Literature DB >> 19076326

Characterizing the risk of infection from Mycobacterium tuberculosis in commercial passenger aircraft using quantitative microbial risk assessment.

Rachael M Jones1, Yoshifumi Masago, Timothy Bartrand, Charles N Haas, Mark Nicas, Joan B Rose.   

Abstract

Quantitative microbial risk assessment was used to predict the likelihood and spatial organization of Mycobacterium tuberculosis (Mtb) transmission in a commercial aircraft. Passenger exposure was predicted via a multizone Markov model in four scenarios: seated or moving infectious passengers and with or without filtration of recirculated cabin air. The traditional exponential (k = 1) and a new exponential (k = 0.0218) dose-response function were used to compute infection risk. Emission variability was included by Monte Carlo simulation. Infection risks were higher nearer and aft of the source; steady state airborne concentration levels were not attained. Expected incidence was low to moderate, with the central 95% ranging from 10(-6) to 10(-1) per 169 passengers in the four scenarios. Emission rates used were low compared to measurements from active TB patients in wards, thus a "superspreader" emitting 44 quanta/h could produce 6.2 cases or more under these scenarios. Use of respiratory protection by the infectious source and/or susceptible passengers reduced infection incidence up to one order of magnitude.

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Year:  2008        PMID: 19076326     DOI: 10.1111/j.1539-6924.2008.01161.x

Source DB:  PubMed          Journal:  Risk Anal        ISSN: 0272-4332            Impact factor:   4.000


  11 in total

1.  Personalized ventilation as a control measure for airborne transmissible disease spread.

Authors:  Jovan Pantelic; Gin Nam Sze-To; Kwok Wai Tham; Christopher Y H Chao; Yong Chuan Mike Khoo
Journal:  J R Soc Interface       Date:  2009-10-07       Impact factor: 4.118

2.  Mathematical models for assessing the role of airflow on the risk of airborne infection in hospital wards.

Authors:  Catherine J Noakes; P Andrew Sleigh
Journal:  J R Soc Interface       Date:  2009-10-07       Impact factor: 4.118

3.  Development of a microbial dose response visualization and modelling application for QMRA modelers and educators.

Authors:  Mark H Weir; Jade Mitchell; William Flynn; Joanna M Pope
Journal:  Environ Model Softw       Date:  2016-11-24       Impact factor: 5.288

4.  Dose-response models for selected respiratory infectious agents: Bordetella pertussis, group a Streptococcus, rhinovirus and respiratory syncytial virus.

Authors:  Rachael M Jones; Yu-Min Su
Journal:  BMC Infect Dis       Date:  2015-02-24       Impact factor: 3.090

5.  Logistic growth of a surface contamination network and its role in disease spread.

Authors:  Hao Lei; Yuguo Li; Shenglan Xiao; Xinyan Yang; ChaoHsin Lin; Sharon L Norris; Daniel Wei; Zhongmin Hu; Shengcheng Ji
Journal:  Sci Rep       Date:  2017-11-01       Impact factor: 4.379

6.  Detecting local risk factors for residual malaria in northern Ghana using Bayesian model averaging.

Authors:  Justin Millar; Paul Psychas; Benjamin Abuaku; Collins Ahorlu; Punam Amratia; Kwadwo Koram; Samuel Oppong; Denis Valle
Journal:  Malar J       Date:  2018-09-29       Impact factor: 2.979

Review 7.  Escherichia coli, cattle and the propagation of disease.

Authors:  Richard A Stein; David E Katz
Journal:  FEMS Microbiol Lett       Date:  2017-03-01       Impact factor: 2.742

Review 8.  Microbial Exchange via Fomites and Implications for Human Health.

Authors:  Brent Stephens; Parham Azimi; Megan S Thoemmes; Mohammad Heidarinejad; Joseph G Allen; Jack A Gilbert
Journal:  Curr Pollut Rep       Date:  2019-08-31

9.  Routes of transmission of influenza A H1N1, SARS CoV, and norovirus in air cabin: Comparative analyses.

Authors:  H Lei; Y Li; S Xiao; C-H Lin; S L Norris; D Wei; Z Hu; S Ji
Journal:  Indoor Air       Date:  2018-01-06       Impact factor: 5.770

10.  HVAC filtration for controlling infectious airborne disease transmission in indoor environments: Predicting risk reductions and operational costs.

Authors:  Parham Azimi; Brent Stephens
Journal:  Build Environ       Date:  2013-09-04       Impact factor: 6.456

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