The ongoing highly contagious coronavirus disease 2019 (COVID-19) pandemic, which started in Wuhan, China, in December 2019, has now become a global public health problem. Using publicly available data from the COVID-19 data repository of Our World in Data, we aimed to investigate the influences of spatial socio-economic vulnerabilities and neighbourliness on the COVID-19 burden in African countries. We analyzed the first wave (January–September 2020) and second wave (October 2020 to May 2021) of the COVID-19 pandemic using spatial statistics regression models. As of 31 May 2021, there was a total of 4,748,948 confirmed COVID-19 cases, with an average, median, and range per country of 101,041, 26,963, and 2191 to 1,665,617, respectively. We found that COVID-19 prevalence in an Africa country was highly dependent on those of neighbouring Africa countries as well as its economic wealth, transparency, and proportion of the population aged 65 or older ( p -value < 0.05). Our finding regarding the high COVID-19 burden in countries with better transparency and higher economic wealth is surprising and counterintuitive. We believe this is a reflection on the differences in COVID-19 testing capacity, which is mostly higher in more developed countries, or data modification by less transparent governments. Country-wide integrated COVID suppression strategies such as limiting human mobility from more urbanized to less urbanized countries, as well as an understanding of a county’s social-economic characteristics, could prepare a country to promptly and effectively respond to future outbreaks of highly contagious viral infections such as COVID-19.
【저자키워드】 sub-Saharan Africa, Coronavirus (COVID-19) pandemic, country-level disparities, spatial regression analysis, 【초록키워드】 COVID-19, coronavirus disease, viral infection, pandemic, COVID-19 pandemic, COVID, Prevalence, COVID-19 testing, Characteristics, outbreak, African, second wave, First wave, COVID-19 cases, Older, Repository, available data, global public health, average, P -value, Modification, contagious, regression models, country, Wuhan, China, analyzed, proportion, median, less, dependent on, respond, influence, 【제목키워드】 COVID-19, African, vulnerability, Effect, Evaluating,