Parasites and their protection against COVID-19- Ecology or Immunology?

  1. Kenneth Ssebambulidde
  2. Ivan Segawa
  3. Kelvin M Abuga
  4. Vivian Nakate
  5. Anthony Kayiira
  6. Jayne Ellis
  7. Lillian Tugume
  8. Agnes N Kiragga
  9. David B Meya

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Abstract

Background: Despite the high infectivity of SARS-CoV-2, the incidence of COVID-19 in Africa has been slower than predicted. We aimed to investigate a possible association between parasitic infections and COVID-19. Methods: An ecological study in which we analysed WHO data on COVID-19 cases in comparison to WHO data on helminths and malaria cases using correlation, regression, and Geographical Information Services analyses. Results: Of the global 3.34 million COVID-19 cases and 238,628 deaths as at May 4th 2020, Africa reported 0.029/3.3 million (0.88%) cases and 1,064/238,628 (0.45%) deaths. In 2018, Africa reported 213/229 million (93%) of all malaria cases, 204/229 million (89%) of schistosomiasis cases, and 271/1068 million (25%) of soil-transmitted helminth cases globally. In contrast, Europe reported 1.5/3.3 million (45%) of global COVID-19 cases and 142,667/238,628 (59%) deaths. Europe had 5.8/1068 million (0.55%) soil-transmitted helminths cases and no malaria/schistosomiasis cases in 2018. We found an inverse correlation between the incidence of COVID-19 and malaria (r -0.17, p =0.002) and COVID-19 and soil-transmitted helminths (r -0.25, p <0.001). Malaria-endemic countries were less likely to have COVID-19 (OR 0.51, 95% CI 0.29-0.90; p =0.02). Similarly, countries endemic for soil-transmitted helminths were less likely to have COVID-19 (OR 0.24, 95% CI 0.13-0.44; p <0.001), as were countries endemic for schistosomiasis (OR 0.22, 95% CI 0.11-0.45; p<0.001). Conclusions: One plausible hypothesis for the comparatively low COVID-19 cases/deaths in parasite-endemic areas is immunomodulation induced by parasites. Studies to elucidate the relationship between parasitic infections and susceptibility to COVID-19 at an individual level are warranted.  

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  1. ScreenIT

    SciScore for 10.1101/2020.05.11.20098053: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Study procedures: Two investigators (KS and IS) captured data using an electronic case report form and entered it into Microsoft Excel 2013 (Microsoft Corporation, WA, USA).
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    Statistical analysis: Statistical analyses were conducted using STATA 15.1 (StataCorp, College Station, TX, USA).
    STATA
    suggested: (Stata, RRID:SCR_012763)
    StataCorp
    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: 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.

    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 10.1101/2020.05.11.20098053v1 on medRxiv