Reconstructing transmission trees for communicable diseases using densely sampled genetic data

Worby, Colin J. and O'Neill, Philip D. and Kypraios, Theodore and Robotham, Julie V. and De Angelis, Daniela and Cartwright, Edward J.P. and Peacock, Sharon J. and Cooper, Ben S. (2016) Reconstructing transmission trees for communicable diseases using densely sampled genetic data. Annals of Applied Statistics, 10 (1). pp. 395-417. ISSN 1941-7330

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Abstract

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.

Item Type: Article
Keywords: Bayesian inference, Infectious disease, Epidemics, Outbreak investigation, Transmission routes
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Mathematical Sciences
Identification Number: 10.1214/15-AOAS898
Depositing User: O'neill, Philip
Date Deposited: 12 May 2017 08:33
Last Modified: 14 May 2017 04:06
URI: http://eprints.nottingham.ac.uk/id/eprint/42771

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