Tamasiga Phemelo, Guta Ashenafi Teshome, Onyeaka Helen, Kalane Maureen Sindisiwe
Duisburg, Germany.
Department of Economics, Addis Ababa University, Addis Ababa, Ethiopia.
J Soc Econ Dev. 2022;24(2):493-510. doi: 10.1007/s40847-022-00184-2. Epub 2022 May 19.
The COVID-19 pandemic has triggered an unprecedented social and economic crisis. This study aims at investigating the impact of socio-economic indicators on the levels of COVID-19 (confirmed and death cases) in sub-Saharan Africa. The investigation makes use of the readily accessible public data: we obtain COVID-19 data from Johns Hopkins and socio-economic indicators from the World Bank. The socio-economic indicators (independent variables) used in the multilinear regression were GDP per capita, gross national income per capita, life expectancy, population density (people per sq. km of land area), the population aged 65 and above, current health expenditure per capita and total population. The dependent variables used were the COVID-19 confirmed and death cases. Amongst the seven socio-economic indicators, only 4 showed a statistically significant impact on COVID-19 cases: population density, gross national income per capita, population aged 65 and above and total population. The obtained of 69% and 63% indicated that the socio-economic indicators captured and explained the variation of COVID-19 confirmed cases and COVID-19 death cases, respectively. The startling results obtained in this study were the negative but statistically significant relationship between COVID-19 deaths and population density and the positive and statistically significant relationship between gross national income per capita and COVID-19 cases (both confirmed and deaths). Both these results are at odds with literature investigating these indicators in Europe, China, India and the UK.
新冠疫情引发了一场前所未有的社会和经济危机。本研究旨在调查社会经济指标对撒哈拉以南非洲地区新冠疫情（确诊病例和死亡病例）水平的影响。该调查利用了易于获取的公开数据：我们从约翰·霍普金斯大学获取新冠疫情数据，从世界银行获取社会经济指标。多元线性回归中使用的社会经济指标（自变量）包括人均国内生产总值、人均国民总收入、预期寿命、人口密度（每平方公里土地面积的人口数）、65岁及以上人口、人均当前卫生支出和总人口。使用的因变量是新冠确诊病例和死亡病例。在这七个社会经济指标中，只有四个对新冠病例有统计学上的显著影响：人口密度、人均国民总收入、65岁及以上人口和总人口。所得到的69%和63%的[具体数值未明确，暂保留原文表述]表明，社会经济指标分别捕捉并解释了新冠确诊病例和新冠死亡病例的变化情况。本研究得出的惊人结果是，新冠死亡病例与人口密度之间存在负相关但具有统计学显著性的关系，以及人均国民总收入与新冠病例（确诊病例和死亡病例）之间存在正相关且具有统计学显著性的关系。这两个结果均与在欧洲、中国、印度和英国对这些指标进行研究的文献结果不一致。
