Automated and partly automated contact tracing: a systematic review to inform the control of COVID-19

  1. Isobel Braithwaite
  2. Thomas Callender
  3. Miriam Bullock
  4. Robert W Aldridge

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

No abstract available

Article activity feed

  1. Version published to 10.1016/s2589-7500(20)30184-9
  2. ScreenIT

    SciScore for 10.1101/2020.05.27.20114447: (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
    20 Search strategy and selection criteria: We searched PubMed, EMBASE, and OVID Global Health for articles from any setting published between 1 January 2000 to 14 April 2020.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    EMBASE
    suggested: (EMBASE, RRID:SCR_001650)
    bioRxiv arXiv, EBSCO Medical COVID Information portal, Cochrane Library and Google Advanced (see Supplementary Information for search terms), and scanned relevant references of included studies.
    Cochrane Library
    suggested: (Cochrane Library, RRID:SCR_013000)

    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:
    Other limitations include the lack of eligible empirical studies of fully-automated contact tracing and a paucity of evidence related to ethical concerns or cost-effectiveness. The modelling studies reflect substantial uncertainty; for example, if environmental transmission (e.g. via droplet contamination of surfaces) of COVID-19 occurs more often than typically assumed by these studies, this would undermine their validity. Given these uncertainties, which relate both to SARS-CoV-2’s transmission and epidemiology and to human behaviour under new, untested scenarios, it is difficult to objectively appraise how realistic the modelling studies’ assumptions (and therefore results) are. Additionally, our review was limited to English-language studies due to short timescales. Our primary outcomes, regarding numbers and proportions of contacts (including of those who become cases) identified, are a key gap in current evidence, and important metrics for evaluation. The integration and relative impacts of manual and automated systems run in parallel are also unexamined. Pre-symptomatic transmission may be substantial in COVID-19,37,38 making timeliness of quarantine critical.8,11 However, the relative timeliness of automated versus manual contact tracing systems is unknown, though partially-automated systems appeared to reduce delays to quarantine.31,33 Additionally, whether quarantine adherence differs between automated and manual systems is unknown. Automated notification might be p...

    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 10.1101/2020.04.14.20063636: (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 will search PubMed, EMBASE, EBSCO Medical COVID information portal, OVID Global Health, Cochrane Library, medRxiv, BioRxiv, and arXiv, for pre-print and peer-reviewed articles from any geographical setting published from 1 January 2000 to 13 April 2020.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    EMBASE
    suggested: (EMBASE, RRID:SCR_001650)
    Cochrane Library
    suggested: (Cochrane Library, RRID:SCR_013000)
    BioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)
    arXiv
    suggested: (arXiv, RRID:SCR_006500)
    Data management: We will use the Covidence software platform for data management, and will report inclusion and exclusion of studies using a PRISMA flowchart.
    Covidence
    suggested: (Covidence, RRID:SCR_016484)

    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.

  4. Version published to 10.1101/2020.05.27.20114447v1 on medRxiv
  5. Version published to 10.1101/2020.04.14.20063636v1 on medRxiv