MODELLING PRESYMPTOMATIC INFECTIOUSNESS IN COVID-19

  1. Russell Cheng
  2. Christopher Dye
  3. John Dagpunar
  4. Brian Williams

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Abstract

This paper considers SEPIR, the extension of an existing parametric SEIR continuous simulation compartment model. Both models can be fitted to real data as they include parameters that can simply be estimated from the data. However SEPIR deploys an additional presymptomatic (also called asymptomatic) infectious stage that is not included in SEIR but which is known to exist in COVID-19. This stage is also parametrised and so can be fitted to data. Both SEPIR and the existing SEIR model assume a homogeneous mixing population, an idealisation that is unrealistic in practice when dynamically varying control strategies are deployed against virus. This means that if either model is to represent more than just a single period in the behaviour of the epidemic, then the parameters of the model will have to be time dependent. This issue is also discussed in this paper.

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  1. ScreenIT

    SciScore for 10.1101/2020.11.01.20224014: (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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  2. Version published to 10.1101/2020.11.01.20224014v1 on medRxiv