A privacy-preserving Bayesian network model for personalised COVID19 risk assessment and contact tracing

  1. Norman E Fenton
  2. Scott McLachlan
  3. Peter Lucas
  4. Kudakwashe Dube
  5. Graham A Hitman
  6. Magda Osman
  7. Evangelia Kyrimi
  8. Martin Neil

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Abstract

Concerns about the practicality and effectiveness of using Contact Tracing Apps (CTA) to reduce the spread of COVID19 have been well documented and, in the UK, led to the abandonment of the NHS CTA shortly after its release in May 2020. One of the key non-technical obstacles to widespread adoption of CTA has been concerns about privacy. We present a causal probabilistic model (a Bayesian network) that provides the basis for a practical CTA solution that does not compromise privacy. Users of the model can provide as much or little personal information as they wish about relevant risk factors, symptoms, and recent social interactions. The model then provides them feedback about the likelihood of the presence of asymptotic, mild or severe COVID19 (past, present and projected). When the model is embedded in a smartphone app, it can be used to detect new outbreaks in a monitored population and identify outbreak locations as early as possible. For this purpose, the only data needed to be centrally collected is the probability the user has COVID19 and the GPS location.

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  1. Version published to 10.1101/2020.07.15.20154286v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.07.15.20154286: (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

    No key resources detected.

    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

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  3. Version published to 10.1101/2020.07.15.20154286v1 on medRxiv