Calibrating an Epidemic Compartment Model to Seroprevalence Survey Data

  1. Michael Halem

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Abstract

To date, the Covid-19 epidemic has produced tremendous cost and harm. However, to date, many epidemic models are not calibrated to seroprevalence survey(s). This paper calibrates a relatively simple, SIR plus confirmed cases (“SIRX”) model against seroprevalence survey data released by the State of New York. The intention of this paper is to demonstrate a potentially new technique of calibration for epidemic models used by scientists, public health officials and governments. The technique can then be incorporated in other more complex models. Open source code is included to assist model developers.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.27.20110478: (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: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Weaknesses:

    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 10.1101/2020.05.27.20110478v1 on medRxiv