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A probabilistic model to evaluate the effectiveness of main solutions to COVID-19 spreading in university buildings according to proximity and time-based consolidated criteria
University buildings are one of the most relevant closed environments in which the COVID-19 event clearly pointed out stakeholders’ needs toward safety issues, especially because of the possibility of day-to-day presences of the same users (i.e. students, teachers) and overcrowding causing long-last...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Tsinghua University Press
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7910197/ https://www.ncbi.nlm.nih.gov/pubmed/33680337 http://dx.doi.org/10.1007/s12273-021-0770-2 |
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author | D’Orazio, Marco Bernardini, Gabriele Quagliarini, Enrico |
author_facet | D’Orazio, Marco Bernardini, Gabriele Quagliarini, Enrico |
author_sort | D’Orazio, Marco |
collection | PubMed |
description | University buildings are one of the most relevant closed environments in which the COVID-19 event clearly pointed out stakeholders’ needs toward safety issues, especially because of the possibility of day-to-day presences of the same users (i.e. students, teachers) and overcrowding causing long-lasting contacts with possible “infectors”. While waiting for the vaccine, as for other public buildings, policy-makers’ measures to limit virus outbreaks combine individual’s strategies (facial masks), occupants’ capacity and access control. But, up to now, no easy-to-apply tools are available for assessing the punctual effectiveness of such measures. To fill this gap, this work proposes a quick and probabilistic simulation model based on consolidated proximity and exposure-time-based rules for virus transmission confirmed by international health organizations. The building occupancy is defined according to university scheduling, identifying the main “attraction areas” in the building (classrooms, break-areas). Scenarios are defined in terms of occupants’ densities and the above-mentioned mitigation strategies. The model is calibrated on experimental data and applied to a relevant university building. Results demonstrate the model capabilities. In particular, it underlines that if such strategies are not combined, the virus spreading can be limited by only using high protection respiratory devices (i.e. FFP3) by almost every occupant. On the contrary, the combination between access control and building capacity limitation can lead to the adoption of lighter protective devices (i.e. surgical masks), thus improving the feasibility, users’ comfort and favorable reception. Simplified rules to combine acceptable mask filters-occupants’ density are thus provided to help stakeholders in organizing users’ presences in the building during the pandemic. ELECTRONIC SUPPLEMENTARY MATERIAL (ESM): supplementary material is available in the online version of this article at 10.1007/s12273-021-0770-2. |
format | Online Article Text |
id | pubmed-7910197 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Tsinghua University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-79101972021-03-01 A probabilistic model to evaluate the effectiveness of main solutions to COVID-19 spreading in university buildings according to proximity and time-based consolidated criteria D’Orazio, Marco Bernardini, Gabriele Quagliarini, Enrico Build Simul Research Article University buildings are one of the most relevant closed environments in which the COVID-19 event clearly pointed out stakeholders’ needs toward safety issues, especially because of the possibility of day-to-day presences of the same users (i.e. students, teachers) and overcrowding causing long-lasting contacts with possible “infectors”. While waiting for the vaccine, as for other public buildings, policy-makers’ measures to limit virus outbreaks combine individual’s strategies (facial masks), occupants’ capacity and access control. But, up to now, no easy-to-apply tools are available for assessing the punctual effectiveness of such measures. To fill this gap, this work proposes a quick and probabilistic simulation model based on consolidated proximity and exposure-time-based rules for virus transmission confirmed by international health organizations. The building occupancy is defined according to university scheduling, identifying the main “attraction areas” in the building (classrooms, break-areas). Scenarios are defined in terms of occupants’ densities and the above-mentioned mitigation strategies. The model is calibrated on experimental data and applied to a relevant university building. Results demonstrate the model capabilities. In particular, it underlines that if such strategies are not combined, the virus spreading can be limited by only using high protection respiratory devices (i.e. FFP3) by almost every occupant. On the contrary, the combination between access control and building capacity limitation can lead to the adoption of lighter protective devices (i.e. surgical masks), thus improving the feasibility, users’ comfort and favorable reception. Simplified rules to combine acceptable mask filters-occupants’ density are thus provided to help stakeholders in organizing users’ presences in the building during the pandemic. ELECTRONIC SUPPLEMENTARY MATERIAL (ESM): supplementary material is available in the online version of this article at 10.1007/s12273-021-0770-2. Tsinghua University Press 2021-02-27 2021 /pmc/articles/PMC7910197/ /pubmed/33680337 http://dx.doi.org/10.1007/s12273-021-0770-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) |
spellingShingle | Research Article D’Orazio, Marco Bernardini, Gabriele Quagliarini, Enrico A probabilistic model to evaluate the effectiveness of main solutions to COVID-19 spreading in university buildings according to proximity and time-based consolidated criteria |
title | A probabilistic model to evaluate the effectiveness of main solutions to COVID-19 spreading in university buildings according to proximity and time-based consolidated criteria |
title_full | A probabilistic model to evaluate the effectiveness of main solutions to COVID-19 spreading in university buildings according to proximity and time-based consolidated criteria |
title_fullStr | A probabilistic model to evaluate the effectiveness of main solutions to COVID-19 spreading in university buildings according to proximity and time-based consolidated criteria |
title_full_unstemmed | A probabilistic model to evaluate the effectiveness of main solutions to COVID-19 spreading in university buildings according to proximity and time-based consolidated criteria |
title_short | A probabilistic model to evaluate the effectiveness of main solutions to COVID-19 spreading in university buildings according to proximity and time-based consolidated criteria |
title_sort | probabilistic model to evaluate the effectiveness of main solutions to covid-19 spreading in university buildings according to proximity and time-based consolidated criteria |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7910197/ https://www.ncbi.nlm.nih.gov/pubmed/33680337 http://dx.doi.org/10.1007/s12273-021-0770-2 |
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