Proactive COVID-19 testing in a partially vaccinated population

  1. Ryan S. McGee
  2. Julian R. Homburger
  3. Hannah E. Williams
  4. Carl T. Bergstrom
  5. Alicia Y. Zhou

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Abstract

During the initial stages of the COVID-19 pandemic, many workplaces and universities implemented institution-wide proactive testing programs of all individuals, ir-respective of symptoms. These measures have proven effective in mitigating outbreaks. As a greater fraction of the population becomes vaccinated, we need to understand what continued benefit, if any, proactive testing can contribute. Here, we address this problem with two distinct modeling approaches: a simple analytical model and a more simulation using the SEIRS+ platform. Both models indicate that proactive testing remains useful until a threshold level of vaccination is reached. This threshold depends on the transmissibility of the virus and the scope of other control measures in place. If a community is able to reach the threshold level of vaccination, testing can cease. Otherwise, continued testing will be an important component of disease control. Because it is usually difficult or impossible to precisely estimate key parameters such as the basic reproduction number for a specific workplace or other setting, our results are more useful for understanding general trends than for making precise quantitative predictions.

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  1. ScreenIT

    SciScore for 10.1101/2021.08.15.21262095: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The SEIRS+ model: Our open source Python framework SEIRS+ implements stochastic network models of infectious disease transmission (https://github.com/ryansmcgee/seirsplus).
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code and data.

    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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  2. Version published to 10.1101/2021.08.15.21262095 on medRxiv