Simple Epidemiological Dynamics Explain Phylogenetic Clustering of HIV from Patients with Recent Infection
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{"title"=>"Simple epidemiological dynamics explain phylogenetic clustering of HIV from patients with recent infection", "type"=>"journal", "authors"=>[{"first_name"=>"Erik M.", "last_name"=>"Volz", "scopus_author_id"=>"23029242600"}, {"first_name"=>"James S.", "last_name"=>"Koopman", "scopus_author_id"=>"7101971636"}, {"first_name"=>"Melissa J.", "last_name"=>"Ward", "scopus_author_id"=>"35185692700"}, {"first_name"=>"Andrew Leigh", "last_name"=>"Brown", "scopus_author_id"=>"7003531603"}, {"first_name"=>"Simon D.W.", "last_name"=>"Frost", "scopus_author_id"=>"7102366418"}], "year"=>2012, "source"=>"PLoS Computational Biology", "identifiers"=>{"pmid"=>"22761556", "sgr"=>"84864075429", "doi"=>"10.1371/journal.pcbi.1002552", "scopus"=>"2-s2.0-84864075429", "pui"=>"365284155", "isbn"=>"1553-734x", "issn"=>"1553734X"}, "id"=>"a32a04d8-7676-385d-bfc9-0daa1ab6db37", "abstract"=>"Phylogenies of highly genetically variable viruses such as HIV-1 are potentially informative of epidemiological dynamics. Several studies have demonstrated the presence of clusters of highly related HIV-1 sequences, particularly among recently HIV-infected individuals, which have been used to argue for a high transmission rate during acute infection. Using a large set of HIV-1 subtype B pol sequences collected from men who have sex with men, we demonstrate that virus from recent infections tend to be phylogenetically clustered at a greater rate than virus from patients with chronic infection ('excess clustering') and also tend to cluster with other recent HIV infections rather than chronic, established infections ('excess co-clustering'), consistent with previous reports. To determine the role that a higher infectivity during acute infection may play in excess clustering and co-clustering, we developed a simple model of HIV infection that incorporates an early period of intensified transmission, and explicitly considers the dynamics of phylogenetic clusters alongside the dynamics of acute and chronic infected cases. We explored the potential for clustering statistics to be used for inference of acute stage transmission rates and found that no single statistic explains very much variance in parameters controlling acute stage transmission rates. We demonstrate that high transmission rates during the acute stage is not the main cause of excess clustering of virus from patients with early/acute infection compared to chronic infection, which may simply reflect the shorter time since transmission in acute infection. Higher transmission during acute infection can result in excess co-clustering of sequences, while the extent of clustering observed is most sensitive to the fraction of infections sampled.", "link"=>"http://www.mendeley.com/research/simple-epidemiological-dynamics-explain-phylogenetic-clustering-hiv-patients-recent-infection", "reader_count"=>72, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>6, "Researcher"=>19, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>19, "Student > Postgraduate"=>4, "Student > Master"=>3, "Other"=>3, "Student > Bachelor"=>5, "Lecturer"=>3, "Professor"=>5, "Unspecified"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>6, "Researcher"=>19, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>19, "Student > Postgraduate"=>4, "Student > Master"=>3, "Other"=>3, "Student > Bachelor"=>5, "Lecturer"=>3, "Professor"=>5, "Unspecified"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>9, "Environmental Science"=>1, "Nursing and Health Professions"=>1, "Biochemistry, Genetics and Molecular Biology"=>3, "Mathematics"=>4, "Agricultural and Biological Sciences"=>27, "Medicine and Dentistry"=>17, "Business, Management and Accounting"=>1, "Veterinary Science and Veterinary Medicine"=>1, "Physics and Astronomy"=>2, "Computer Science"=>4, "Immunology and Microbiology"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>17}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>27}, "Computer Science"=>{"Computer Science"=>4}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}, "Mathematics"=>{"Mathematics"=>4}, "Unspecified"=>{"Unspecified"=>9}, "Environmental Science"=>{"Environmental Science"=>1}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>1}}, "reader_count_by_country"=>{"Canada"=>1, "United States"=>5, "United Kingdom"=>4, "Italy"=>1, "Portugal"=>2}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/616814"], "description"=>"<p>The x-axis gives the time since the beginning of the epidemic, or equivalently, the threshold TMRCA used to calculate the number of lineages over time. Green describes the simulated number of late infections. Blue describes the simulated number of early infections. Dots show the simulated ancestor function for the number of lineages that correspond to late infections. And x's show the simulated ancestor function for lineages in early infection. Dashed lines show the prediction of the deterministic coalescent. The top row shows results for a sample taken at 15 years following the initial infections, and the bottom shows results for a sample at 30 years. The right column shows results for a sample fractions of 20%, and the left column for a census of prevalent infections(100%).