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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Main Authors:	Morelli, Marco J., Thébaud, Gaël, Chadœuf, Joël, King, Donald P., Haydon, Daniel T., Soubeyrand, Samuel
Format:	Online
Language:	English
Published:	Public Library of Science 2012
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3499255/

id	pubmed-3499255
recordtype	oai_dc
spelling	pubmed-34992552012-11-19 A Bayesian Inference Framework to Reconstruct Transmission Trees Using Epidemiological and Genetic Data Morelli, Marco J. Thébaud, Gaël Chadœuf, Joël King, Donald P. Haydon, Daniel T. Soubeyrand, Samuel Research Article 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. Public Library of Science 2012-11-15 /pmc/articles/PMC3499255/ /pubmed/23166481 http://dx.doi.org/10.1371/journal.pcbi.1002768 Text en © 2012 Morelli et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Morelli, Marco J. Thébaud, Gaël Chadœuf, Joël King, Donald P. Haydon, Daniel T. Soubeyrand, Samuel
spellingShingle	Morelli, Marco J. Thébaud, Gaël Chadœuf, Joël King, Donald P. Haydon, Daniel T. Soubeyrand, Samuel A Bayesian Inference Framework to Reconstruct Transmission Trees Using Epidemiological and Genetic Data
author_facet	Morelli, Marco J. Thébaud, Gaël Chadœuf, Joël King, Donald P. Haydon, Daniel T. Soubeyrand, Samuel
author_sort	Morelli, Marco J.
title	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_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_sort	bayesian inference framework to reconstruct transmission trees using epidemiological and genetic data
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.
publisher	Public Library of Science
publishDate	2012
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3499255/
_version_	1611924486356992000

A Bayesian Inference Framework to Reconstruct Transmission Trees Using Epidemiological and Genetic Data

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