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Identifying mitigation strategies for COVID-19 superspreading on flights using models that account for passenger movement
BACKGROUND: Despite commercial airlines mandating masks, there have been multiple documented events of COVID-19 superspreading on flights. Conventional models do not adequately explain superspreading patterns on flights, with infection spread wider than expected from proximity based on passenger sea...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8925197/ https://www.ncbi.nlm.nih.gov/pubmed/35306163 http://dx.doi.org/10.1016/j.tmaid.2022.102313 |
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author | Namilae, Sirish Wu, Yuxuan Mubayi, Anuj Srinivasan, Ashok Scotch, Matthew |
author_facet | Namilae, Sirish Wu, Yuxuan Mubayi, Anuj Srinivasan, Ashok Scotch, Matthew |
author_sort | Namilae, Sirish |
collection | PubMed |
description | BACKGROUND: Despite commercial airlines mandating masks, there have been multiple documented events of COVID-19 superspreading on flights. Conventional models do not adequately explain superspreading patterns on flights, with infection spread wider than expected from proximity based on passenger seating. An important reason for this is that models typically do not consider the movement of passengers during the flight, boarding, or deplaning. Understanding the risks for each of these aspects could provide insight into effective mitigation measures. METHODS: We modeled infection risk from seating and fine-grained movement patterns – boarding, deplaning, and inflight movement. We estimated infection model parameters from a prior superspreading event. We validated the model and the impact of interventions using available data from three flights, including cabin layout and seat locations of infected and uninfected passengers, to suggest interventions to mitigate COVID-19 superspreading events during air travel. Specifically, we studied: 1) London to Hanoi with 201 passengers, including 13 secondary infections among passengers; 2) Singapore to Hangzhou with 321 passengers, including 12 to 14 secondary infections; 3) a non-superspreading event on a private jet in Japan with 9 passengers and no secondary infections. RESULTS: Our results show that the inclusion of passenger movement better explains the infection spread patterns than conventional models do. We also found that FFP2/N95 mask usage would have reduced infection by 95–100%, while cloth masks would have reduced it by only 40–80%. Results indicate that leaving the middle seat vacant is effective in reducing infection, and the effectiveness increases when combined with good quality masks. However, with a good mask, the risk is quite low even without the middle seats being empty. CONCLUSIONS: Our results suggest the need for more stringent guidelines to reduce aviation-related superspreading events of COVID-19. |
format | Online Article Text |
id | pubmed-8925197 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89251972022-03-17 Identifying mitigation strategies for COVID-19 superspreading on flights using models that account for passenger movement Namilae, Sirish Wu, Yuxuan Mubayi, Anuj Srinivasan, Ashok Scotch, Matthew Travel Med Infect Dis Article BACKGROUND: Despite commercial airlines mandating masks, there have been multiple documented events of COVID-19 superspreading on flights. Conventional models do not adequately explain superspreading patterns on flights, with infection spread wider than expected from proximity based on passenger seating. An important reason for this is that models typically do not consider the movement of passengers during the flight, boarding, or deplaning. Understanding the risks for each of these aspects could provide insight into effective mitigation measures. METHODS: We modeled infection risk from seating and fine-grained movement patterns – boarding, deplaning, and inflight movement. We estimated infection model parameters from a prior superspreading event. We validated the model and the impact of interventions using available data from three flights, including cabin layout and seat locations of infected and uninfected passengers, to suggest interventions to mitigate COVID-19 superspreading events during air travel. Specifically, we studied: 1) London to Hanoi with 201 passengers, including 13 secondary infections among passengers; 2) Singapore to Hangzhou with 321 passengers, including 12 to 14 secondary infections; 3) a non-superspreading event on a private jet in Japan with 9 passengers and no secondary infections. RESULTS: Our results show that the inclusion of passenger movement better explains the infection spread patterns than conventional models do. We also found that FFP2/N95 mask usage would have reduced infection by 95–100%, while cloth masks would have reduced it by only 40–80%. Results indicate that leaving the middle seat vacant is effective in reducing infection, and the effectiveness increases when combined with good quality masks. However, with a good mask, the risk is quite low even without the middle seats being empty. CONCLUSIONS: Our results suggest the need for more stringent guidelines to reduce aviation-related superspreading events of COVID-19. Elsevier Ltd. 2022 2022-03-16 /pmc/articles/PMC8925197/ /pubmed/35306163 http://dx.doi.org/10.1016/j.tmaid.2022.102313 Text en © 2022 Elsevier Ltd. All rights reserved. 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 Namilae, Sirish Wu, Yuxuan Mubayi, Anuj Srinivasan, Ashok Scotch, Matthew Identifying mitigation strategies for COVID-19 superspreading on flights using models that account for passenger movement |
title | Identifying mitigation strategies for COVID-19 superspreading on flights using models that account for passenger movement |
title_full | Identifying mitigation strategies for COVID-19 superspreading on flights using models that account for passenger movement |
title_fullStr | Identifying mitigation strategies for COVID-19 superspreading on flights using models that account for passenger movement |
title_full_unstemmed | Identifying mitigation strategies for COVID-19 superspreading on flights using models that account for passenger movement |
title_short | Identifying mitigation strategies for COVID-19 superspreading on flights using models that account for passenger movement |
title_sort | identifying mitigation strategies for covid-19 superspreading on flights using models that account for passenger movement |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8925197/ https://www.ncbi.nlm.nih.gov/pubmed/35306163 http://dx.doi.org/10.1016/j.tmaid.2022.102313 |
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