Segregation of children into small groups for in-person learning during the COVID-19 pandemic

  1. Luis Manuel Muñoz-Nava
  2. Marcos Nahmad

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Abstract

The COVID-19 pandemic affected in-person learning worldwide due to fears that schools could contribute to the propagation of the virus within their communities. Using computational modeling, we compare the reopening of schools with mitigation measures with a strategy in which schoolchildren are segregated into small isolated groups or “bubbles,” where children physically interact without restrictions while receiving remote instruction from their teachers. This strategy is robust to common perturbations and is more flexible and stable than reopening of schools. Our modeling results and a real-world implementation of a bubbles program in an elementary school in Mexico City support that this strategy is an effective transition alternative, especially in communities with low vaccination rates or where operational costs associated to safely reopening schools cannot be afforded.

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  1. ScreenIT

    SciScore for 10.1101/2021.08.17.21261993: (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 school network and formation of bubbles: Using the NetworkX (https://networkx.org/) python package, we modeled a school network composed of 200 independent families.
    python
    suggested: (IPython, RRID:SCR_001658)
    Data analysis and visualization: All the data obtained from the simulations was analyzed using the Python3 programming language (https://www.python.org/).
    Python3
    suggested: None
    https://www.python.org/
    suggested: (CVXOPT - Python Software for Convex Optimization, RRID:SCR_002918)
    The packages used for the analysis were Pandas (https://pandas.pydata.org/) and Numpy (https://numpy.org/), for the statistical test we used the Scipy (https://www.scipy.org/) package and the visualization was done with the Matplotlib (https://matplotlib.org/) package.
    Numpy
    suggested: (NumPy, RRID:SCR_008633)
    Scipy
    suggested: (SciPy, RRID:SCR_008058)
    Matplotlib
    suggested: (MatPlotLib, RRID:SCR_008624)

    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.
    • No funding statement was detected.
    • No protocol registration statement was detected.

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

    About SciScore

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  2. Version published to 10.1101/2021.08.17.21261993 on medRxiv