alexa Seminal Mycobacterium Tuberculosis in vivo Transmission Studies: Reanalysis Using Probabilistic Modelling
ISSN: 2161-1068

Mycobacterial Diseases
Open Access

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Research Article

Seminal Mycobacterium Tuberculosis in vivo Transmission Studies: Reanalysis Using Probabilistic Modelling

Chacha M. Issarow1, Robin Wood2*, and Nicola Mulder1

1Computational Biology Group, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, South Africa

2The Desmond Tutu HIV Centre, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, South Africa

*Corresponding Author:
Robin Wood
The Desmond Tutu HIV Centre
Institute of Infectious Disease and Molecular Medicine
Faculty of Health Sciences
University of Cape Town
South Africa
Tel: 27 216506966
Fax: 27 21 6506963
E-mail: [email protected]

Received date: June 20, 2016; Accepted date: July 07, 2016; Published date: July 13, 2016

Citation: Issarow CM, Wood R, Mulder N (2016) Seminal Mycobacterium Tuberculosis in vivo Transmission Studies: Reanalysis Using Probabilistic Modelling. Mycobact Dis 6:217. doi:10.4172/2161-1068.1000217

Copyright: © 2016 Issarow CM, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Much of our current knowledge of Mycobacterium tuberculosis (MTB) transmission originates from seminal human-to-guinea pig in vivo studies, carried out in the 1950s. Similar methodology has been used to investigate human immunodeficiency virus (HIV) co-infection and multidrug resistant TB. However, all these studies have had to reconcile the need to use high facility ventilation rates in order to decrease risks of human-to-human infection while demonstrating human-to-guinea pig transmission. While these studies demonstrate tuberculosis (TB) contagion can be airborne they also estimated extremely low infectivity of TB cases. However, calculated infectivity was based on a theoretical concept of quantal infection and assumed that the guinea pig model was 100% sensitivity for the remote detection of viable TB organisms in highly diluted air exhausted from the facility. High facility ventilation markedly decreases the probability of a successful guinea pig infection by both dilution of the exhaled breath and decreasing the proportion of air sampled by guinea pigs. In this study, we used a new mathematical model based on Poisson distribution and previous guinea pig experimental data to quantify a more realistic estimate of the number of infective organisms required to produce a successful infection for exposed guinea pigs in the in vivo studies. Furthermore, we explored the probability of exposed guinea pigs acquiring infection in these studies. We found that the in vivo studies to date were underestimated to demonstrate transmission derived from any but the most productive infectious cases. All four in vivo studies have remarkably low probability of infection of exposed guinea pigs due to either high ventilation rates or insensitive mathematical model used in these studies. Therefore, our analysis would suggest that the production of infective organisms by TB cases might have been markedly underestimated. This reassessment of the infectivity of guinea pigs is compatible with recent findings of very high numbers of TB genomes present in health care environments and the very diverse distribution of TB strains present in highly endemic settings which indicates a multiplicity of infective sources.

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