期刊
ANNALS OF APPLIED STATISTICS
卷 10, 期 1, 页码 395-417出版社
INST MATHEMATICAL STATISTICS
DOI: 10.1214/15-AOAS898
关键词
Bayesian inference; infectious disease; epidemics; outbreak investigation; transmission routes
资金
- European Community [Mastering Hospital Antimicrobial Resistance (MOSAR) network] [LSHP-CT-2007-037941]
- National Institute of General Medical Sciences of the National Institutes of Health [U54GM088558]
- UK Medical Research Council [U105260566]
- UKCRC Translational Infection Research Initiative (MRC) [G1000803]
- Public Health England
- Medical Research Council
- Department for International Development [MR/K006924/1]
- Mahidol Oxford Tropical Medicine Research Unit is part of the Wellcome Trust Major Overseas Programme in SE Asia [106698/Z/14/Z]
- MRC [G1000803, MC_U105260556, MR/K006924/1] Funding Source: UKRI
- Medical Research Council [G1000803, MC_U105260556, MR/K006924/1] Funding Source: researchfish
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.
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