COVID-19 pandemic in the African continent: Forecasts of cumulative cases, new infections, and mortality

  1. T Achoki
  2. U Alam
  3. L Were
  4. T Gebremedhin
  5. F Senkubuge
  6. A Lesego
  7. S Liu
  8. R Wamai
  9. Y Kinfu

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Abstract

The epidemiology of COVID-19 remains speculative in Africa. To the best of our knowledge, no study, using robust methodology provides its trajectory for the region or accounts for local context. This paper is the first systematic attempt to provide prevalence, incidence, and mortality estimates across Africa.

Methods

Caseloads and incidence forecasts are from a co-variate-based instrumental variable regression model. Fatality rates from Italy and China were applied to generate mortality estimates after making relevant health system and population-level characteristics related adjustments between each of the African countries.

Results

By June 30 2020, around 16.3 million people in Africa will contract COVID-19 (95% CI 718,403 to 98,358,799). Northern and Eastern Africa will be the most and least affected areas. Cumulative cases by June 30 are expected to reach around 2.9 million (95% CI 465,028 to 18,286,358) in Southern Africa, 2.8 million (95% CI 517,489 to 15,056,314) in Western Africa, and 1.2 million (95% CI 229,111 to 6,138,692) in Central Africa. Incidence for the month of April 2020 is expected to be highest in Djibouti, 32.8 per 1000 (95% CI 6.25 to 171.77), while Morocco will experience among the highest fatalities (1,045 deaths, 95% CI 167 to 6,547).

Conclusion

Less urbanized countries with low levels of socio-economic development (hence least connected to the world), are likely to register lower and slower transmissions at the early stages of an epidemic. However, the same enabling factors that worked for their benefit can hinder interventions that have lessened the impact of COVID-19 elsewhere.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.09.20059154: (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
    SentencesResources
    The relevant data for each country was sourced from IHME. (21-22) Analysis was performed using STATA version 16.0.(33)
    STATA
    suggested: (Stata, RRID:SCR_012763)

    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:
    (21-22) Despite this, we have made efforts to mitigate these limitations by curating data from various credible sources around the world and assessing them for consistency. In developing our model, we have used data sources from 193 countries globally and used statistical relationships to fill data gaps on covariates in the Africa region. Additionally, our model performance was assessed through rigorous out of sample calibration using k-fold cross-validation techniques and obtaining robust results. We have also restricted our forecast to the first few months (April-June) and refrained from a long-term projection exercise. Firstly, this is because we anticipate new and additional data to emerge in the short term that will lead to better and improved estimates. Secondly, where data is sparse, any long-term projection is more likely to be detached from reality and can easily become a wild guess and is therefore less useful for informing policy actions. Thirdly, if the countries in the region will not be able to put effective strategies soon (or in those who have already initiated them render to be ineffective), the extent and consequence of COVID-19 on the continent would be greater than any predictive model could anticipate. Within these caveats, our study substantially contributes to the growing literature on understanding the trajectory of the COVID-19 pandemic. This perspective is particularly important for Africa at a time of controlling a pandemic given that Africa the reg...

    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

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  2. Version published to 10.1101/2020.04.09.20059154v3 on medRxiv
  3. Version published to 10.1101/2020.04.09.20059154v2 on medRxiv
  4. Version published to 10.1101/2020.04.09.20059154v1 on medRxiv