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A Probabilistic Transmission Model to Assess Infection Risk from Mycobacterium Tuberculosis in Commercial Passenger Trains

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  • Szu‐Chieh Chen
  • Chung‐Min Liao
  • Sih‐Syuan Li
  • Shu‐Han You

Abstract

The objective of this article is to characterize the risk of infection from airborne Mycobacterium tuberculosis bacilli exposure in commercial passenger trains based on a risk‐based probabilistic transmission modeling. We investigated the tuberculosis (TB) infection risks among commercial passengers by inhaled aerosol M. tuberculosis bacilli and quantify the patterns of TB transmission in Taiwan High Speed Rail (THSR). A deterministic Wells‐Riley mathematical model was used to account for the probability of infection risk from M. tuberculosis bacilli by linking the cough‐generated aerosol M. tuberculosis bacilli concentration and particle size distribution. We found that (i) the quantum generation rate of TB was estimated with a lognormal distribution of geometric mean (GM) of 54.29 and geometric standard deviation (GSD) of 3.05 quantum/h at particle size ≤ 5 μm and (ii) the basic reproduction numbers (R0) were estimated to be 0.69 (0.06–6.79), 2.82 (0.32–20.97), and 2.31 (0.25–17.69) for business, standard, and nonreserved cabins, respectively. The results indicate that commercial passengers taking standard and nonreserved cabins had higher transmission risk than those in business cabins based on conservatism. Our results also reveal that even a brief exposure, as in the bronchoscopy cases, can also result in a transmission when the quantum generation rate is high. This study could contribute to a better understanding of the dynamics of TB transmission in commercial passenger trains by assessing the relationship between TB infectiousness, passenger mobility, and key model parameters such as seat occupancy, ventilation rate, and exposure duration.

Suggested Citation

  • Szu‐Chieh Chen & Chung‐Min Liao & Sih‐Syuan Li & Shu‐Han You, 2011. "A Probabilistic Transmission Model to Assess Infection Risk from Mycobacterium Tuberculosis in Commercial Passenger Trains," Risk Analysis, John Wiley & Sons, vol. 31(6), pages 930-939, June.
    • Handle: RePEc:wly:riskan:v:31:y:2011:i:6:p:930-939
    DOI: 10.1111/j.1539-6924.2010.01552.x
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    References listed on IDEAS

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    1. Mark Nicas, 1996. "An Analytical Framework for Relating Dose, Risk, and Incidence: An Application to Occupational Tuberculosis Infection," Risk Analysis, John Wiley & Sons, vol. 16(4), pages 527-538, August.
    2. Gwangpyo Ko & Kimberly M. Thompson & Edward A. Nardell, 2004. "Estimation of Tuberculosis Risk on a Commercial Airliner," Risk Analysis, John Wiley & Sons, vol. 24(2), pages 379-388, April.
    3. Adrian Roderick Escombe & David A J Moore & Jon S Friedland & Carlton A Evans & Robert H Gilman, 2007. "Natural Ventilation for Prevention of Airborne Contagion: Authors' Reply," PLOS Medicine, Public Library of Science, vol. 4(5), pages 1-2, May.
    4. Rachael M. Jones & Yoshifumi Masago & Timothy Bartrand & Charles N. Haas & Mark Nicas & Joan B. Rose, 2009. "Characterizing the Risk of Infection from Mycobacterium tuberculosis in Commercial Passenger Aircraft Using Quantitative Microbial Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 29(3), pages 355-365, March.
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    1. Li, Tao & Rong, Lili & Zhang, Anming, 2021. "Assessing regional risk of COVID-19 infection from Wuhan via high-speed rail," Transport Policy, Elsevier, vol. 106(C), pages 226-238.

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