Spatial clustering of COVID-19 cases and mortality at the global scale

Understanding the spatial distribution of COVID-19 cases and mortality is important for identifying high-risk areas and strengthening global public health preparedness. Although many COVID-19 spatial studies have been conducted at national or regional scales, fewer have applied a consistent global spatial framework to compare the clustering of both reported cases and deaths. This study analysed global spatial clustering of COVID-19 cases and deaths using country- and territory-level data from the WHO COVID-19 Global Dataset. The analysis used WHO-reported cumulative cases and deaths per 100,000 population, specifically the fields “Cases-cumulative total per 100000 population” and “Deaths - cumulative total per 100000 population.” Records with missing standardized values, unmatched geographic identifiers, and non-country summary records were removed before spatial analysis. Global Moran’s I was used to assess overall spatial autocorrelation, while Getis-Ord Gi* hotspot analysis was applied to identify statistically significant hotspots and coldspots at 90%, 95%, and 99% confidence levels. Results showed significant positive spatial autocorrelation for both COVID-19 cases (Moran’s I = 0.193, z = 11.57, p < 0.001) and deaths (Moran’s I = 0.300, z = 17.95, p < 0.001), indicating non-random global clustering. COVID-19 case hotspots were mainly concentrated in Europe, while case coldspots were largely found in Sub-Saharan Africa, South Asia, the Middle East, and parts of East and Central Asia. Mortality hotspots were broader, extending across Europe and much of South America, whereas mortality coldspots were concentrated in Sub-Saharan Africa, the Middle East, South Asia, China, Southeast Asia, and parts of Oceania. The stronger clustering of deaths suggests that COVID-19 mortality was more geographically concentrated than reported infections, which may reflect differences in demographic vulnerability, healthcare capacity, reporting systems, and public health responses; however, these factors were not directly evaluated in this study. These findings demonstrate the value of combining global and local spatial statistics to support targeted surveillance, resource allocation, and preparedness planning for future infectious disease outbreaks.