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A dynamic physical-distancing model to evaluate spatial measures for prevention of Covid-19 spread()
Motivated by the global pandemic of COVID-19, this study investigates the spatial factors influencing physical distancing, and how these affect the transmission of the SARS-CoV-2 virus, by integrating pedestrian dynamics with a modified susceptible–exposed–infectious model. Contacts between infected...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Authors. Published by Elsevier B.V.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8709728/ https://www.ncbi.nlm.nih.gov/pubmed/34975209 http://dx.doi.org/10.1016/j.physa.2021.126734 |
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author | Xiao, Tianyi Mu, Tong Shen, Sunle Song, Yiming Yang, Shufan He, Jie |
author_facet | Xiao, Tianyi Mu, Tong Shen, Sunle Song, Yiming Yang, Shufan He, Jie |
author_sort | Xiao, Tianyi |
collection | PubMed |
description | Motivated by the global pandemic of COVID-19, this study investigates the spatial factors influencing physical distancing, and how these affect the transmission of the SARS-CoV-2 virus, by integrating pedestrian dynamics with a modified susceptible–exposed–infectious model. Contacts between infected and susceptible pedestrians are examined by determining physical-distancing pedestrian dynamics in three types of spaces, and used to estimate the proportion of newly infected pedestrians in these spaces. Desired behaviour for physical distancing can be observed from simulation results, and aggregated simulation findings reveal that certain layouts enable physical distancing to reduce the transmission of SARS-CoV-2. We also provide policymakers with several design guidelines on how to proactively design more effective and resilient space layouts in the context of pandemics to keep low transmission risks while maintaining a high pedestrian volume. This approach has enormous application potential for other infectious-disease transmission and space assessments. |
format | Online Article Text |
id | pubmed-8709728 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Authors. Published by Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87097282021-12-28 A dynamic physical-distancing model to evaluate spatial measures for prevention of Covid-19 spread() Xiao, Tianyi Mu, Tong Shen, Sunle Song, Yiming Yang, Shufan He, Jie Physica A Article Motivated by the global pandemic of COVID-19, this study investigates the spatial factors influencing physical distancing, and how these affect the transmission of the SARS-CoV-2 virus, by integrating pedestrian dynamics with a modified susceptible–exposed–infectious model. Contacts between infected and susceptible pedestrians are examined by determining physical-distancing pedestrian dynamics in three types of spaces, and used to estimate the proportion of newly infected pedestrians in these spaces. Desired behaviour for physical distancing can be observed from simulation results, and aggregated simulation findings reveal that certain layouts enable physical distancing to reduce the transmission of SARS-CoV-2. We also provide policymakers with several design guidelines on how to proactively design more effective and resilient space layouts in the context of pandemics to keep low transmission risks while maintaining a high pedestrian volume. This approach has enormous application potential for other infectious-disease transmission and space assessments. The Authors. Published by Elsevier B.V. 2022-04-15 2021-12-25 /pmc/articles/PMC8709728/ /pubmed/34975209 http://dx.doi.org/10.1016/j.physa.2021.126734 Text en © 2021 The Authors Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Xiao, Tianyi Mu, Tong Shen, Sunle Song, Yiming Yang, Shufan He, Jie A dynamic physical-distancing model to evaluate spatial measures for prevention of Covid-19 spread() |
title | A dynamic physical-distancing model to evaluate spatial measures for prevention of Covid-19 spread() |
title_full | A dynamic physical-distancing model to evaluate spatial measures for prevention of Covid-19 spread() |
title_fullStr | A dynamic physical-distancing model to evaluate spatial measures for prevention of Covid-19 spread() |
title_full_unstemmed | A dynamic physical-distancing model to evaluate spatial measures for prevention of Covid-19 spread() |
title_short | A dynamic physical-distancing model to evaluate spatial measures for prevention of Covid-19 spread() |
title_sort | dynamic physical-distancing model to evaluate spatial measures for prevention of covid-19 spread() |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8709728/ https://www.ncbi.nlm.nih.gov/pubmed/34975209 http://dx.doi.org/10.1016/j.physa.2021.126734 |
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