A Bayesian inference framework to reconstruct transmission trees using epidemiological and genetic data.
The accurate identification of the route of transmission taken by an infectious agent through a host population is critical to understanding its epidemiology and informing measures for its control. However, reconstruction of transmission routes during an epidemic is often an underdetermined problem:...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2012-01-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1002768 |
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| author | Marco J Morelli Gaël Thébaud Joël Chadœuf Donald P King Daniel T Haydon Samuel Soubeyrand |
| author_facet | Marco J Morelli Gaël Thébaud Joël Chadœuf Donald P King Daniel T Haydon Samuel Soubeyrand |
| author_sort | Marco J Morelli |
| collection | DOAJ |
| description | The accurate identification of the route of transmission taken by an infectious agent through a host population is critical to understanding its epidemiology and informing measures for its control. However, reconstruction of transmission routes during an epidemic is often an underdetermined problem: data about the location and timings of infections can be incomplete, inaccurate, and compatible with a large number of different transmission scenarios. For fast-evolving pathogens like RNA viruses, inference can be strengthened by using genetic data, nowadays easily and affordably generated. However, significant statistical challenges remain to be overcome in the full integration of these different data types if transmission trees are to be reliably estimated. We present here a framework leading to a bayesian inference scheme that combines genetic and epidemiological data, able to reconstruct most likely transmission patterns and infection dates. After testing our approach with simulated data, we apply the method to two UK epidemics of Foot-and-Mouth Disease Virus (FMDV): the 2007 outbreak, and a subset of the large 2001 epidemic. In the first case, we are able to confirm the role of a specific premise as the link between the two phases of the epidemics, while transmissions more densely clustered in space and time remain harder to resolve. When we consider data collected from the 2001 epidemic during a time of national emergency, our inference scheme robustly infers transmission chains, and uncovers the presence of undetected premises, thus providing a useful tool for epidemiological studies in real time. The generation of genetic data is becoming routine in epidemiological investigations, but the development of analytical tools maximizing the value of these data remains a priority. Our method, while applied here in the context of FMDV, is general and with slight modification can be used in any situation where both spatiotemporal and genetic data are available. |
| format | Article |
| id | doaj-art-deff33e7175e46c597a439e10038273e |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-deff33e7175e46c597a439e10038273e2025-08-20T02:34:09ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582012-01-01811e100276810.1371/journal.pcbi.1002768A Bayesian inference framework to reconstruct transmission trees using epidemiological and genetic data.Marco J MorelliGaël ThébaudJoël ChadœufDonald P KingDaniel T HaydonSamuel SoubeyrandThe accurate identification of the route of transmission taken by an infectious agent through a host population is critical to understanding its epidemiology and informing measures for its control. However, reconstruction of transmission routes during an epidemic is often an underdetermined problem: data about the location and timings of infections can be incomplete, inaccurate, and compatible with a large number of different transmission scenarios. For fast-evolving pathogens like RNA viruses, inference can be strengthened by using genetic data, nowadays easily and affordably generated. However, significant statistical challenges remain to be overcome in the full integration of these different data types if transmission trees are to be reliably estimated. We present here a framework leading to a bayesian inference scheme that combines genetic and epidemiological data, able to reconstruct most likely transmission patterns and infection dates. After testing our approach with simulated data, we apply the method to two UK epidemics of Foot-and-Mouth Disease Virus (FMDV): the 2007 outbreak, and a subset of the large 2001 epidemic. In the first case, we are able to confirm the role of a specific premise as the link between the two phases of the epidemics, while transmissions more densely clustered in space and time remain harder to resolve. When we consider data collected from the 2001 epidemic during a time of national emergency, our inference scheme robustly infers transmission chains, and uncovers the presence of undetected premises, thus providing a useful tool for epidemiological studies in real time. The generation of genetic data is becoming routine in epidemiological investigations, but the development of analytical tools maximizing the value of these data remains a priority. Our method, while applied here in the context of FMDV, is general and with slight modification can be used in any situation where both spatiotemporal and genetic data are available.https://doi.org/10.1371/journal.pcbi.1002768 |
| spellingShingle | Marco J Morelli Gaël Thébaud Joël Chadœuf Donald P King Daniel T Haydon Samuel Soubeyrand A Bayesian inference framework to reconstruct transmission trees using epidemiological and genetic data. PLoS Computational Biology |
| title | A Bayesian inference framework to reconstruct transmission trees using epidemiological and genetic data. |
| title_full | A Bayesian inference framework to reconstruct transmission trees using epidemiological and genetic data. |
| title_fullStr | A Bayesian inference framework to reconstruct transmission trees using epidemiological and genetic data. |
| title_full_unstemmed | A Bayesian inference framework to reconstruct transmission trees using epidemiological and genetic data. |
| title_short | A Bayesian inference framework to reconstruct transmission trees using epidemiological and genetic data. |
| title_sort | bayesian inference framework to reconstruct transmission trees using epidemiological and genetic data |
| url | https://doi.org/10.1371/journal.pcbi.1002768 |
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