The Annals of Applied Statistics
- Ann. Appl. Stat.
- Volume 10, Number 1 (2016), 395-417.
Reconstructing transmission trees for communicable diseases using densely sampled genetic data
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.
Ann. Appl. Stat., Volume 10, Number 1 (2016), 395-417.
Received: July 2014
Revised: November 2015
First available in Project Euclid: 25 March 2016
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Worby, Colin J.; O’Neill, Philip D.; Kypraios, Theodore; Robotham, Julie V.; De Angelis, Daniela; Cartwright, Edward J. P.; Peacock, Sharon J.; Cooper, Ben S. Reconstructing transmission trees for communicable diseases using densely sampled genetic data. Ann. Appl. Stat. 10 (2016), no. 1, 395--417. doi:10.1214/15-AOAS898. https://projecteuclid.org/euclid.aoas/1458909921
- Appendix: Transmission tree sampling approach. Full description of the tree sampling approach, as well as supplementary figures.
- Code: Software. R package “bitrugs” (Bayesian Inference of Transmission Routes Using Genome Sequences), with implementation of data simulation and MCMC algorithm.