COVID-19: Extracting the Pattern of Morbidity and Mortality Among Countries in the African Region

As the coronavirus disease 2019 (COVID-19) spreads worldwide, there were fears that the African continent would be torn apart by the pandemic. For instance, the World Health Organization reportedly warned that African countries should “prepare for the worst.” In this study, non-parametric data analytics and data mining techniques were deployed on five COVID-19 datasets from the African region to extract knowledge on how the pandemic has affected the continent. Results from non-parametric tests, including the Friedman, Kendall’s W, and Wilcoxon rank sum tests, showed that the distribution of morbidity and mortality figures across African countries and territories are statistically different. The agglomerative hierarchical clustering was deployed to extract four clusters of countries based on the similarity in the number of confirmed cases and deaths from the coronavirus infection. The clusters include the least-hit, moderately hit, badly hit, and worst-hit countries. These findings have established that though most African countries are underdeveloped, the effect of the pandemic is not uniform across the continent. It is therefore suggested that the better positioned countries should extend a hand of fellowship to the countries in need so that together, the African continent would be rid of the pandemic. This would pave the way for a uniform, post-pandemic, socioeconomic development across the continent.