The Determinants of the Low COVID-19 Transmission and Mortality Rates in Africa: A Cross- Country Analysis

20 Pages Posted: 1 Sep 2021

See all articles by Yagai Bouba

Yagai Bouba

University of Rome Tor Vergata

Emmanuel Kagning Tsinda

Tohoku University

Maxime Descartes Mbogning Fonkou

University Grenoble Alpes

Gideon Sadikiel Mmbando

Tohoku University

Nicola Luigi Bragazzi

York University

Jude Dzevela Kong

Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University

Date Written: July 31, 2021

Abstract

Background: More than one year after the beginning of the international spread of COVID- 19, the reasons explaining its apparently lower reported burden in Africa is still to be fully elucidated. Few studies previously investigated the potential reasons explaining this epidemiological observation using data at the level of a few African countries. However, an updated analysis considering the various epidemiological waves and variables across an array of categories, with a focus on African countries might help to better understand the COVID-19 pandemic on the continent. Thus, we investigated the potential reasons for the persistently lower transmission and mortality rates of COVID-19 in Africa. Methods: Data were collected from publicly available and well-known online sources. Cumulative numbers of COVID-19 cases and deaths per one million population reported by African countries up to February 2021 were used to estimate the transmission and mortality rates of COVID-19, respectively. Covariates were collected across several data sources: clinical/diseases data, health system performance, demographic parameters, economic indicators, climatic, pollution and radiation variables, and use of social media. Collinearities were corrected using variance inflation factor (VIF) and selected variables were fitted to a multiple regression model using the R statistical package. Results: Our model (adjusted R-squared: 0.7) found that the number of COVID-19 tests per one million population, GINI index, global health security (GHS) index and mean body mass index (BMI) were significantly associated (P<0.05) with COVID-19 cases per one million population. No association was found with the median life expectancy, proportion of rural population and BCG coverage rate. On the other hand, diabetes prevalence, number of nurses and GHS index were found to be significantly associated with COVID-19 deaths per one million population (adjusted R-squared of 0.5). Moreover, the median life expectancy and lower respiratory infections rate showed a trend towards significance. No association was found with BCG coverage or communicable disease burden. Conclusions: Low health system capacity, together with some clinical and socio-economic factors were predictors of the reported burden of COVID-19 in Africa. Our results emphasise the need for Africa to strengthen its overall health system capacity to efficiently detect and respond to public health crises.

Note: Funding: This research is funded by Canada's International Development Research Centre 604 (IDRC) (Grant No. 109559-001).

Declaration of Interests: The authors declare that they have no competing interests.

Keywords: COVID-19, transmission, mortality, Africa, cross-country analysis

Suggested Citation

Bouba, Yagai and Kagning Tsinda, Emmanuel and Mbogning Fonkou, Maxime Descartes and Mmbando, Gideon Sadikiel and Bragazzi, Nicola Luigi and Kong, Jude Dzevela, The Determinants of the Low COVID-19 Transmission and Mortality Rates in Africa: A Cross- Country Analysis (July 31, 2021). Available at SSRN: https://ssrn.com/abstract=3897058 or http://dx.doi.org/10.2139/ssrn.3897058

Yagai Bouba

University of Rome Tor Vergata ( email )

Via di Tor Vergata
Rome, Lazio 00133
Italy

Emmanuel Kagning Tsinda

Tohoku University ( email )

SKK Building, Katahira 2
Aoba-ku, Sendai, Miyagi 980-8577
Japan

Maxime Descartes Mbogning Fonkou

University Grenoble Alpes ( email )

151 Rue des Universités
Saint-Martin-d'Hères, 38400
France

Gideon Sadikiel Mmbando

Tohoku University ( email )

SKK Building, Katahira 2
Aoba-ku, Sendai, Miyagi 980-8577
Japan

Nicola Luigi Bragazzi (Contact Author)

York University ( email )

4700 Keele Street
Toronto, Ontario M3J 1P3
Canada

Jude Dzevela Kong

Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University ( email )

4700 Keele St
Toronto, ON M3J 1P3
Canada

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