How the Dynamics and Structure of Sexual Contact Networks Shape Pathogen Phylogenies
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{"title"=>"How the Dynamics and Structure of Sexual Contact Networks Shape Pathogen Phylogenies", "type"=>"journal", "authors"=>[{"first_name"=>"Katy", "last_name"=>"Robinson", "scopus_author_id"=>"57197296985"}, {"first_name"=>"Nick", "last_name"=>"Fyson", "scopus_author_id"=>"36625247900"}, {"first_name"=>"Ted", "last_name"=>"Cohen", "scopus_author_id"=>"7202415780"}, {"first_name"=>"Christophe", "last_name"=>"Fraser", "scopus_author_id"=>"35460815100"}, {"first_name"=>"Caroline", "last_name"=>"Colijn", "scopus_author_id"=>"16416871500"}], "year"=>2013, "source"=>"PLoS Computational Biology", "identifiers"=>{"doi"=>"10.1371/journal.pcbi.1003105", "issn"=>"1553734X", "sgr"=>"84879522621", "scopus"=>"2-s2.0-84879522621", "pui"=>"369208179", "pmid"=>"23818840", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)"}, "id"=>"6e473c61-c268-31a7-b527-ddae63d987cb", "abstract"=>"The characteristics of the host contact network over which a pathogen is transmitted affect both epidemic spread and the projected effectiveness of control strategies. Given the importance of understanding these contact networks, it is unfortunate that they are very difficult to measure directly. This challenge has led to an interest in methods to infer information about host contact networks from pathogen phylogenies, because in shaping a pathogen's opportunities for reproduction, contact networks also shape pathogen evolution. Host networks influence pathogen phylogenies both directly, through governing opportunities for evolution, and indirectly by changing the prevalence and incidence. Here, we aim to separate these two effects by comparing pathogen evolution on different host networks that share similar epidemic trajectories. This approach allows use to examine the direct effects of network structure on pathogen phylogenies, largely controlling for confounding differences arising from population dynamics. We find that networks with more heterogeneous degree distributions yield pathogen phylogenies with more variable cluster numbers, smaller mean cluster sizes, shorter mean branch lengths, and somewhat higher tree imbalance than networks with relatively homogeneous degree distributions. However, in particular for dynamic networks, we find that these direct effects are relatively modest. These findings suggest that the role of the epidemic trajectory, the dynamics of the network and the inherent variability of metrics such as cluster size must each be taken into account when trying to use pathogen phylogenies to understand characteristics about the underlying host contact network.", "link"=>"http://www.mendeley.com/research/dynamics-structure-sexual-contact-networks-shape-pathogen-phylogenies", "reader_count"=>91, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>6, "Researcher"=>24, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>30, "Student > Postgraduate"=>4, "Other"=>5, "Student > Master"=>6, "Student > Bachelor"=>4, "Lecturer"=>3, "Lecturer > Senior Lecturer"=>2, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>6, "Researcher"=>24, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>30, "Student > Postgraduate"=>4, "Other"=>5, "Student > Master"=>6, "Student > Bachelor"=>4, "Lecturer"=>3, "Lecturer > Senior Lecturer"=>2, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>9, "Agricultural and Biological Sciences"=>30, "Veterinary Science and Veterinary Medicine"=>1, "Computer Science"=>11, "Engineering"=>1, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>3, "Mathematics"=>13, "Medicine and Dentistry"=>16, "Physics and Astronomy"=>1, "Psychology"=>3, "Social Sciences"=>1, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>16}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>3}, "Mathematics"=>{"Mathematics"=>13}, "Unspecified"=>{"Unspecified"=>9}, "Environmental Science"=>{"Environmental Science"=>1}, "Engineering"=>{"Engineering"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>30}, "Computer Science"=>{"Computer Science"=>11}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>1}}, "reader_count_by_country"=>{"United States"=>7, "Rwanda"=>1, "United Kingdom"=>2, "Portugal"=>1}, "group_count"=>3}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1094658"], "description"=>"<p>A: Phylogenetic tree from the NATSAL network, corresponding to the pathogen prevalence in panels D and E (blue lines). B and C: tree derived from the pathogen spreading on an ER network, corresponding to the red lines in D and E respectively. The ER prevalence was varied by changing the transmission parameter. Sampling was done at time 260 weeks.