</p>", "links"=>[], "tags"=>["simulated", "epidemics", "deterministic", "approximations", "prevalent", "infections", "functions", "lineages"], "article_id"=>287296, "categories"=>["Mathematics", "Genetics", "Biotechnology", "Evolutionary Biology"], "users"=>["Erik M. Volz", "James S. Koopman", "Melissa J. Ward", "Andrew Leigh Brown", "Simon D. W. Frost"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002552.g003", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Two_simulated_epidemics_and_the_deterministic_approximations_for_the_prevalent_number_of_early_and_late_infections_and_the_ancestor_functions_the_number_of_lineages_over_time_/287296", "title"=>"Two simulated epidemics and the deterministic approximations for the prevalent number of early and late infections and the ancestor functions (the number of lineages over time).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-06-28 02:01:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/616722"], "description"=>"<p>Left: The mean CD4 cell count (top) and frequency of ambiguous sites (bottom) versus the threshold TMRCA used to form clusters. Middle: The assortativity coefficient, a measure of similarity of co-clustered taxa, versus the treshold TMRCA used to form clusters. Assortativity of CD4 is at top, and frequency of ambiguous sites is bottom. Right: The size of each matrix element is proportional to number of co-clusterings between taxa categorized by CD4 (top, ) or quartile of frequency of ambiguous sites (bottom). The color represents the extent to which the count of co-clusterings exceeds the expectation if clusters were forming at random. The color scale (far right) shows strong assortativity within quartiles. The vertical red bar represents the threshold which was used to create clusters and the matrix derived from the set of clusters. This threshold corresponds to the maximum of the assortativity coefficient for the derived matrix.</p>", "links"=>[], "tags"=>["clustering", "excess", "co-clustering", "patients"], "article_id"=>287209, "categories"=>["Mathematics", "Genetics", "Biotechnology", "Evolutionary Biology"], "users"=>["Erik M. Volz", "James S. Koopman", "Melissa J. Ward", "Andrew Leigh Brown", "Simon D. W. Frost"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002552.g002", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Excess_clustering_and_excess_co_clustering_of_virus_from_patients_with_early_acute_infections_/287209", "title"=>"Excess clustering and excess co-clustering of virus from patients with early/acute infections.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-06-28 02:00:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/617002"], "description"=>"<p>Epidemiological parameters.</p>", "links"=>[], "tags"=>["genetics and genomics", "public health and epidemiology", "Evolutionary biology", "mathematics"], "article_id"=>287491, "categories"=>["Mathematics", "Genetics", "Biotechnology", "Evolutionary Biology"], "users"=>["Erik M. Volz", "James S. Koopman", "Melissa J. Ward", "Andrew Leigh Brown", "Simon D. W. Frost"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002552.t001", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Epidemiological_parameters_/287491", "title"=>"Epidemiological parameters.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-06-28 02:04:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/616933"], "description"=>"<p>The threshold TMRCA was five years before the most recent sample. Sample size and distribution of samples over time was matched to the Detroit MSM phylogeny.</p>", "links"=>[], "tags"=>["hiv", "genealogies", "transmissions", "attributable"], "article_id"=>287420, "categories"=>["Mathematics", "Genetics", "Biotechnology", "Evolutionary Biology"], "users"=>["Erik M. Volz", "James S. Koopman", "Melissa J. Ward", "Andrew Leigh Brown", "Simon D. W. Frost"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002552.g004", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_statistics_from_HIV_gene_genealogies_versus_the_fraction_of_transmissions_attributable_to_early_acute_infection_/287420", "title"=>"Summary statistics from HIV gene genealogies versus the fraction of transmissions attributable to early/acute infection.