Reconstructing transmission trees for communicable diseases using densely sampled genetic data

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.

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.

Citation

Worby, C. J., O'Neill, P. D., Kypraios, T., Robotham, J. V., De Angelis, D., Cartwright, E. J., …Cooper, B. S. (2016). Reconstructing transmission trees for communicable diseases using densely sampled genetic data. Annals of Applied Statistics, 10(1), https://doi.org/10.1214/15-AOAS898

• Journal Article Type: Article
• Acceptance Date: Nov 25, 2015
• Publication Date: Mar 25, 2016
• Deposit Date: May 12, 2017
• Publicly Available Date: May 12, 2017
• Journal: Annals of Applied Statistics
• Print ISSN: 1932-6157
• Electronic ISSN: 1941-7330
• Publisher: Institute of Mathematical Statistics (IMS)
• Peer Reviewed: Peer Reviewed
• Volume: 10
• Issue: 1
• DOI: https://doi.org/10.1214/15-AOAS898
• Keywords: Bayesian inference, Infectious disease, Epidemics, Outbreak investigation, Transmission routes
• Public URL: https://nottingham-repository.worktribe.com/output/779173
• Publisher URL: http://projecteuclid.org/euclid.aoas/1458909921#info