Occurrence and transmission potential of asymptomatic and presymptomatic SARS-CoV-2 infections: A living systematic review and meta-analysis

  1. Diana Buitrago-Garcia
  2. Dianne Egli-Gany
  3. Michel J. Counotte
  4. Stefanie Hossmann
  5. Hira Imeri
  6. Aziz Mert Ipekci
  7. Georgia Salanti
  8. Nicola Low

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Abstract

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  1. ScreenIT

    SciScore for 10.1101/2020.04.25.20079103: (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
    We searched the covid-19 living evidence database [10], which is generated using automated workflow processes [5] to: i) provide daily updates of searches of four electronic databases: Medline Pubmed
    Medline
    suggested: (MEDLINE, RRID:SCR_002185)
    Ovid Embase, bioRxiv and medRxiv, using medical subject headings and free text keywords for SARS-CoV-2 infection and covid-19; ii) de-duplicate the records; iii) tag records that are preprints; and iv) allow searches of titles and abstracts using Boolean operators.
    bioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)
    One reviewer extracted data using a pre-piloted extraction form in REDCap and a second reviewer verified the extracted data using the query system.
    REDCap
    suggested: (REDCap, RRID:SCR_003445)

    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:
    Strengths and weaknesses: A strength of this review is that we used clear definitions and separated review questions to distinguish between SARS-CoV-2 infections that remain asymptomatic throughout their course from those that become symptomatic, and to separate proportions of people with infection from their contribution to transmission in a population. This living systematic review uses methods to minimise bias whilst increasing the speed of the review process [5,6], and will be updated regularly. We only included studies that provided information about follow-up through the course of infection, which allowed reliable assessment about the proportion of asymptomatic people in different settings. In the statistical synthesis of proportions, we used a method that accounts for the binary nature of the data and avoids the normality approximation (weighted logistic regression). Limitation of the review are that most included studies were not designed to estimate the proportion of asymptomatic SARS-CoV-2 infection and definitions of asymptomatic status were often incomplete or absent. The risks of bias, particularly those affecting selection of participants, differed between studies and could result in both underestimation and overestimation of the true proportion of asymptomatic infections. Also, we did not consider the possible impact of false negative RT-PCR results, which might be more likely to occur in asymptomatic infections [116] and would underestimate the proportion of a...

    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.
    • Thank you for including a protocol registration statement.

    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 10.1371/journal.pmed.1003346
  3. ScreenIT

    SciScore for 10.1101/2020.04.25.20079103: (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
    DATA SOURCES PubMed , Embase , bioRxiv and medRxiv using a living evidence database of SARS-CoV-2 literature , searched on 25 March 2020 and updated on 20 April 2020 .
    PubMed
    suggested: (PubMed, SCR_004846)
          <div style="margin-bottom:8px">
        <div><b>Embase</b></div>
        <div>suggested: (EMBASE, <a href="https://scicrunch.org/resources/Any/search?q=SCR_001650">SCR_001650</a>)</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">We searched the covid-19 living evidence database , which includes daily updates of searches of four electronic databases: Medline Pubmed</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>Medline</b></div>
        <div>suggested: (MEDLINE, <a href="https://scicrunch.org/resources/Any/search?q=SCR_002185">SCR_002185</a>)</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Ovid Embase , bioRxiv and medRxiv , using medical subject headings and keywords for SARS-CoV-2 infection and covid-19.9 The data supplement reports the search strings for each database.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>bioRxiv</b></div>
        <div>suggested: (bioRxiv, <a href="https://scicrunch.org/resources/Any/search?q=SCR_003933">SCR_003933</a>)</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">A second reviewer verified the extracted data using the query system in REDCap.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>REDCap</b></div>
        <div>suggested: (REDCap, <a href="https://scicrunch.org/resources/Any/search?q=SCR_003445">SCR_003445</a>)</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">In principle, the proportion of individuals that will develop symptoms can be derived by subtraction from the estimated proportion with true asymptomatic infections; from our review, we would estimate that 85% (95% CI 78 to 90%) of individuals with SARS-CoV-2 will develop symptoms.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>SARS-CoV-2</b></div>
        <div>suggested: (Sino Biological Cat# 40143-R019, <a href="https://scicrunch.org/resources/Any/search?q=AB_2827973">AB_2827973</a>)</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Competing Interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: GS has participated in two scientific meetings for Merck and Biogen.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>Biogen</b></div>
        <div>suggested: (Biogen Idec, <a href="https://scicrunch.org/resources/Any/search?q=SCR_003790">SCR_003790</a>)</div>
      </div>
    </td></tr></table>

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

    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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  4. Version published to 10.1101/2020.04.25.20079103 on medRxiv