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-06-28 02:03:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/321089", "https://ndownloader.figshare.com/files/321095", "https://ndownloader.figshare.com/files/321110", "https://ndownloader.figshare.com/files/321123", "https://ndownloader.figshare.com/files/321149", "https://ndownloader.figshare.com/files/321173"], "description"=>"<div><p>Phylogenies of highly genetically variable viruses such as HIV-1 are potentially informative of epidemiological dynamics. Several studies have demonstrated the presence of clusters of highly related HIV-1 sequences, particularly among recently HIV-infected individuals, which have been used to argue for a high transmission rate during acute infection. Using a large set of HIV-1 subtype B pol sequences collected from men who have sex with men, we demonstrate that virus from recent infections tend to be phylogenetically clustered at a greater rate than virus from patients with chronic infection (‘excess clustering’) and also tend to cluster with other recent HIV infections rather than chronic, established infections (‘excess co-clustering’), consistent with previous reports. To determine the role that a higher infectivity during acute infection may play in excess clustering and co-clustering, we developed a simple model of HIV infection that incorporates an early period of intensified transmission, and explicitly considers the dynamics of phylogenetic clusters alongside the dynamics of acute and chronic infected cases. We explored the potential for clustering statistics to be used for inference of acute stage transmission rates and found that no single statistic explains very much variance in parameters controlling acute stage transmission rates. We demonstrate that high transmission rates during the acute stage is not the main cause of excess clustering of virus from patients with early/acute infection compared to chronic infection, which may simply reflect the shorter time since transmission in acute infection. Higher transmission during acute infection can result in excess co-clustering of sequences, while the extent of clustering observed is most sensitive to the fraction of infections sampled.</p> </div>", "links"=>[], "tags"=>["epidemiological", "phylogenetic", "clustering", "hiv", "patients"], "article_id"=>123346, "categories"=>["Mathematics", "Genetics", "Biotechnology", "Evolutionary Biology"], "users"=>["Erik M. Volz", "James S. Koopman", "Melissa J. Ward", "Andrew Leigh Brown", "Simon D. W. Frost"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002552.s001", "https://dx.doi.org/10.1371/journal.pcbi.1002552.s002", "https://dx.doi.org/10.1371/journal.pcbi.1002552.s003", "https://dx.doi.org/10.1371/journal.pcbi.1002552.s004", "https://dx.doi.org/10.1371/journal.pcbi.1002552.s005", "https://dx.doi.org/10.1371/journal.pcbi.1002552.s006"], "stats"=>{"downloads"=>15, "page_views"=>28, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Simple_Epidemiological_Dynamics_Explain_Phylogenetic_Clustering_of_HIV_from_Patients_with_Recent_Infection/123346", "title"=>"Simple Epidemiological Dynamics Explain Phylogenetic Clustering of HIV from Patients with Recent Infection", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-06-28 00:55:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/616664"], "description"=>"<p>Dark branches with taxa labeled <i>A</i> correspond to stage-1 (early/acute infected hosts). Light branches with taxa labeled <i>C</i> correspond to stage-2 (chronic infections). Event 1 represents the coalescence of two lineages corresponding to early/acute infection. Event 2 represents coalescence of an early and a late infection. Event 3 represents the stage transition of an early infection to a late infection. Event 4 represents the transmission by a late infection which is not ancestral to the sample. Top: Includes an unsampled lineage (dashed). Middle: The unsampled lineage has been pruned from the tree. The point where the lineage is pruned corresponds to event 4. Bottom: The number of lineages as a function of time (NLFT) which correspond to a host with early/acute infection (black) or chronic infection (grey).</p>", "links"=>[], "tags"=>["genealogy", "generated", "hiv"], "article_id"=>287154, "categories"=>["Mathematics", "Genetics", "Biotechnology", "Evolutionary Biology"], "users"=>["Erik M. Volz", "James S. Koopman", "Melissa J. Ward", "Andrew Leigh Brown", "Simon D. W. Frost"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002552.g001", "stats"=>{"downloads"=>3, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_simple_gene_genealogy_that_could_be_generated_by_the_HIV_model_/287154", "title"=>"A simple gene genealogy that could be generated by the HIV model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-06-28 01:59:14"}

PMC Usage Stats | Further Information

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Relative Metric

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