COVID-19 in Africa project
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This file contains the aggregated dataset that was the basis of this paper ' Reasons for Low Burden of COVID-19 in Africa: An Explorative Cross-Sectional Analysis of Twenty-One African Countries from January to June 2020.'
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METHODS Country Selection Twenty-one African countries were systemically selected using multiple parameters (stated below) to get appropriate representation across several countries. Countries were initially grouped according to the African Union recognized geopolitical regions; North, Central, West, East and South Africa. The countries in these regions were ranked according to performance using socioeconomic class, level of medical care, literacy level and COVID-19 testing capabilities. Indicators used for these categories were Gross Domestic Product Per Capita, Physicians/1000 population, youth literacy rate and total tests/million population, respectively. In instances of a tie between countries, we selected the country with the highest 2020 world openness score/passport index rank, an index of global mobility (see data source in supplementary data). Two cohorts of top and worst performers were formed, and the two most frequently occurring countries across all criteria were included in the study (Table 1). Nigeria was included in the study due to its significant influence on other African countries owning to its population, trade, and economic roles, which play essential roles in the transmission and spread of the pandemic. Data Collection and analysis The response assessed in this study were analysed in three large groups: International travel restrictions, physical and social distancing, and movement restrictions (lockdown measures; curfews, partial or/and National lockdowns). Country specific preventive measure data and COVID-19 statistics were gathered from several official sources, including official government statements, regular gazettes, published guidelines, W.H.O, Africa C.D.C. guidelines, and COVID 19 tracking websites (see the supplementary file with data sources). We also gathered data and metrics on health and socioeconomic parameters such as youth/adult literacy rate, age dependency ratio, hospital beds/10,000 population, life expectancy, G.D.P., health care security index, median age (2020), life expectancy (2015-2020), hypertension death per 100,000, coronary artery disease death per 100,000, human development index, diabetes mellitus death per 100,000, human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) death per 100,000, kidney disease death per 100,000 and tuberculosis death per 100,000 (see supplemental table for the data sources used in this study). Data collection, cleaning, analysis (including Pearson correlation), and visualization were done in Microsoft Excel and Graph Pad Prism version 9.