The basic reproduction number of SARS-CoV-2: a scoping review of available evidence

  1. Ann Barber
  2. John Griffin
  3. Miriam Casey
  4. Áine B. Collins
  5. Elizabeth Ann Lane
  6. Quirine Ten Bosch
  7. Mart De Jong
  8. David Mc Evoy
  9. Andrew W. Byrne
  10. Conor G. McAloon
  11. Francis Butler
  12. Kevin Hunt
  13. Simon J. More

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Abstract

Background

The transmissibility of SARS-CoV-2 determines both the ability of the virus to invade a population and the strength of intervention that would be required to contain or eliminate the spread of infection. The basic reproduction number, R 0 , provides a quantitative measure of the transmission potential of a pathogen.

Objective

Conduct a scoping review of the available literature providing estimates of R 0 for SARS-CoV-2, provide an overview of the drivers of variation in R 0 estimates and the considerations taken in the calculation of the parameter.

Design

Scoping review of available literature between the 01 December 2019 and 07 May 2020.

Data sources

Both peer-reviewed and pre-print articles were searched for on PubMed, Google Scholar, MedRxiv and BioRxiv.

Selection criteria

Studies were selected for review if (i) the estimation of R 0 for SARS-CoV-2 represented either the initial stages of the outbreak or the initial stages of the outbreak prior to the onset of widespread population restriction (“lockdown”), (ii) the exact dates of the study period were provided and (iii) the study provided primary estimates of R 0 .

Results

A total of 20 R 0 for SARS-CoV-2 estimates were extracted from 15 studies. There was substantial variation in the estimates reported. Estimates derived from mathematical models fell within a wider range of 1.94-6.94 than statistical models which fell between the range of 2.2 to 4.4. Several studies made assumptions about the length of the infectious period which ranged from 5.8-20 days and the serial interval which ranged from 4.41-14 days. For a given set of parameters a longer duration of infectiousness or a longer serial interval equates to a higher R 0 . Several studies took measures to minimise bias in early case reporting, to account for the potential occurrence of super-spreading events, and to account for early sub-exponential epidemic growth.

Conclusions

The variation in reported estimates of R 0 reflects the complex nature of the parameter itself, including the context (i.e. social/spatial structure), the methodology used to estimate the parameter, and model assumptions. R 0 is a fundamental parameter in the study of infectious disease dynamics, however it provides limited practical applicability outside of the context in which it was estimated, and should be calculated and interpreted with this in mind.

STRENGTHS AND LIMITATIONS OF THE SCOPING REVIEW

  • This study provides an overview of basic reproduction number estimates for SARS-CoV-2 across a range of settings, a fundamental parameter in gauging the transmissibility of an emerging infectious disease.

  • The key drivers of variation in R 0 estimates and considerations in the calculation of the parameter highlighted across the reviewed studies are discussed.

  • This evidence may be used to help inform modelling studies and intervention strategies.

  • Given the need for rapid dissemination of information on a newly emerging infectious disease, several of the reviewed papers were in the pre-print phase yet to be peer-reviewed.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.07.28.20163535: (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
    Publications listed in the electronic databases PubMed, Google Scholar, MedRxiv and BioRxiv were searched with the following keywords: (“Novel coronavirus” OR “SARS-CoV-2” OR “2019-nCoV” OR “COVID-19”) AND “reproduction number”.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Google Scholar
    suggested: (Google Scholar, RRID:SCR_008878)
    BioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)

    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. ScreenIT

    SciScore for 10.1101/2020.07.28.20163535: (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
    Data sources: Both peer-reviewed and pre-print articles were searched for on PubMed, Google Scholar, MedRxiv and BioRxiv.
    BioRxiv
    suggested: (bioRxiv, SCR_003933)
    Publications listed in the electronic databases PubMed, Google Scholar, MedRxiv and BioRxiv were searched with the following keywords: (“Novel coronavirus” OR “SARS‐CoV‐2” OR “2019-nCoV” OR “COVID-19”) AND “reproduction number”.
    PubMed
    suggested: (PubMed, SCR_004846)
          <div style="margin-bottom:8px">
        <div><b>Google Scholar</b></div>
        <div>suggested: (Google Scholar, <a href="https://scicrunch.org/resources/Any/search?q=SCR_008878">SCR_008878</a>)</div>
      </div>
    </td></tr></table>

    Data from additional tools added to each annotation on a weekly basis.

    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.

  3. Version published to 10.1101/2020.07.28.20163535v1 on medRxiv