Exact vs. Approximate Computation: Reconciling Different Estimates of Mycobacterium tuberculosis Epidemiological Parameters

Exact computational methods for inference in population genetics are intuitively preferable to approximate analyses. We reconcile two starkly different estimates of the reproductive number of tuberculosis from previous studies that used the same genotyping data and underlying model. This demonstrates the value of approximate analyses in validating exact methods.

[1]  G. Schoolnik,et al.  The epidemiology of tuberculosis in San Francisco. A population-based study using conventional and molecular methods. , 1994, The New England journal of medicine.

[2]  R. Plevin,et al.  Approximate Bayesian Computation in Evolution and Ecology , 2011 .

[3]  Charles J. Geyer,et al.  Importance Sampling, Simulated Tempering, and Umbrella Sampling , 2011 .

[4]  Andrew R. Francis,et al.  The epidemiological fitness cost of drug resistance in Mycobacterium tuberculosis , 2009, Proceedings of the National Academy of Sciences.

[5]  Sergei L. Kosakovsky Pond,et al.  Phylodynamics of Infectious Disease Epidemics , 2009, Genetics.

[6]  C. Dye,et al.  Will tuberculosis become resistant to all antibiotics? , 2001, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[7]  Andrew R. Francis,et al.  Using Approximate Bayesian Computation to Estimate Tuberculosis Transmission Parameters From Genotype Data , 2006, Genetics.

[8]  Paul Marjoram,et al.  Markov chain Monte Carlo without likelihoods , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[9]  O. François,et al.  Approximate Bayesian Computation (ABC) in practice. , 2010, Trends in ecology & evolution.

[10]  S. Sisson,et al.  Likelihood-free Markov chain Monte Carlo , 2010, 1001.2058.

[11]  Huldrych F. Günthard,et al.  Using an Epidemiological Model for Phylogenetic Inference Reveals Density Dependence in HIV Transmission , 2013, Molecular biology and evolution.

[12]  David A. Rasmussen,et al.  Inference for Nonlinear Epidemiological Models Using Genealogies and Time Series , 2011, PLoS Comput. Biol..

[13]  Ted Cohen,et al.  Modeling epidemics of multidrug-resistant M. tuberculosis of heterogeneous fitness , 2004, Nature Medicine.

[14]  T. Stadler.  Inferring Epidemiological Parameters on the Basis of Allele Frequencies , 2011, Genetics.