Estimating infectiousness throughout SARS-CoV-2 infection course

  1. Terry C. Jones
  2. Guido Biele
  3. Barbara Mühlemann
  4. Talitha Veith
  5. Julia Schneider
  6. Jörn Beheim-Schwarzbach
  7. Tobias Bleicker
  8. Julia Tesch
  9. Marie Luisa Schmidt
  10. Leif Erik Sander
  11. Florian Kurth
  12. Peter Menzel
  13. Rolf Schwarzer
  14. Marta Zuchowski
  15. Jörg Hofmann
  16. Andi Krumbholz
  17. Angela Stein
  18. Anke Edelmann
  19. Victor Max Corman
  20. Christian Drosten

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Abstract

The role that individuals with asymptomatic or mildly symptomatic severe acute respiratory syndrome coronavirus 2 have in transmission of the virus is not well understood. Jones et al. investigated viral load in patients, comparing those showing few, if any, symptoms with hospitalized cases. Approximately 400,000 individuals, mostly from Berlin, were tested from February 2020 to March 2021 and about 6% tested positive. Of the 25,381 positive subjects, about 8% showed very high viral loads. People became infectious within 2 days of infection, and in hospitalized individuals, about 4 days elapsed from the start of virus shedding to the time of peak viral load, which occurred 1 to 3 days before the onset of symptoms. Overall, viral load was highly variable, but was about 10-fold higher in persons infected with the B.1.1.7 variant. Children had slightly lower viral loads than adults, although this difference may not be clinically significant.

Science , abi5273, this issue p. eabi5273

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  1. Version published to 10.1126/science.abi5273
  2. ScreenIT

    SciScore for 10.1101/2020.06.08.20125484: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    RandomizationSample collection and processing dates are similarly randomly adjusted.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All randomization was performed using the Mersenne Twister algorithm (with period 219937-1), as implemented in the random module of Python (version 3.8.2).
    Python
    suggested: (IPython, RRID:SCR_001658)
    The following Python (version 3.8) software packages were used in the analysis and production of images: Scipy (version 1.4.1) (26), pandas (version 1.0.3) (27), statsmodels (version 0.11.1) (28), matplotlib (version 3.2.1) (29), numpy (1.18.3) (30), seaborn (version 0.10.1) (31), and scikit_posthocs (version 0.6.4) (32).
    Scipy
    suggested: (SciPy, RRID:SCR_008058)
    matplotlib
    suggested: (MatPlotLib, RRID:SCR_008624)

    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:
    Irrespective of this limitation, the important resemblance of shedding kinetics between SARS-CoV-2 and influenza allows comparisons that can at least provisionally inform the evolving working hypotheses in public health practice. For instance, in a study based on household contact testing for influenza (20), a difference in viral load of ca. 0.7 log10 on the day of symptom onset was associated with an increase of infectivity of 22% (i.e., 22% more secondary infections from an index case). An additional increase of 0.88 log10 in viral load caused infectivity to increase by another 22%. One might consider comparing these values to the difference we found between children aged 0-9 years and the older people in the cobas dataset (ca. −0.6 to −0.8 log10), and translating to correspondingly less infectivity based on the influenza findings. But, as explained, the LC480 dataset better represents children sampled in community/cluster testing, and these children would represent those attending kindergartens and schools. No statistically significant differences between children and adults were detected in the analyses of the LC480 dataset and the Bayesian analysis of the same dataset showed smaller differences between all groups examined than in the cobas sample. We propose that it would be incautious not to place more weight on the information from the LC480 data, and therefore assume that children of both age tiers (0-9 and 0-19 years) have virtually the same average viral loads as ad...

    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.

  3. ScreenIT

    SciScore for 10.1101/2020.06.08.20125484: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.RandomizationSample collection and processing dates are similarly randomly adjusted.Blindingnot detected.Power Analysisnot detected.Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All randomization was performed using the Mersenne Twister algorithm ( with period 2​19937​-1) , as implemented in the ​random​ module of Python ( version 3.8.2) .
    Python
    suggested: (IPython, SCR_001658)
    The following Python ( version 3.8 ) software packages were used in the analysis and production of images: Scipy ( version 1.4.1 ) ​ ( ​26)​ ​ , pandas ( version 1.0.3 ) ​ ( ​27​)​ , statsmodels ( version 0.11.1 ) ( ​28)​ ​ , matplotlib
    Scipy
    suggested: (SciPy, SCR_008058)

    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 10.1101/2020.06.08.20125484 on medRxiv