Open Access
March 2016 Reconstructing transmission trees for communicable diseases using densely sampled genetic data
Colin J. Worby, Philip D. O’Neill, Theodore Kypraios, Julie V. Robotham, Daniela De Angelis, Edward J. P. Cartwright, Sharon J. Peacock, Ben S. Cooper
Ann. Appl. Stat. 10(1): 395-417 (March 2016). DOI: 10.1214/15-AOAS898


Whole genome sequencing of pathogens from multiple hosts in an epidemic offers the potential to investigate who infected whom with unparalleled resolution, potentially yielding important insights into disease dynamics and the impact of control measures. We considered disease outbreaks in a setting with dense genomic sampling, and formulated stochastic epidemic models to investigate person-to-person transmission, based on observed genomic and epidemiological data. We constructed models in which the genetic distance between sampled genotypes depends on the epidemiological relationship between the hosts. A data-augmented Markov chain Monte Carlo algorithm was used to sample over the transmission trees, providing a posterior probability for any given transmission route. We investigated the predictive performance of our methodology using simulated data, demonstrating high sensitivity and specificity, particularly for rapidly mutating pathogens with low transmissibility. We then analyzed data collected during an outbreak of methicillin-resistant Staphylococcus aureus in a hospital, identifying probable transmission routes and estimating epidemiological parameters. Our approach overcomes limitations of previous methods, providing a framework with the flexibility to allow for unobserved infection times, multiple independent introductions of the pathogen and within-host genetic diversity, as well as allowing forward simulation.


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Colin J. Worby. Philip D. O’Neill. Theodore Kypraios. Julie V. Robotham. Daniela De Angelis. Edward J. P. Cartwright. Sharon J. Peacock. Ben S. Cooper. "Reconstructing transmission trees for communicable diseases using densely sampled genetic data." Ann. Appl. Stat. 10 (1) 395 - 417, March 2016.


Received: 1 July 2014; Revised: 1 November 2015; Published: March 2016
First available in Project Euclid: 25 March 2016

zbMATH: 1358.62110
MathSciNet: MR3480501
Digital Object Identifier: 10.1214/15-AOAS898

Keywords: Bayesian inference , epidemics , infectious disease , outbreak investigation , transmission routes

Rights: Copyright © 2016 Institute of Mathematical Statistics

Vol.10 • No. 1 • March 2016
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