Andrew M. Bate , Daniel Miller , Marco-Felipe King , Katy-Anne Moseley , Jingsi Xu , Ian Hall , Martín López-García , Simon T. Parker , Catherine J. Noakes
{"title":"采用定量微生物风险评估方法估算公交车上的 SARS-CoV-2 暴露量","authors":"Andrew M. Bate , Daniel Miller , Marco-Felipe King , Katy-Anne Moseley , Jingsi Xu , Ian Hall , Martín López-García , Simon T. Parker , Catherine J. Noakes","doi":"10.1016/j.jth.2024.101829","DOIUrl":null,"url":null,"abstract":"<div><h3>Introduction</h3><p>We adapt and extend the Transmission of Virus in Carriages (TVC) model, a Quantitative Microbial Risk Assessment (QMRA) computational model originally developed to estimate exposure risk to SARS-CoV-2 in a subway carriage, to estimate exposure risk on a bus. The aim is to analyse the relative importance of different behavioural and environmental factors influencing exposure in this public transport setting, especially exposure to large doses when considering viral load variability between infectious passengers.</p></div><div><h3>Methods</h3><p>The QMRA model considers individual exposure during a bus journey through three routes: close-range exposure to aerosols and droplets due to being at close proximity (<span><math><mo><</mo></math></span>2m) of an infected passenger, long-range airborne exposure at long distances (<span><math><mo>></mo></math></span>2m), and transmission via contaminated surfaces (fomite route).</p></div><div><h3>Results</h3><p>Model predictions show that disease prevalence and bus loading levels have a major impact both on the likelihood of exposure and probability of receiving a large dose. Mask wearing is predicted to greatly reduce the magnitude of doses received, especially from close-range exposure. Assumptions around viral load also have a major impact on doses received, with large long-range airborne doses only occurring under very wide viral load distributions. Doses are not uniform around the bus, with close-range dose being generally more likely in the middle of the bus and fomite doses depending on the types of available surfaces around passengers’ seated/standing positions. Surface contamination is predicted to be greatest on traversal poles that may be touched by many passengers while boarding and alighting.</p></div><div><h3>Conclusions</h3><p>These model predictions have implications on the effectiveness of various mitigations to SARS-CoV-2 transmission on buses.</p></div>","PeriodicalId":47838,"journal":{"name":"Journal of Transport & Health","volume":"38 ","pages":"Article 101829"},"PeriodicalIF":3.2000,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214140524000756/pdfft?md5=45e3d92d41343d710e15d695942a1802&pid=1-s2.0-S2214140524000756-main.pdf","citationCount":"0","resultStr":"{\"title\":\"A quantitative microbial risk assessment approach to estimate exposure to SARS-CoV-2 on a bus\",\"authors\":\"Andrew M. Bate , Daniel Miller , Marco-Felipe King , Katy-Anne Moseley , Jingsi Xu , Ian Hall , Martín López-García , Simon T. Parker , Catherine J. Noakes\",\"doi\":\"10.1016/j.jth.2024.101829\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Introduction</h3><p>We adapt and extend the Transmission of Virus in Carriages (TVC) model, a Quantitative Microbial Risk Assessment (QMRA) computational model originally developed to estimate exposure risk to SARS-CoV-2 in a subway carriage, to estimate exposure risk on a bus. The aim is to analyse the relative importance of different behavioural and environmental factors influencing exposure in this public transport setting, especially exposure to large doses when considering viral load variability between infectious passengers.</p></div><div><h3>Methods</h3><p>The QMRA model considers individual exposure during a bus journey through three routes: close-range exposure to aerosols and droplets due to being at close proximity (<span><math><mo><</mo></math></span>2m) of an infected passenger, long-range airborne exposure at long distances (<span><math><mo>></mo></math></span>2m), and transmission via contaminated surfaces (fomite route).</p></div><div><h3>Results</h3><p>Model predictions show that disease prevalence and bus loading levels have a major impact both on the likelihood of exposure and probability of receiving a large dose. Mask wearing is predicted to greatly reduce the magnitude of doses received, especially from close-range exposure. Assumptions around viral load also have a major impact on doses received, with large long-range airborne doses only occurring under very wide viral load distributions. Doses are not uniform around the bus, with close-range dose being generally more likely in the middle of the bus and fomite doses depending on the types of available surfaces around passengers’ seated/standing positions. Surface contamination is predicted to be greatest on traversal poles that may be touched by many passengers while boarding and alighting.</p></div><div><h3>Conclusions</h3><p>These model predictions have implications on the effectiveness of various mitigations to SARS-CoV-2 transmission on buses.</p></div>\",\"PeriodicalId\":47838,\"journal\":{\"name\":\"Journal of Transport & Health\",\"volume\":\"38 \",\"pages\":\"Article 101829\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-06-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2214140524000756/pdfft?md5=45e3d92d41343d710e15d695942a1802&pid=1-s2.0-S2214140524000756-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Transport & Health\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2214140524000756\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Transport & Health","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214140524000756","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
A quantitative microbial risk assessment approach to estimate exposure to SARS-CoV-2 on a bus
Introduction
We adapt and extend the Transmission of Virus in Carriages (TVC) model, a Quantitative Microbial Risk Assessment (QMRA) computational model originally developed to estimate exposure risk to SARS-CoV-2 in a subway carriage, to estimate exposure risk on a bus. The aim is to analyse the relative importance of different behavioural and environmental factors influencing exposure in this public transport setting, especially exposure to large doses when considering viral load variability between infectious passengers.
Methods
The QMRA model considers individual exposure during a bus journey through three routes: close-range exposure to aerosols and droplets due to being at close proximity (2m) of an infected passenger, long-range airborne exposure at long distances (2m), and transmission via contaminated surfaces (fomite route).
Results
Model predictions show that disease prevalence and bus loading levels have a major impact both on the likelihood of exposure and probability of receiving a large dose. Mask wearing is predicted to greatly reduce the magnitude of doses received, especially from close-range exposure. Assumptions around viral load also have a major impact on doses received, with large long-range airborne doses only occurring under very wide viral load distributions. Doses are not uniform around the bus, with close-range dose being generally more likely in the middle of the bus and fomite doses depending on the types of available surfaces around passengers’ seated/standing positions. Surface contamination is predicted to be greatest on traversal poles that may be touched by many passengers while boarding and alighting.
Conclusions
These model predictions have implications on the effectiveness of various mitigations to SARS-CoV-2 transmission on buses.