Cargando…

Inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases

Pathogen sequence data have been exploited to infer who infected whom, by using empirical and model-based approaches. Most of these approaches exploit one pathogen sequence per infected host (e.g. individual, household, field). However, modern sequencing techniques can reveal the polymorphic nature...

Descripción completa

Detalles Bibliográficos
Autores principales: Alamil, M., Hughes, J., Berthier, K., Desbiez, C., Thébaud, G., Soubeyrand, S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6553606/
https://www.ncbi.nlm.nih.gov/pubmed/31056055
http://dx.doi.org/10.1098/rstb.2018.0258
_version_ 1783424843329306624
author Alamil, M.
Hughes, J.
Berthier, K.
Desbiez, C.
Thébaud, G.
Soubeyrand, S.
author_facet Alamil, M.
Hughes, J.
Berthier, K.
Desbiez, C.
Thébaud, G.
Soubeyrand, S.
author_sort Alamil, M.
collection PubMed
description Pathogen sequence data have been exploited to infer who infected whom, by using empirical and model-based approaches. Most of these approaches exploit one pathogen sequence per infected host (e.g. individual, household, field). However, modern sequencing techniques can reveal the polymorphic nature of within-host populations of pathogens. Thus, these techniques provide a subsample of the pathogen variants that were present in the host at the sampling time. Such data are expected to give more insight on epidemiological links than a single sequence per host. In general, a mechanistic viewpoint to transmission and micro-evolution has been followed to infer epidemiological links from these data. Here, we investigate an alternative approach grounded on statistical learning. The idea consists of learning the structure of epidemiological links with a pseudo-evolutionary model applied to training data obtained from contact tracing, for example, and using this initial stage to infer links for the whole dataset. Such an approach has the potential to be particularly valuable in the case of a risk of erroneous mechanistic assumptions, it is sufficiently parsimonious to allow the handling of big datasets in the future, and it is versatile enough to be applied to very different contexts from animal, human and plant epidemiology. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’. This issue is linked with the subsequent theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’.
format Online
Article
Text
id pubmed-6553606
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher The Royal Society
record_format MEDLINE/PubMed
spelling pubmed-65536062019-06-19 Inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases Alamil, M. Hughes, J. Berthier, K. Desbiez, C. Thébaud, G. Soubeyrand, S. Philos Trans R Soc Lond B Biol Sci Articles Pathogen sequence data have been exploited to infer who infected whom, by using empirical and model-based approaches. Most of these approaches exploit one pathogen sequence per infected host (e.g. individual, household, field). However, modern sequencing techniques can reveal the polymorphic nature of within-host populations of pathogens. Thus, these techniques provide a subsample of the pathogen variants that were present in the host at the sampling time. Such data are expected to give more insight on epidemiological links than a single sequence per host. In general, a mechanistic viewpoint to transmission and micro-evolution has been followed to infer epidemiological links from these data. Here, we investigate an alternative approach grounded on statistical learning. The idea consists of learning the structure of epidemiological links with a pseudo-evolutionary model applied to training data obtained from contact tracing, for example, and using this initial stage to infer links for the whole dataset. Such an approach has the potential to be particularly valuable in the case of a risk of erroneous mechanistic assumptions, it is sufficiently parsimonious to allow the handling of big datasets in the future, and it is versatile enough to be applied to very different contexts from animal, human and plant epidemiology. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’. This issue is linked with the subsequent theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. The Royal Society 2019-06-24 2019-05-06 /pmc/articles/PMC6553606/ /pubmed/31056055 http://dx.doi.org/10.1098/rstb.2018.0258 Text en © 2019 The Authors. http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
spellingShingle Articles
Alamil, M.
Hughes, J.
Berthier, K.
Desbiez, C.
Thébaud, G.
Soubeyrand, S.
Inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases
title Inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases
title_full Inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases
title_fullStr Inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases
title_full_unstemmed Inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases
title_short Inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases
title_sort inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6553606/
https://www.ncbi.nlm.nih.gov/pubmed/31056055
http://dx.doi.org/10.1098/rstb.2018.0258
work_keys_str_mv AT alamilm inferringepidemiologicallinksfromdeepsequencingdataastatisticallearningapproachforhumananimalandplantdiseases
AT hughesj inferringepidemiologicallinksfromdeepsequencingdataastatisticallearningapproachforhumananimalandplantdiseases
AT berthierk inferringepidemiologicallinksfromdeepsequencingdataastatisticallearningapproachforhumananimalandplantdiseases
AT desbiezc inferringepidemiologicallinksfromdeepsequencingdataastatisticallearningapproachforhumananimalandplantdiseases
AT thebaudg inferringepidemiologicallinksfromdeepsequencingdataastatisticallearningapproachforhumananimalandplantdiseases
AT soubeyrands inferringepidemiologicallinksfromdeepsequencingdataastatisticallearningapproachforhumananimalandplantdiseases