Does sub-Saharan Africa truly defy the forecasts of the COVID-19 pandemic? Response from population data

  1. Dongmo Christophe Fokoua-Maxime
  2. Monique Amor-Ndjabo
  3. Amandus Ankobil
  4. Momah Victor-Kiyung
  5. Steve Franck-Metomb
  6. Simeon Pierre Choukem

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Abstract

Introduction

Since its identification, the COVID-19 infection has caused substantial mortality and morbidity worldwide, but sub-Saharan Africa seems to defy the predictions. We aimed to verify this hypothesis using strong statistical methods.

Methods

We conducted a cross-sectional study comparing the projected and actual numbers as well as population proportions of COVID-19 cases in the 46 sub-Saharan African countries on May 1 st , May 29 th (4 weeks later) and June 26 th (8 weeks later). The source of the projected number of cases was a publication by scientists from the Center for Mathematical Modeling of Infectious Diseases of the London School of Hygiene & Tropical Medicine, whereas the actual number of cases was obtained from the WHO situation reports. We calculated the percentage difference between the projected and actual numbers of cases per country. Further, “N-1” chi-square tests with Bonferroni correction were used to compare the projected and actual population proportion of COVID-19 cases, along with the 95% confidence interval of the difference between these population proportions. All statistical tests were 2-sided, with 0.05 used as threshold for statistical significance.

Results

On May 1 st , May 29 th and June 26 th , respectively 40 (86.95%), 45 (97.82%) and 41 (89.13%) of the sub-Saharan African countries reported a number of confirmed cases that was lower than the predicted number of 1000 cases for May 1 st and 10000 for both May 29 th and June 26 th . At these dates, the population proportions of confirmed Covid-19 cases were significantly lower (p-value <0.05) than the projected proportions of cases. Across all these dates, South-Africa always exceeded the predicted number and population proportion of COVID-19 infections.

Conclusion

Sub-Saharan African countries did defy the dire predictions of the COVID-19 burden. Preventive measures should be further enforced to preserve this positive outcome.

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  1. ScreenIT

    SciScore for 10.1101/2020.07.06.20147124: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIRB: Our study was exempt from Institutional Review Board approval since we used data already collected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    These parameters presented several limitations. First, they were estimated in the Chinese population which substantially differs from the sub-Saharan African population in many aspects that can fundamentally impact the spread of the COVID-19 infection. Furthermore, Abbot et al., did acknowledge the scarcity of data at the time they built their model and they further admitted that their results would be significantly impacted by the availability of new data [12]. In the same line, Bi et al., did point out that their study had “numerous limitations”, including the high risk of bias due to the multiplicity of data collection protocols, the fact that it was “impossible to identify every potential contact an individual has”, and the fact that asymptomatic travelers were missed [13]. Finally, for such predictions to come true, the living conditions should remain stable in order to agree with the statistical model parameters. However, the COVID-19 pandemic is an ever-changing affection which prevent predictive models from describing it accurately. It is important to note that the actual figures might be low because all cases might not have been reported. Because of the novelty of the COVID-19 infection, testing kits had to be made from scratch. Countries with technical and infrastructural abilities were the first to dispose of COVID-19 tests. The African continent suffers from a substantial lack of high technical capacities that can produce such quality tests in sufficient amounts; ...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.07.06.20147124v1 on medRxiv