COVID-19 Exposure Assessment Tool (CEAT): Exposure quantification based on ventilation, infection prevalence, group characteristics, and behavior
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Abstract
The coronavirus disease 2019 (COVID-19) Exposure Assessment Tool (CEAT) allows users to compare respiratory relative risk to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for various scenarios, providing understanding of how combinations of protective measures affect risk. CEAT incorporates mechanistic, stochastic, and epidemiological factors including the (i) emission rate of virus, (ii) viral aerosol degradation and removal, (iii) duration of activity/exposure, (iv) inhalation rates, (v) ventilation rates (indoors/outdoors), (vi) volume of indoor space, (vii) filtration, (viii) mask use and effectiveness, (ix) distance between people (taking into account both near-field and far-field effects of proximity), (x) group size, (xi) current infection rates by variant, (xii) prevalence of infection and immunity in the community, (xiii) vaccination rates, and (xiv) implementation of COVID-19 testing procedures. CEAT applied to published studies of COVID-19 transmission events demonstrates the model’s accuracy. We also show how health and safety professionals at NASA Ames Research Center used CEAT to manage potential risks posed by SARS-CoV-2 exposures.
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SciScore for 10.1101/2022.03.02.22271806: (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 Sentences Resources All data was recorded in Microsoft Excel 2019 and all data analyses were completed using R version 4.0.3, Microsoft Excelsuggested: (Microsoft Excel, RRID:SCR_016137)To generate a graphical representation of the data (Fig. 4) we associated numerical values to the different parameters in the table and utilized R version 4.03, RStudio version 1.4.1717 with the following R packages: ggplot2 v3.3.5 (Wickham 2016). RStudiosuggested: (RStudio, RRID:SCR_000432)ggplot2suggested: (ggplot2, RRID:SCR_014601)Results from OddPub: We did not detect open data. We also did not detect open code. …
SciScore for 10.1101/2022.03.02.22271806: (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 Sentences Resources All data was recorded in Microsoft Excel 2019 and all data analyses were completed using R version 4.0.3, Microsoft Excelsuggested: (Microsoft Excel, RRID:SCR_016137)To generate a graphical representation of the data (Fig. 4) we associated numerical values to the different parameters in the table and utilized R version 4.03, RStudio version 1.4.1717 with the following R packages: ggplot2 v3.3.5 (Wickham 2016). RStudiosuggested: (RStudio, RRID:SCR_000432)ggplot2suggested: (ggplot2, RRID:SCR_014601)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.
- Thank you for including a protocol registration statement.
Results from scite Reference Check: We found no unreliable references.
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