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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2019
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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 |
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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 |
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