Reconstructing transmission trees for communicable diseases using densely sampled genetic data
Worby, Colin J
O’Neill, Philip D
Robotham, Julie V
de, Angelis Daniela
Cartwright, Edward JP
Cooper, Ben S
Annals of Applied Statistics
Institute of Mathematical Statistics
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Worby, C. J., O’Neill, P. D., Kypraios, T., Robotham, J. V., de, A. D., Cartwright, E. J., Peacock, S., & et al. (2016). Reconstructing transmission trees for communicable diseases using densely sampled genetic data. Annals of Applied Statistics, 10 395-417. https://doi.org/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.
Bayesian inference, infectious disease, epidemics, outbreak investigation, transmission routes
Funding received from the following: The European Community [Mastering Hospital Antimicrobial Resistance (MOSAR) network contract LSHP-CT-2007-037941]. The National Institute of General Medical Sciences of the National Institutes of Health under award number U54GM088558. The UK Medical Research Council (Unit Programme number U105260566). The UKCRC Translational Infection Research Initiative (MRC Grant number G1000803) and Public Health England. The Medical Research Council and Department for International Development (Grant number MR/K006924/1). The Mahidol Oxford Tropical Medicine Research Unit is part of the Wellcome Trust Major Overseas Programme in SE Asia (Grant number 106698/Z/14/Z).
External DOI: https://doi.org/10.1214/15-AOAS898
This record's URL: https://www.repository.cam.ac.uk/handle/1810/254828