</p>", "links"=>[], "tags"=>["Population biology", "epidemiology", "Infectious disease epidemiology", "Theoretical biology", "constructed", "pathogens", "spreading", "natsal", "er", "networks", "supporting"], "article_id"=>726432, "categories"=>["Biological Sciences"], "users"=>["Katy Robinson", "Nick Fyson", "Ted Cohen", "Christophe Fraser", "Caroline Colijn"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003105.g002", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Trees_constructed_from_pathogens_spreading_on_the_NATSAL_and_ER_networks_supporting_very_different_epidemic_trajectories_/726432", "title"=>"Trees constructed from pathogens spreading on the NATSAL and ER networks supporting very different epidemic trajectories.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-20 01:47:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/1094668"], "description"=>"<p>Clustering was done with a cut-off distance of 0.06 as in the results for homochronous sampling. The expected value of imbalance is 0.074 <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003105#pcbi.1003105-Heard2\" target=\"_blank\">[51]</a>, considerably less than the imbalance of the heterochronously sampled trees from both networks.</p>", "links"=>[], "tags"=>["Population biology", "epidemiology", "Infectious disease epidemiology", "Theoretical biology", "lengths", "imbalance", "heterochronous", "sampling"], "article_id"=>726441, "categories"=>["Biological Sciences"], "users"=>["Katy Robinson", "Nick Fyson", "Ted Cohen", "Christophe Fraser", "Caroline Colijn"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003105.g011", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Clustering_branch_lengths_and_imbalance_for_heterochronous_sampling_on_the_dynamic_network_with_/726441", "title"=>"Clustering, branch lengths and imbalance for heterochronous sampling on the dynamic network with .", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-20 01:47:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/1094665"], "description"=>"<p>Dashed line indicates the expected imbalance for trees of this size <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003105#pcbi.1003105-Heard2\" target=\"_blank\">[51]</a>. Prevalence and incidence over time in an ER network (blue) and NATSAL-based network (red) are shown in the bottom row for dynamic ER and NATSAL underlying contact networks. The number of lineages through time (LTT) in the trees for ER (solid) and NATSAL (dotted) is also shown. The LTT plots show the LTT for all trees; mean LTT at each time are indicated with dotted and solid lines and the coloured regions range from the minimum to the maximum. Distributions were close to uniform over this range. The ranges almost entirely overlap.</p>", "links"=>[], "tags"=>["Population biology", "epidemiology", "Infectious disease epidemiology", "Theoretical biology", "imbalance", "pathogen", "duration", "infectiousness", "taken", "simulations", "incidence", "prevalence", "matched"], "article_id"=>726439, "categories"=>["Biological Sciences"], "users"=>["Katy Robinson", "Nick Fyson", "Ted Cohen", "Christophe Fraser", "Caroline Colijn"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003105.g009", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cluster_count_branch_length_and_imbalance_top_row_for_a_pathogen_with_duration_of_infectiousness_d_8202_8202_40_taken_from_simulations_in_which_incidence_and_prevalence_were_as_closely_matched_as_possible_/726439", "title"=>"Cluster count, branch length and imbalance (top row) for a pathogen with duration of infectiousness d = 40, taken from simulations in which incidence and prevalence were as closely matched as possible.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-20 01:47:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/1094667"], "description"=>"<p><b>) and static.</b></p>", "links"=>[], "tags"=>["Population biology", "epidemiology", "Infectious disease epidemiology", "Theoretical biology", "leaf-to-leaf", "scaled", "matched", "prevalence", "unmatched"], "article_id"=>726440, "categories"=>["Biological Sciences"], "users"=>["Katy Robinson", "Nick Fyson", "Ted Cohen", "Christophe Fraser", "Caroline Colijn"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003105.g010", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mean_leaf_to_leaf_distance_scaled_to_the_total_distance_in_each_tree_for_the_matched_prevalence_scenario_unmatched_dynamic_/726440", "title"=>"Mean leaf-to-leaf distance scaled to the total distance in each tree, for the matched prevalence scenario, unmatched dynamic (", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-20 01:47:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/1094664"], "description"=>"<p>Dashed lines indicate the expected imbalance for trees of this size <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003105#pcbi.1003105-Heard2\" target=\"_blank\">[51]</a>.