Article activity feed

  1. Our take

    This systematic review, available as a preprint and thus not yet peer reviewed, leveraged individual level data from 43 studies including 932 unique individuals with 5328 observations to analyze viral load distribution, and 66 studies including 1198 individuals and 7240 observations to analyze viral shedding. This study observed that SARS-CoV-2 viral load peaked prior to symptom onset and remained elevated for up to three weeks, while MERS-CoV and SARS-CoV viral loads peaked after symptom onset; and that severe patients had significantly higher viral loads than mild patients. SARS-CoV-2, MERS-CoV, and SARS-CoV had median viral shedding durations in respiratory samples of 4.8, 4.2, and 1.2 days after symptom onset; and in gastrointestinal samples, SARS-CoV-2 and MERS-CoV had median viral shedding of 4.9 days, and 1.9 days, respectively. This study supports previous evidence from smaller sampled studies that viral load peak is prior to symptom onset for SARS-COV-2, and the transmission potential from both respiratory and gastrointestinal samples.

    Study design

    other

    Study population and setting

    This study systematically reviewed, collated, and analyzed unique individual patient data (IPD) from published studies which described temporal viral load and shedding data from coronaviruses, including SARS-CoV, SARS-CoV-2 and MERS. Following PRISMA-IPD guidelines for systematic reviews of individual IPD, this study searched for papers on PubMed through June 8, 2020. In total, data from 43 studies including 932 unique individuals with 5,328 observations were utilized for the analysis of viral load distribution, and 66 studies including 1,198 individuals and 7,240 observations were used for the viral shedding analysis. Viral load trajectories were generated using a series of generalized additive models and the duration of viral shedding was analyzed by fitting log-normal models to the data accounting for interval censoring.

    Summary of main findings

    This study observed that SARS-CoV-2 viral load peaked prior to symptom onset and remained elevated for up to three weeks, while MERS-CoV and SARS-CoV viral loads peaked after symptom onset. Severe patients had significantly higher viral loads than mild patients (9.9% higher after converting from log10 copies to raw copy number). SARS-CoV-2, MERS-CoV, and SARS-CoV had median viral shedding durations in respiratory samples of 4.8, 4.2, and 1.2 days after symptom onset. In gastrointestinal samples, SARS-CoV-2 and MERS-CoV had median viral shedding of 4.9 days, and 1.9 days, respectively. No differences in duration of SARS-CoV-2 viral shedding were seen by sex or with age.

    Study strengths

    Combing data from across studies allows for a large sample of individual-level data to support statistical modeling to quantify temporal distributions with adjustment for factors such as clinical and demographic characteristics. Comparison of SARS-CoV-2 with other coronaviruses allowed for better understanding of why the transmission dynamics of SARS-CoV-2 may differ from other coronaviruses.

    Limitations

    Testing frequency was not consistent across specific study samples or disease severity, which may introduce selection bias. The pooled data did not have did not have complete data for age, sex, severity, and hospitalization status for all individuals. Copy number values were not reported consistently, as 65% of the studies included reported only cycle threshold (Ct) values from the qPCR samples instead of copy number, and therefore Ct values were converted to log10 copy number using an average standard curve which may have introduced error. There was limited uniformity in time measurement for the timing of symptom onset. Finally not all eligible studies contributed data leaving the possibility of selection bias.

    Value added

    This study leveraged pooled individual-level to assess temporal viral load dynamics and shedding duration across studies for SARS-CoV, MERS-COV, and SARS-CoV-2.

    Read the original source
    Was this evaluation helpful?
  2. SciScore for 10.1101/2020.09.28.20202028: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomization26, 27] Data Extraction: Data extraction was performed by three authors (AEB, AC, and RAA), and three data points from each paper were randomly checked for data entry or interpretation errors by one author (LAS).
    BlindingStudy Selection: Each title was blindly screened by three reviewers.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Search Strategy: On June 8th, 2020, the investigators performed a PubMed search with the phrase, “((corona*) OR (COVID*) OR (”SARS-CoV-2”) OR (SARS*) OR (MERS*)) AND ((”viral load”) OR (”viral shedding”) OR (serolog*)).
    PubMed
    suggested: (PubMed, RRID:SCR_004846)

    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:
    This, like every study, is not without limitations. First, 65% of the studies included reported only Ct values from the qPCR samples. Copy number (32% of our data) is a standardized measure of viral load across studies whereas Ct values are not. To compare across papers, we had to convert Ct values to log10 copy number using an average standard curve. This introduced artificial variation in our data, which we controlled for by treating study ID as a random effect. We also ran a sensitivity analysis comparing the published, standardized copy numbers to our copy-number converted Ct values (Supplementary Appendix C). Though we observed no major qualitative differences between the raw and converted data (especially within diseases), the value of the peak viral loads (in log10 copies) that we report here should not be taken out of context. Having used standard methods of conversion, this leads to a limitation of the field writ large. Second, limited numbers of MERS fecal shedding and none for SARS limits the ability to draw firm conclusions about the transmission routes of these pathogens. However these estimates do suggest lines of enquiry for future research. Third, due to incomplete recording of data, we had to impute missing values to increase statistical power. This did not qualitatively change the viral load trajectories (Supplementary Appendix D and E) and is common practice when dealing with diverse observational data. Fourth, due to the novelty of each coronavirus when it...

    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.

    Read the original source
    Was this evaluation helpful?