Unifying viral genetics and human transportation data to predict the global transmission dynamics of human influenza H3N2.

Information on global human movement patterns is central to spatial epidemiological models used to predict the behavior of influenza and other infectious diseases. Yet it remains difficult to test which modes of dispersal drive pathogen spread at various geographic scales using standard epidemiologi...

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Autores principales:	Philippe Lemey, Andrew Rambaut, Trevor Bedford, Nuno Faria, Filip Bielejec, Guy Baele, Colin A Russell, Derek J Smith, Oliver G Pybus, Dirk Brockmann, Marc A Suchard
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Immunologic diseases. Allergy RC581-607 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/b1ff4becd42c480cb05214b14b6d3fcf
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spelling	oai:doaj.org-article:b1ff4becd42c480cb05214b14b6d3fcf2021-11-18T06:06:58ZUnifying viral genetics and human transportation data to predict the global transmission dynamics of human influenza H3N2.1553-73661553-737410.1371/journal.ppat.1003932https://doaj.org/article/b1ff4becd42c480cb05214b14b6d3fcf2014-02-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24586153/?tool=EBIhttps://doaj.org/toc/1553-7366https://doaj.org/toc/1553-7374Information on global human movement patterns is central to spatial epidemiological models used to predict the behavior of influenza and other infectious diseases. Yet it remains difficult to test which modes of dispersal drive pathogen spread at various geographic scales using standard epidemiological data alone. Evolutionary analyses of pathogen genome sequences increasingly provide insights into the spatial dynamics of influenza viruses, but to date they have largely neglected the wealth of information on human mobility, mainly because no statistical framework exists within which viral gene sequences and empirical data on host movement can be combined. Here, we address this problem by applying a phylogeographic approach to elucidate the global spread of human influenza subtype H3N2 and assess its ability to predict the spatial spread of human influenza A viruses worldwide. Using a framework that estimates the migration history of human influenza while simultaneously testing and quantifying a range of potential predictive variables of spatial spread, we show that the global dynamics of influenza H3N2 are driven by air passenger flows, whereas at more local scales spread is also determined by processes that correlate with geographic distance. Our analyses further confirm a central role for mainland China and Southeast Asia in maintaining a source population for global influenza diversity. By comparing model output with the known pandemic expansion of H1N1 during 2009, we demonstrate that predictions of influenza spatial spread are most accurate when data on human mobility and viral evolution are integrated. In conclusion, the global dynamics of influenza viruses are best explained by combining human mobility data with the spatial information inherent in sampled viral genomes. The integrated approach introduced here offers great potential for epidemiological surveillance through phylogeographic reconstructions and for improving predictive models of disease control.Philippe LemeyAndrew RambautTrevor BedfordNuno FariaFilip BielejecGuy BaeleColin A RussellDerek J SmithOliver G PybusDirk BrockmannMarc A SuchardPublic Library of Science (PLoS)articleImmunologic diseases. AllergyRC581-607Biology (General)QH301-705.5ENPLoS Pathogens, Vol 10, Iss 2, p e1003932 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Immunologic diseases. Allergy RC581-607 Biology (General) QH301-705.5
spellingShingle	Immunologic diseases. Allergy RC581-607 Biology (General) QH301-705.5 Philippe Lemey Andrew Rambaut Trevor Bedford Nuno Faria Filip Bielejec Guy Baele Colin A Russell Derek J Smith Oliver G Pybus Dirk Brockmann Marc A Suchard Unifying viral genetics and human transportation data to predict the global transmission dynamics of human influenza H3N2.
description	Information on global human movement patterns is central to spatial epidemiological models used to predict the behavior of influenza and other infectious diseases. Yet it remains difficult to test which modes of dispersal drive pathogen spread at various geographic scales using standard epidemiological data alone. Evolutionary analyses of pathogen genome sequences increasingly provide insights into the spatial dynamics of influenza viruses, but to date they have largely neglected the wealth of information on human mobility, mainly because no statistical framework exists within which viral gene sequences and empirical data on host movement can be combined. Here, we address this problem by applying a phylogeographic approach to elucidate the global spread of human influenza subtype H3N2 and assess its ability to predict the spatial spread of human influenza A viruses worldwide. Using a framework that estimates the migration history of human influenza while simultaneously testing and quantifying a range of potential predictive variables of spatial spread, we show that the global dynamics of influenza H3N2 are driven by air passenger flows, whereas at more local scales spread is also determined by processes that correlate with geographic distance. Our analyses further confirm a central role for mainland China and Southeast Asia in maintaining a source population for global influenza diversity. By comparing model output with the known pandemic expansion of H1N1 during 2009, we demonstrate that predictions of influenza spatial spread are most accurate when data on human mobility and viral evolution are integrated. In conclusion, the global dynamics of influenza viruses are best explained by combining human mobility data with the spatial information inherent in sampled viral genomes. The integrated approach introduced here offers great potential for epidemiological surveillance through phylogeographic reconstructions and for improving predictive models of disease control.
format	article
author	Philippe Lemey Andrew Rambaut Trevor Bedford Nuno Faria Filip Bielejec Guy Baele Colin A Russell Derek J Smith Oliver G Pybus Dirk Brockmann Marc A Suchard
author_facet	Philippe Lemey Andrew Rambaut Trevor Bedford Nuno Faria Filip Bielejec Guy Baele Colin A Russell Derek J Smith Oliver G Pybus Dirk Brockmann Marc A Suchard
author_sort	Philippe Lemey
title	Unifying viral genetics and human transportation data to predict the global transmission dynamics of human influenza H3N2.
title_short	Unifying viral genetics and human transportation data to predict the global transmission dynamics of human influenza H3N2.
title_full	Unifying viral genetics and human transportation data to predict the global transmission dynamics of human influenza H3N2.
title_fullStr	Unifying viral genetics and human transportation data to predict the global transmission dynamics of human influenza H3N2.
title_full_unstemmed	Unifying viral genetics and human transportation data to predict the global transmission dynamics of human influenza H3N2.
title_sort	unifying viral genetics and human transportation data to predict the global transmission dynamics of human influenza h3n2.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/b1ff4becd42c480cb05214b14b6d3fcf
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Unifying viral genetics and human transportation data to predict the global transmission dynamics of human influenza H3N2.

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