The spread and burden of the COVID-19 pandemic in sub-Saharan Africa: comparison between predictions and actual data and lessons learned

  1. Christophe Dongmo Fokoua-Maxime
  2. Yahia Bellouche
  3. Dillonne Ngonpong Tchigui-Ariolle
  4. Tchana Loic Tchato-Yann
  5. Simeon Pierre Choukem

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Abstract

Introduction

Sub-Saharan Africa (SSA) was predicted to be severely affected by the coronavirus disease 2019 (COVID-19) pandemic, but the actual data seem to have contradicted these forecasts. This study attempted to verify this observation by comparing predictions against actual data on the spread and burden of the COVID-19 pandemic in SSA.

Methods

Focused on the period from March 1 st to September 30 th , 2020, we compared (1) the predicted interval dates when each SSA country would report 1 000 and 10 000 COVID-19 cases, to the actual dates when these numbers were attained, as well as (2) the daily number of predicted versus actual COVID-19 cases.

Further, we calculated the case fatality ratio of the COVID-19 infection in SSA, and the correlation coefficient between the weekly average number of confirmed COVID-19 cases reported by each country and the weekly average stringency index of its anti-COVID-19 policy measures.

Results

84.61% (33) and 100% (39) of the 39 SSA countries for which predictions were made did not reach a total of 1 000 and 10 000 confirmed COVID-19 cases at the predicted interval dates. The daily number of confirmed COVID-19 cases was lower than the one projected for all SSA countries. The case fatality ratio of the COVID-19 infection in SSA was 3.42%. Among the 44 SSA countries for which the correlation could be estimated, it was negative for 17 (38.6 %) of them.

Conclusions

The natural characteristics of SSA and the public health measures implemented might partly explain that the actual data were lower than the predictions on the COVID-19 pandemic in SSA, but the low case ascertainment and the numerous asymptomatic cases did significantly influence this observation.

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  1. ScreenIT

    SciScore for 10.1101/2022.05.04.22274692: (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

    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 results might be explained by the limitations of the statistical models which yielded these predictions. Additionally, specific local population and environmental characteristics as well as the low case ascertainment might have had a mitigating effect. The prediction model of the MRC Centre for Global Infectious Disease Analysis at Imperial College London was built on estimates of severity obtained from data from China and Europe, and model parameters obtained from data from China and the United Kingdom (11). On the other hand, Pearson et al. considered that the reproductive number R (which is the number of ancillary cases that one case would generate if in contact with a completely susceptible population (18)) would be 2, that the dispersion estimate k (which is the variance of R over the mean of R and quantifies whether a set of observed cases are clustered or dispersed when compared to cases following a standard negative binomial distribution (19)) would be 0.58, and that the serial interval (which is the time that elapses between two consecutive cases of an infectious disease (20)) would be normally distributed with a mean of 4.7 ± 2.9 days (1). These model parameters all originated from populations which substantially differ from SSA populations in terms of composition, density, living customs, and health status, all of which impact the dynamic of the COVID-19 pandemic. Furthermore, Abbot et al. acknowledged that data were scarce at the time they estimated R and k,...

    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.

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

    Results from scite Reference Check: We found no unreliable references.

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  2. Version published to 10.1101/2022.05.04.22274692v1 on medRxiv