Bayesian emulation and history matching of JUNE

  1. I. Vernon
  2. J. Owen
  3. J. Aylett-Bullock
  4. C. Cuesta-Lazaro
  5. J. Frawley
  6. A. Quera-Bofarull
  7. A. Sedgewick
  8. D. Shi
  9. H. Truong
  10. M. Turner
  11. J. Walker
  12. T. Caulfield
  13. K. Fong
  14. F. Krauss

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Abstract

We analyze JUNE : a detailed model of COVID-19 transmission with high spatial and demographic resolution, developed as part of the RAMP initiative. JUNE  requires substantial computational resources to evaluate, making model calibration and general uncertainty analysis extremely challenging. We describe and employ the uncertainty quantification approaches of Bayes linear emulation and history matching to mimic JUNE  and to perform a global parameter search, hence identifying regions of parameter space that produce acceptable matches to observed data, and demonstrating the capability of such methods.

This article is part of the theme issue ‘Technical challenges of modelling real-life epidemics and examples of overcoming these’.

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  1. Version published to 10.1098/rsta.2022.0039
  2. ScreenIT

    SciScore for 10.1101/2022.02.21.22271249: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code.

    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.

    Results from scite Reference Check: We found no unreliable references.

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  3. Version published to 10.1101/2022.02.21.22271249v1 on medRxiv