Recurrent SARS-CoV-2 RNA positivity after COVID-19: a systematic review and meta-analysis

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Abstract

Present study aimed to estimate the incidence of recurrent SARS-CoV-2 RNA positivity after recovery from COVID-19 and to determine the factors associated with recurrent positivity. We searched the PubMed, MedRxiv, BioRxiv, the Cochrane Library, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry for studies published to June 12, 2020. Studies were reviewed to determine the risk of bias. A random-effects model was used to pool results. Heterogeneity was assessed using I 2 . Fourteen studies of 2568 individuals were included. The incidence of recurrent SARS-CoV-2 positivity was 14.8% (95% confidence interval [CI] 11.44–18.19%). The pooled estimate of the interval from disease onset to recurrence was 35.4 days (95% CI 32.65–38.24 days), and from the last negative to the recurrent positive result was 9.8 days (95% CI 7.31–12.22 days). Patients with younger age and a longer initial illness were more likely to experience recurrent SARS-CoV-2 positivity, while patients with diabetes, severe disease, and a low lymphocyte count were less likely to experience. Present study concluded that the incidence of recurrent SARS-CoV-2 positivity was 14.8% suggesting further studies must be conducted to elucidate the possibility of infectious individuals with prolonged or recurrent RNA positivity.

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  1. SciScore for 10.1101/2020.07.19.20157453: (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
    7] The protocol of this review was published in the International Prospective Register of Systematic Reviews (PROSPERO) on May 14, 2020, reference no. CRD42020186306.[8] Search strategy and information resources: A search was conducted on PubMed, MedRxiv, BioRxiv, Cochrane Library,
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    BioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)
    Cochrane Library
    suggested: (Cochrane Library, RRID:SCR_013000)
    ClinicalTrials.gov, the WHO international register of clinical trials registry using the search term in Medical Subjects Headings (MeSH) and free text: (“2019 nCoV” OR “2019nCoV” OR “2019 novel coronavirus” OR “COVID 19” OR “COVID19” OR “new coronavirus” OR “novel coronavirus” OR “SARS CoV-2” OR (Wuhan AND coronavirus) OR “COVID 19” OR “SARS-CoV” OR “2019-nCoV” OR “SARS-CoV-2”)
    MeSH
    suggested: (MeSH, RRID:SCR_004750)
    Data management and study selection: Literature search results were organized using Mendeley (
    Mendeley
    suggested: (Mendeley Data, RRID:SCR_002750)
    The incidence for each individual study with its standard error (SE) adds to the study data in RevMan.
    RevMan
    suggested: (RevMan, RRID:SCR_003581)

    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.