Publication: Reconstructing transmission trees for communicable diseases using densely sampled genetic data
Issued Date
2016-03-01
Resource Type
ISSN
19417330
19326157
19326157
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2-s2.0-84975127301
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Mahidol University
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SCOPUS
Bibliographic Citation
Annals of Applied Statistics. Vol.10, No.1 (2016), 395-417
Suggested Citation
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. Annals of Applied Statistics. Vol.10, No.1 (2016), 395-417. doi:10.1214/15-AOAS898 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/43623
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Title
Reconstructing transmission trees for communicable diseases using densely sampled genetic data
Abstract
© Institute of Mathematical Statistics, 2016. 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 indepen-dent introductions of the pathogen and within-host genetic diversity, as well as allowing forward simulation.