</p>", "links"=>[], "tags"=>["Population biology", "epidemiology", "Infectious disease epidemiology", "Theoretical biology", "imbalance", "phylogenetic", "trees", "scenarios", "duration", "40", "static"], "article_id"=>726438, "categories"=>["Biological Sciences"], "users"=>["Katy Robinson", "Nick Fyson", "Ted Cohen", "Christophe Fraser", "Caroline Colijn"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003105.g008", "stats"=>{"downloads"=>1, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Tree_imbalance_in_phylogenetic_trees_from_the_scenarios_left_dynamic_with_duration_40_weeks_right_static_networks_/726438", "title"=>"Tree imbalance in phylogenetic trees from the scenarios (left) dynamic with duration 40 weeks; (right) static networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-20 01:47:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1094661"], "description"=>"<p>Note that the NATSAL network admits similar epidemic trajectories with markedly different degree distributions (A–C). Panels D, E show the cumulative distributions of the cluster sizes in the ER (red) and NATSAL (blue) networks, and illustrate that these do not parallel the degree distribution; NATSAL networks do not have particularly more variable cluster sizes within trees. Panels F and G show the variance and skewness in boxplots; each box represents all trees from the given network and time point as in other figures.</p>", "links"=>[], "tags"=>["Population biology", "epidemiology", "Infectious disease epidemiology", "Theoretical biology", "incidence", "1-cumulative", "distributions", "er", "natsal", "networks"], "article_id"=>726435, "categories"=>["Biological Sciences"], "users"=>["Katy Robinson", "Nick Fyson", "Ted Cohen", "Christophe Fraser", "Caroline Colijn"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003105.g005", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Network_prevalence_incidence_and_1_cumulative_degree_distributions_for_ER_red_and_NATSAL_blue_dynamic_networks_with_/726435", "title"=>"Network prevalence, incidence and 1-cumulative degree distributions for ER (red) and NATSAL (blue) dynamic networks with .", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-20 01:47:15"}
  • {"files"=>["https://ndownloader.figshare.com/files/1094660"], "description"=>"<p>The NATSAL trees displays early divergence compared to the ER trees, and this affects the number of clusters. Panel A shows different epidemic trajectories and their corresponding trees, B shows more similar trajectories, and C shows closely matched epidemics. The tree differences are most modest in panel C where the pathogen population dynamics are closely matched. Edges in each cluster are drawn with the same colour. The threshold value for clustering was 0.1.</p>", "links"=>[], "tags"=>["Population biology", "epidemiology", "Infectious disease epidemiology", "Theoretical biology", "trees", "derived", "er-like", "networks", "natsal", "illustrating", "pathogen", "prevalence"], "article_id"=>726434, "categories"=>["Biological Sciences"], "users"=>["Katy Robinson", "Nick Fyson", "Ted Cohen", "Christophe Fraser", "Caroline Colijn"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003105.g004", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_typical_trees_derived_from_ER_like_networks_top_row_and_NATSAL_networks_middle_row_illustrating_that_pathogen_prevalence_bottom_row_as_well_as_networks_both_influence_trees_/726434", "title"=>"Comparison of typical trees derived from ER-like networks (top row) and NATSAL networks (middle row) illustrating that pathogen prevalence (bottom row) as well as networks both influence trees.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-20 01:47:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/1094670"], "description"=>"<div><p>The characteristics of the host contact network over which a pathogen is transmitted affect both epidemic spread and the projected effectiveness of control strategies. Given the importance of understanding these contact networks, it is unfortunate that they are very difficult to measure directly. This challenge has led to an interest in methods to infer information about host contact networks from pathogen phylogenies, because in shaping a pathogen's opportunities for reproduction, contact networks also shape pathogen evolution. Host networks influence pathogen phylogenies both directly, through governing opportunities for evolution, and indirectly by changing the prevalence and incidence. Here, we aim to separate these two effects by comparing pathogen evolution on different host networks that share similar epidemic trajectories. This approach allows use to examine the direct effects of network structure on pathogen phylogenies, largely controlling for confounding differences arising from population dynamics. We find that networks with more heterogeneous degree distributions yield pathogen phylogenies with more variable cluster numbers, smaller mean cluster sizes, shorter mean branch lengths, and somewhat higher tree imbalance than networks with relatively homogeneous degree distributions. However, in particular for dynamic networks, we find that these direct effects are relatively modest. These findings suggest that the role of the epidemic trajectory, the dynamics of the network and the inherent variability of metrics such as cluster size must each be taken into account when trying to use pathogen phylogenies to understand characteristics about the underlying host contact network.