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 w...
| Main Authors: | , , , , , , , |
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| Format: | Journal article |
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Institute of Mathematical Statistics (IMS)
2015
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| _version_ | 1826284863863390208 |
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| author | Worby, C O'Neill, P Kypraios, T Robotham, J De Angelis, D Cartwright, E Peacock, S Cooper, B |
| author_facet | Worby, C O'Neill, P Kypraios, T Robotham, J De Angelis, D Cartwright, E Peacock, S Cooper, B |
| author_sort | Worby, C |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T01:20:14Z |
| format | Journal article |
| id | oxford-uuid:900ef063-e2bd-43d7-ab06-4db4b3294fe6 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T01:20:14Z |
| publishDate | 2015 |
| publisher | Institute of Mathematical Statistics (IMS) |
| record_format | dspace |
| spelling | oxford-uuid:900ef063-e2bd-43d7-ab06-4db4b3294fe62022-03-26T23:08:59ZReconstructing transmission trees for communicable diseases using densely sampled genetic dataJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:900ef063-e2bd-43d7-ab06-4db4b3294fe6Symplectic Elements at OxfordInstitute of Mathematical Statistics (IMS)2015Worby, CO'Neill, PKypraios, TRobotham, JDe Angelis, DCartwright, EPeacock, SCooper, BWhole 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. |
| spellingShingle | Worby, C O'Neill, P Kypraios, T Robotham, J De Angelis, D Cartwright, E Peacock, S Cooper, B Reconstructing transmission trees for communicable diseases using densely sampled genetic data |
| title | Reconstructing transmission trees for communicable diseases using densely sampled genetic data |
| title_full | Reconstructing transmission trees for communicable diseases using densely sampled genetic data |
| title_fullStr | Reconstructing transmission trees for communicable diseases using densely sampled genetic data |
| title_full_unstemmed | Reconstructing transmission trees for communicable diseases using densely sampled genetic data |
| title_short | Reconstructing transmission trees for communicable diseases using densely sampled genetic data |
| title_sort | reconstructing transmission trees for communicable diseases using densely sampled genetic data |
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