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Originally published as Genetics Published Articles Ahead of Print on April 19, 2006.
Genetics, Vol. 173, 1511-1520, July 2006, Copyright © 2006
doi:10.1534/genetics.106.055574
Using Approximate Bayesian Computation to Estimate Tuberculosis Transmission Parameters From Genotype Data
Mark M. Tanaka*,1,
Andrew R. Francis
,
Fabio Luciani* and
S. A. Sisson
* School of Biotechnology and Biomolecular Sciences and School of Mathematics, University of New South Wales, Sydney, NSW 2052, Australia and School of Computing and Mathematics, University of Western Sydney, Penrith South DC, NSW 1797, Australia
1 Corresponding author: School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW 2052, Australia.
E-mail: m.tanaka{at}unsw.edu.au
Tuberculosis can be studied at the population level by genotyping strains of Mycobacterium tuberculosis isolated from patients. We use an approximate Bayesian computational method in combination with a stochastic model of tuberculosis transmission and mutation of a molecular marker to estimate the net transmission rate, the doubling time, and the reproductive value of the pathogen. This method is applied to a published data set from San Francisco of tuberculosis genotypes based on the marker IS6110. The mutation rate of this marker has previously been studied, and we use those estimates to form a prior distribution of mutation rates in the inference procedure. The posterior point estimates of the key parameters of interest for these data are as follows: net transmission rate, 0.69/year [95% credibility interval (C.I.) 0.38, 1.08]; doubling time, 1.08 years (95% C.I. 0.64, 1.82); and reproductive value 3.4 (95% C.I. 1.4, 79.7). These figures suggest a rapidly spreading epidemic, consistent with observations of the resurgence of tuberculosis in the United States in the 1980s and 1990s.
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