Sub-Saharan African Countries’ COVID-19 Research: An analysis of the External and Internal Contributions, Collaboration Patterns and Funding Sources

  1. Toluwase Victor Asubiaro
  2. Hafsah Shaik

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Abstract

This study aims at providing some evidence-based insight into Sub-Saharan Africa’s first eighteen months of COVID-19 research by evaluating its research contributions, patterns of collaboration, and funding sources. Eighteen months (2020 January 1-2021 June 30) COVID-19 publication data of 46 Sub-Saharan African countries was collected from Scopus for analysis. Country of affiliation of the authors and funding agencies data was analyzed to understand country contributions, collaboration pattern and funding sources. USA (23.08%) and the UK (19.63%), the top two external contributors, collaborated with Sub-Saharan African countries about three times more than other countries. Collaborative papers between Sub-Saharan African countries - without contributions from outside the region-made up less than five percent of the sample, whereas over 50% of the papers were written in collaboration with researchers from outside the region. Organizations that are in USA and the UK funded 45% of all the COVID-19 research from Sub-Saharan Africa. 53.44% of all the funding from Sub-Saharan African countries came from South African organizations. This study provides evidence that pan-African COVID-19 research collaboration is low, perhaps due to poor funding and lack of institutional support within Sub-Saharan Africa. This mirrors the collaborative features of science in Sub-Saharan Africa before the COVID-19 pandemic. The high volume of international collaboration during the pandemic is a good development. There is also a strong need to forge more robust pan-African research collaboration networks, through funding from Africa’s national and regional government organizations, with the specific objective of meeting local COVID-19 and other healthcare needs.

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  1. ScreenIT

    SciScore for 10.1101/2021.09.27.21264044: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot 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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

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

    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 10.1101/2021.09.27.21264044v1 on medRxiv
  3. Version published to 10.1515/opis-2020-0125