A population framework for predicting the proportion of people infected by the far-field airborne transmission of SARS-CoV-2 indoors

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Abstract

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  1. SciScore for 10.1101/2021.11.24.21266807: (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: 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: We detected the following sentences addressing limitations in the study:
    Limitations: Some limitations and uncertainties in this work have already been addressed, particularly those concerning the viral load and the dose-response relationship. However, there are a number of other aspects that increase uncertainty in it. Firstly, the models assume homogenous instantly mixed indoor air to simplify the estimate of a dose. This assumption is unlikely to be true in some spaces, especially in large spaces where the concentrations of virions in the air is likely be a function of the distance from the infected person. It is unclear at which space volume this assumption becomes less useful, but it is likely to be a few thousand cubic metres. The approach described in Section 2 only considers the far-field transmission of virus, and not near-field transmission, which is likely to be the dominant route of transmission. The concentration of the virus in aerosols and droplets per unit volume of air is several orders of magnitude greater closer to the infected person at distances of < 2 m [3, 9]. However, it is likely that the method of calculating the probability of viral load of infected people, P (L), is also important for the dose received by near-field transmission and should be explored further in the future. The distribution of viral load of an infected person around the median will affect the probability of transmission. We apply a log-normal distribution, see Section 2, but another, such as the Weibull distribution, will affect the transmission probabi...

    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.


    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.