Unifying Viral Genetics and Human Transportation Data to Predict the Global Transmission Dynamics of Human Influenza H3N2
Publication Date
February 20, 2014
Journal
PLOS Pathogens
Authors
Philippe Lemey, Andrew Rambaut, Trevor Bedford, Nuno Faria, et al
Volume
10
Issue
2
Pages
e1003932
DOI
http://doi.org/10.1371/journal.ppat.1003932
Publisher URL
http://journals.plos.org/plospathogens/article?id=10.1371%2Fjournal.ppat.1003932
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/24586153
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3930559
Europe PMC
http://europepmc.org/abstract/MED/24586153
Web of Science
000332085900036
Scopus
84895733259
Mendeley
http://www.mendeley.com/research/unifying-viral-genetics-human-transportation-data-predict-global-transmission-dynamics-human-influen
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{"title"=>"Unifying Viral Genetics and Human Transportation Data to Predict the Global Transmission Dynamics of Human Influenza H3N2", "type"=>"journal", "authors"=>[{"first_name"=>"Philippe", "last_name"=>"Lemey", "scopus_author_id"=>"6602704841"}, {"first_name"=>"Andrew", "last_name"=>"Rambaut", "scopus_author_id"=>"7004230842"}, {"first_name"=>"Trevor", "last_name"=>"Bedford", "scopus_author_id"=>"7003593301"}, {"first_name"=>"Nuno", "last_name"=>"Faria", "scopus_author_id"=>"36764482900"}, {"first_name"=>"Filip", "last_name"=>"Bielejec", "scopus_author_id"=>"53163103400"}, {"first_name"=>"Guy", "last_name"=>"Baele", "scopus_author_id"=>"14027824900"}, {"first_name"=>"Colin A.", "last_name"=>"Russell", "scopus_author_id"=>"35278898300"}, {"first_name"=>"Derek J.", "last_name"=>"Smith", "scopus_author_id"=>"16231558100"}, {"first_name"=>"Oliver G.", "last_name"=>"Pybus", "scopus_author_id"=>"6701390795"}, {"first_name"=>"Dirk", "last_name"=>"Brockmann", "scopus_author_id"=>"7004068729"}, {"first_name"=>"Marc A.", "last_name"=>"Suchard", "scopus_author_id"=>"6603887291"}], "year"=>2014, "source"=>"PLoS Pathogens", "identifiers"=>{"issn"=>"15537374", "scopus"=>"2-s2.0-84895733259", "pui"=>"372548644", "doi"=>"10.1371/journal.ppat.1003932", "isbn"=>"1553-7374 (Electronic)\\r1553-7366 (Linking)", "sgr"=>"84895733259", "pmid"=>"24586153"}, "id"=>"dd7490f4-eb17-3a23-8133-dd3b4c3c0373", "abstract"=>"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.", "link"=>"http://www.mendeley.com/research/unifying-viral-genetics-human-transportation-data-predict-global-transmission-dynamics-human-influen", "reader_count"=>209, "reader_count_by_academic_status"=>{"Unspecified"=>4, "Professor > Associate Professor"=>8, "Researcher"=>43, "Student > Doctoral Student"=>15, "Student > Ph. D. Student"=>68, "Student > Postgraduate"=>6, "Student > Master"=>35, "Other"=>4, "Student > Bachelor"=>16, "Lecturer > Senior Lecturer"=>2, "Professor"=>8}, "reader_count_by_user_role"=>{"Unspecified"=>4, "Professor > Associate Professor"=>8, "Researcher"=>43, "Student > Doctoral Student"=>15, "Student > Ph. D. 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Figshare 11102 Apr 20:04 UTC

Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1392622"], "description"=>"<p>Maximum clade credibility (MCC) tree colored according to the time spent in the air communities as inferred by the GLM diffusion model. The tree represents one of the three different sub-sampled data sets discretized according to the 14 air communities. Branches are colored according the Markov reward estimates for each location. The uncertainty of these estimates is represented by superimposing an additional gray color proportional to the Shannon entropy of the Markov reward values. The trunk lineage in the tree is represented by the thick upper branch path from the root to the nodes that represent the ancestors of samples that are exclusively from December 2006. The total time spent in each location (in years) along the trunk between 2002 and 2006 is plotted on the left of the tree. The trunk reward proportion for each location through time between 2002 and 2006 is summarized at the top of the tree. Both the total trunk time and the trunk reward proportions through time are averaged over the three sub-sampled data sets. In the trunk proportion through time plot, the number of Southeast Asian and Chinese samples are represented by a white full and dashed line respectively (secondary Y-axis).</p>", "links"=>[], "tags"=>["Computational biology", "microbiology", "Virology", "Viral evolution", "Population biology", "epidemiology", "Infectious disease epidemiology", "Spatial epidemiology", "Molecular epidemiology", "reconstruction", "spatial"], "article_id"=>939837, "categories"=>["Biological Sciences", "Medicine"], "users"=>["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"], "doi"=>"https://dx.doi.org/10.1371/journal.ppat.1003932.g003", "stats"=>{"downloads"=>1, "page_views"=>22, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Phylogeographic_reconstruction_and_spatial_history_of_the_trunk_lineage_/939837", "title"=>"Phylogeographic reconstruction and spatial history of the trunk lineage.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-20 03:12:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/1392623"], "description"=>"<p>The simulations were performed using (A) an equal rate matrix, (B) a matrix of airline passengers flows, (C) standard phylogeographic estimates and (D) GLM phylogeographic estimates only considering air travel as a predictor. Spearman rank correlations () and mean absolute error (MAE; in days) considering all locations except for Mexico are provided for each comparison. The data points are colored according to the air communities represented in <a href=\"http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003932#ppat-1003932-g001\" target=\"_blank\">Fig. 1</a>. The dotted lines represent a 1-to-1 correspondence between observed peaks and simulated H1N1 peaks.</p>", "links"=>[], "tags"=>["Computational biology", "microbiology", "Virology", "Viral evolution", "Population biology", "epidemiology", "Infectious disease epidemiology", "Spatial epidemiology", "Molecular epidemiology", "observed", "h1n1", "peaks", "simulated"], "article_id"=>939838, "categories"=>["Biological Sciences", "Medicine"], "users"=>["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"], "doi"=>"https://dx.doi.org/10.1371/journal.ppat.1003932.g004", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_among_observed_H1N1_peaks_and_simulated_peaks_based_on_different_migration_rate_models_/939838", "title"=>"Correlation among observed H1N1 peaks and simulated peaks based on different migration rate models.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-20 03:12:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/1392629", "https://ndownloader.figshare.com/files/1392630", "https://ndownloader.figshare.com/files/1392631", "https://ndownloader.figshare.com/files/1392632", "https://ndownloader.figshare.com/files/1392633", "https://ndownloader.figshare.com/files/1392634", "https://ndownloader.figshare.com/files/1392635"], "description"=>"<div><p>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.</p></div>", "links"=>[], "tags"=>["Computational biology", "microbiology", "Virology", "Viral evolution", "Population biology", "epidemiology", "Infectious disease epidemiology", "Spatial epidemiology", "Molecular epidemiology", "viral", "genetics", "influenza"], "article_id"=>939844, "categories"=>["Biological Sciences", "Medicine"], "users"=>["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"], "doi"=>["https://dx.doi.org/10.1371/journal.ppat.1003932.s001", "https://dx.doi.org/10.1371/journal.ppat.1003932.s002", "https://dx.doi.org/10.1371/journal.ppat.1003932.s003", "https://dx.doi.org/10.1371/journal.ppat.1003932.s004", "https://dx.doi.org/10.1371/journal.ppat.1003932.s005", "https://dx.doi.org/10.1371/journal.ppat.1003932.s006", "https://dx.doi.org/10.1371/journal.ppat.1003932.s007"], "stats"=>{"downloads"=>10, "page_views"=>21, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Unifying_Viral_Genetics_and_Human_Transportation_Data_to_Predict_the_Global_Transmission_Dynamics_of_Human_Influenza_H3N2_/939844", "title"=>"Unifying Viral Genetics and Human Transportation Data to Predict the Global Transmission Dynamics of Human Influenza H3N2", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-02-20 03:12:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/1392619"], "description"=>"<p>The inclusion probabilities are defined by the indicator expectations because they reflect the frequency at which the predictor is included in the model and therefore represent the support for the predictor. Indicator expectations corresponding to Bayes factor support values of 10 and 100 are represented by a thin and thick vertical line respectively in these bar plots. The contribution of each predictor, when included in the model (), where is the coefficient or effect size, is represented by the mean and credible intervals of the GLM coefficients on a log scale. NA<sup>1</sup>: no conditional effect size available because the predictor was never included in the model. We tested different population size and density measures, different incidence-based measures and different seasonal measures (<a href=\"http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003932#ppat.1003932.s007\" target=\"_blank\">Text S1</a>), but only list the estimates for a representative predictor for the sake of clarity. The estimates for the full set of predictors are summarized for each sub-sampled data set in <a href=\"http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003932#ppat.1003932.s006\" target=\"_blank\">Fig. S5</a>. NA<sup>2</sup>: no indicator expectation or conditional effect size available because the predictor was not available for this discretization of the sequence data.</p>", "links"=>[], "tags"=>["Computational biology", "microbiology", "Virology", "Viral evolution", "Population biology", "epidemiology", "Infectious disease epidemiology", "Spatial epidemiology", "Molecular epidemiology", "h3n2", "diffusion", "14", "communities", "15", "26", "geographic"], "article_id"=>939834, "categories"=>["Biological Sciences", "Medicine"], "users"=>["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"], "doi"=>"https://dx.doi.org/10.1371/journal.ppat.1003932.g002", "stats"=>{"downloads"=>4, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predictors_of_global_H3N2_diffusion_among_the_14_air_communities_and_the_15_amp_26_geographic_locations_/939834", "title"=>"Predictors of global H3N2 diffusion among the 14 air communities and the 15 & 26 geographic locations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-20 03:12:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/1392616"], "description"=>"<p>The colored dots represent the airports in each community for which passenger flux data was used in the analysis. The areas with corresponding colors represent the geographical area within the communities for which H3N2 sequence samples were available. The 14 communities and associated data are listed in <a href=\"http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003932#ppat.1003932.s007\" target=\"_blank\">Text S1</a>.</p>", "links"=>[], "tags"=>["Computational biology", "microbiology", "Virology", "Viral evolution", "Population biology", "epidemiology", "Infectious disease epidemiology", "Spatial epidemiology", "Molecular epidemiology", "communities", "modularity", "maximization"], "article_id"=>939831, "categories"=>["Biological Sciences", "Medicine"], "users"=>["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"], "doi"=>"https://dx.doi.org/10.1371/journal.ppat.1003932.g001", "stats"=>{"downloads"=>0, "page_views"=>23, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_14_global_air_communities_identified_through_a_modularity_maximization_analyses_of_air_transportation_data_/939831", "title"=>"14 global air communities identified through a modularity maximization analyses of air transportation data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-20 03:12:29"}
PMC Usage Stats 121717 Apr 17:54 UTC

PMC Usage Stats | Further Information

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Relative Metric 380405 Apr 23:49 UTC

Relative Metric

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