. The analysis of spatial structure revealed more insight into the distribution of the disease, shedding more light on areas with needs for more investigation (outlier locations) and providing opportunities for mitigating spread and re-emergence.


Available in the full paper.