An in-depth statistical analysis of the COVID-19 pandemic’s initial spread in the WHO African region
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Abstract
During the first wave of the COVID-19 pandemic, sub-Saharan African countries experienced comparatively lower rates of SARS-CoV-2 infections and related deaths than in other parts of the world, the reasons for which remain unclear. Yet, there was also considerable variation between countries. Here, we explored potential drivers of this variation among 46 of the 47 WHO African region Member States in a cross-sectional study. We described five indicators of early COVID-19 spread and severity for each country as of 29 November 2020: delay in detection of the first case, length of the early epidemic growth period, cumulative and peak attack rates and crude case fatality ratio (CFR). We tested the influence of 13 pre-pandemic and pandemic response predictor variables on the country-level variation in the spread and severity indicators using multivariate statistics and regression analysis. We found that wealthier African countries, with larger tourism industries and older populations, had higher peak (p<0.001) and cumulative (p<0.001) attack rates, and lower CFRs (p=0.021). More urbanised countries also had higher attack rates (p<0.001 for both indicators). Countries applying more stringent early control policies experienced greater delay in detection of the first case (p<0.001), but the initial propagation of the virus was slower in relatively wealthy, touristic African countries (p=0.023). Careful and early implementation of strict government policies were likely pivotal to delaying the initial phase of the pandemic, but did not have much impact on other indicators of spread and severity. An over-reliance on disruptive containment measures in more resource-limited contexts is neither effective nor sustainable. We thus urge decision-makers to prioritise the reduction of resource-based health disparities, and surveillance and response capacities in particular, to ensure global resilience against future threats to public health and economic stability.
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SciScore for 10.1101/2021.08.21.21262401: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources The Principal Component Analysis (PCA): We performed the PCA on our explanatory variable dataset with two objectives in mind: 1) reducing the number of original variables (from 13) and 2) to discover if the 46 analyzed African countries can be classified according to a composite profile of socioeconomic, geographic, and epidemiological dimensions (Borcard et al., 2011) (see Fig. 2C; correlation matrix plot), scaling variables to unit variance (using function “PCA” from the FactoMineR package; Le et al., 2008). FactoMineRsuggested: (FactoMineR, RRID:SCR_014602)Data were processed using … SciScore for 10.1101/2021.08.21.21262401: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources The Principal Component Analysis (PCA): We performed the PCA on our explanatory variable dataset with two objectives in mind: 1) reducing the number of original variables (from 13) and 2) to discover if the 46 analyzed African countries can be classified according to a composite profile of socioeconomic, geographic, and epidemiological dimensions (Borcard et al., 2011) (see Fig. 2C; correlation matrix plot), scaling variables to unit variance (using function “PCA” from the FactoMineR package; Le et al., 2008). FactoMineRsuggested: (FactoMineR, RRID:SCR_014602)Data were processed using both Python (version 3.7.10) and R (version 4.0.2) (https://www.r-project.org/). Pythonsuggested: (IPython, RRID:SCR_001658)https://www.r-project.org/suggested: (R Project for Statistical Computing, RRID:SCR_001905)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Strengths and limitations: Our study is one of the most comprehensive studies describing the first wave of COVID-19 pandemic in Africa, where it is less studied. Using the 5 response variables and 13 predictors we analyzed, we believe that we managed to get a thorough understanding of the burden, severity, spatial trends, and the evolution of the pandemic in the WHO African region. The other strength of our study is the robustness of our estimates. The analysis conducted with countries having at least 10 deaths confirmed the roles of tourism, age distribution, and GDP on the attack rates and CFR. It also established that nations with strict governmental policies had a late onset of the COVID-19 outbreak and those having a high attack rate in neighbouring countries managed to reduce the speed at which the first 50 infections occur in them. However, there exists heterogeneity across the countries in this continent, which limits our ability to extract general conclusions. One important limitation of this study is the lack of standardization in testing policies (Velasquez et al., 2021) and notification of cases between countries. Additionally, the availability of individual patient data corresponding to each predictor variable, where possible, is useful in obtaining a clearer insight of the pandemic situation. However, such studies are limited in the context of Africa and have addressed the impacts of a few individual-level factors only (Dalal et al., 2021). Similar to other regi...
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Results from scite Reference Check: We found no unreliable references.
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