</p></div>", "links"=>[], "tags"=>["Population biology", "epidemiology", "Infectious disease epidemiology", "Theoretical biology", "networks", "pathogen"], "article_id"=>726442, "categories"=>["Biological Sciences"], "users"=>["Katy Robinson", "Nick Fyson", "Ted Cohen", "Christophe Fraser", "Caroline Colijn"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003105", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_How_the_Dynamics_and_Structure_of_Sexual_Contact_Networks_Shape_Pathogen_Phylogenies_/726442", "title"=>"How the Dynamics and Structure of Sexual Contact Networks Shape Pathogen Phylogenies", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-06-20 01:47:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/1094663"], "description"=>"<p>Mean internal/external branch lengths for trees derived from epidemics on dynamic (d = 40 weeks) and static networks.</p>", "links"=>[], "tags"=>["Population biology", "epidemiology", "Infectious disease epidemiology", "Theoretical biology", "lengths", "trees", "derived", "epidemics", "static"], "article_id"=>726437, "categories"=>["Biological Sciences"], "users"=>["Katy Robinson", "Nick Fyson", "Ted Cohen", "Christophe Fraser", "Caroline Colijn"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003105.g007", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mean_internal_external_branch_lengths_for_trees_derived_from_epidemics_on_dynamic_d_8202_8202_40_weeks_and_static_networks_/726437", "title"=>"Mean internal/external branch lengths for trees derived from epidemics on dynamic (d = 40 weeks) and static networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-20 01:47:17"}
  • {"files"=>["https://ndownloader.figshare.com/files/1094662"], "description"=>"<p>A: mean branch lengths in trees from dynamic networks with a pathogen with duration of infectiousness 40 weeks; B mean branch lengths in static networks with duration of infectiousness 40 weeks. C, D: ratio of mean branch length to total tree distance, from dynamic and static networks.</p>", "links"=>[], "tags"=>["Population biology", "epidemiology", "Infectious disease epidemiology", "Theoretical biology", "lengths", "phylogenetic", "trees"], "article_id"=>726436, "categories"=>["Biological Sciences"], "users"=>["Katy Robinson", "Nick Fyson", "Ted Cohen", "Christophe Fraser", "Caroline Colijn"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003105.g006", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Branch_lengths_in_phylogenetic_trees_from_the_scenarios_/726436", "title"=>"Branch lengths in phylogenetic trees from the scenarios.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-20 01:47:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/1094659"], "description"=>"<p>A: cluster numbers (dynamic with duration 40 weeks); B cluster numbers (static network); C cluster sizes (dynamic with duration 40 weeks); D cluster sizes (static network).</p>", "links"=>[], "tags"=>["Population biology", "epidemiology", "Infectious disease epidemiology", "Theoretical biology", "sizes", "clusters", "samples", "phylogenetic", "trees"], "article_id"=>726433, "categories"=>["Biological Sciences"], "users"=>["Katy Robinson", "Nick Fyson", "Ted Cohen", "Christophe Fraser", "Caroline Colijn"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003105.g003", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mean_number_and_mean_sizes_of_clusters_of_two_or_more_samples_in_phylogenetic_trees_from_the_scenarios_/726433", "title"=>"Mean number and mean sizes of clusters of two or more samples in phylogenetic trees from the scenarios.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-20 01:47:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/1094657"], "description"=>"<p>Route A: direct effect of the host contact network on the pathogen phylogeny. Route B: the host contact network changes the population dynamics, sometimes dramatically, and this in turn affects the pathogen phylogeny.</p>", "links"=>[], "tags"=>["Population biology", "epidemiology", "Infectious disease epidemiology", "Theoretical biology", "illustrating", "dependence", "pathogen", "phylogenies"], "article_id"=>726431, "categories"=>["Biological Sciences"], "users"=>["Katy Robinson", "Nick Fyson", "Ted Cohen", "Christophe Fraser", "Caroline Colijn"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003105.g001", "stats"=>{"downloads"=>2, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_illustrating_the_dependence_of_pathogen_phylogenies_on_both_the_host_contact_network_and_the_pathogen_s_population_dynamics_/726431", "title"=>"Schematic illustrating the dependence of pathogen phylogenies on both the host contact network and the pathogen's population dynamics.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-20 01:47:11"}

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Relative Metric

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