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. and Soubeyrand, S. (2019) Inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases. Philosophical Transactions of the Royal Society B: Biological Sciences, 374(1775), 20180258. (PMID:31056055)

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Publisher's URL: https://royalsocietypublishing.org/doi/full/10.1098/rstb.2018.0258

Abstract

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'.

Item Type:Articles
Additional Information:This work was funded by an ANR grant (SMITID project; ANR-16-CE35-0006). J.H. is funded by the Medical Research Council (MC_UU_12014/12). Field and laboratory work for the plant virus was funded by the Division for Plant Health and Environment (SPE) of INRA through the AAP-SPE-2014 framework.
Keywords:Contact information, infectious disease, pathogen spread, training data, transmission trees, within-host pathogen diversity.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Hughes, Dr Joseph
Authors: Alamil, M., Hughes, J., Berthier, K., Desbiez, C., Thébaud, G., and Soubeyrand, S.
College/School:College of Medical Veterinary and Life Sciences > Institute of Infection Immunity and Inflammation
Journal Name:Philosophical Transactions of the Royal Society B: Biological Sciences
Publisher:The Royal Society
ISSN:0962-8436
ISSN (Online):1471-2970
Published Online:06 May 2019
Copyright Holders:Copyright © 2019 The Authors
First Published:First published in Philosophical Transactions of the Royal Society B: Biological Sciences 374(1775):20180258
Publisher Policy:Reproduced under a Creative Commons license

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