Why is the number of COVID-19 cases lower than expected in Sub-Saharan Africa? A cross-sectional analysis of the role of demographic and geographic factors

Abstract

Unlike initially predicted by WHO, the severity of the novel coronavirus pandemic has remained relatively low in Sub-Saharan Africa, more than two months after the first confirmed cases were identified. In this paper, we analyze the extent to which demographic and geographic factors associated to the disease explain this phenomenon. We use publicly available data from a cross-section of 182 countries worldwide, and we employ a regression analysis that accounts for possible misreporting of COVID-19 cases, as well as a Ramsey-type specification that preserves degree of freedom. We found that proportion of population aged 65+, population density, and urbanization are significantly positively associated with high numbers of active infected cases, while mean temperature around the first quarter (January-March) is negatively associated to this COVID-19 outcome. These factors are those for which Africa has a comparative advantage. In contrast, factors for which Africa has a relative disadvantage, such as income and quality of health care infrastructure, are found to be insignificant predictors of the spread of the pandemic. These results hold even when accounting for possible underreporting, as well as differences in the duration of the epidemic in each country, as measured by the time elapsed since the first confirmed case occurred. We conclude that differences in demographic and geographic characteristics help understand the relatively low progression of the pandemic in sub-Saharan Africa as well as the gap in the number of active cases between this region and the rest of the World. We also found, however, that this gap is insignificant beyond these factors, and is expected to narrow over time as the pandemic evolves. These results provide insights for relevant urban policies and kinds of development planning to consider in the fight against disease spreads of the coronavirus type.

Acceptability of Government Measures Against Covid-19 Pandemic in Senegal: a Mixed Methods Study

“…This perception of their vulnerability acquired in a few months is remarkable and can be a positive element in terms of communication about the pandemic. As in the case of the analyses concerning the spread of HIV on the continent [46], it could be hypothesised that, although susceptibility to the COVID-19 pandemic appears to be lower than elsewhere in the world [58]for reasons which remain to be explained, the vulnerability of the elderly is obvious and perceived as such by those concerned.…”

The SARS-CoV-2 pandemic: A syndemic perspective

“…Actually, the low intensity and lethality of the national epidemics in most African countries suggest hypothetical protective interactions of the high burden of tuberculosis (and/or BCG coverage) and tropical parasitic diseases, along with the lack of health-care infrastructure capable of clinically detecting and confirming COVID-19 cases, the implementation of social distancing and hygiene, international air traffic flows, the climate, the relatively young and rural population, the genetic polymorphism of the angiotensin-converting enzyme 2 receptor, cross-immunity and the use of antimalarial drugs [ [70] , [71] , [72] , [73] , [74] , [75] , [76] , [77] , [78] , [79] , [80] ]. However, the detection of a new variant of the SARS-CoV-2 in South Africa (variant 501Y·V2) in middle December 2020 with preliminary studies suggesting that the variant is associated with a higher viral load, which may suggest potential for increased transmissibility, might challenge the low transmissibility, low lethality trend observed so far in most African Countries [ 81 ].…”

COVID-19 modelling in the Caribbean: Spatial and statistical assessments

“…The study adopted a cross sectional analytical technique as the models attempted to demonstrate the current circumstances in each Caribbean country and the connection between the current accumulation of COVID-19 cases and deaths and the exogenous variables. Cross-sectional analysis to model the influence of social factors on COVID-19 cases and deaths is well documented in the literature ( Murphy et al, 2020 ; Nguimkeu and Tadadjeu, 2021 ; Saqlain et al, 2020 ).…”

“…We adopted a linear approach assuming strict exogeneity amongst the explanatory variables. A linear approach was outlined by several studies, as the literature shows the variations in COVID-19 cases deaths and cases fits a linear structural model best when regressed against various exogenous variables ( Adekunle et al, 2020 ; Nguimkeu and Tadadjeu, 2021 ; Ogundokun et al, 2020 ; Rath et al, 2020 ; Sardokie and Owusu, 2020 ). The functional relationship is expressed as: where Y iN are the dependent variables related to aggregated COVID-19 cases and deaths for the individual countries (i) for the total number of countries observed in the dataset (N) and X 1 ,X 2 ,.....,X iN are independent exogenous variables for the ith country in the total number of countries (N).…”