Immuno-epidemiological Modeling of HIV-1 Predicts High Heritability of the Set-Point Virus Load, while Selection for CTL Escape Dominates Virulence Evolution
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{"title"=>"Immuno-epidemiological Modeling of HIV-1 Predicts High Heritability of the Set-Point Virus Load, while Selection for CTL Escape Dominates Virulence Evolution", "type"=>"journal", "authors"=>[{"first_name"=>"Christiaan H.", "last_name"=>"van Dorp", "scopus_author_id"=>"56458641900"}, {"first_name"=>"Michiel", "last_name"=>"van Boven", "scopus_author_id"=>"7004419934"}, {"first_name"=>"Rob J.", "last_name"=>"de Boer", "scopus_author_id"=>"7102680310"}], "year"=>2014, "source"=>"PLoS Computational Biology", "identifiers"=>{"issn"=>"15537358", "scopus"=>"2-s2.0-84919625666", "sgr"=>"84919625666", "pui"=>"601019907", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)", "pmid"=>"25522184", "doi"=>"10.1371/journal.pcbi.1003899"}, "id"=>"e27f7f58-df31-3ce8-a380-5d8da5b3d07b", "abstract"=>"It has been suggested that HIV-1 has evolved its set-point virus load to be optimized for transmission. Previous epidemiological models and studies into the heritability of set-point virus load confirm that this mode of adaptation within the human population is feasible. However, during the many cycles of replication between infection of a host and transmission to the next host, HIV-1 is under selection for escape from immune responses, and not transmission. Here we investigate with computational and mathematical models how these two levels of selection, within-host and between-host, are intertwined. We find that when the rate of immune escape is comparable to what has been observed in patients, immune selection within hosts is dominant over selection for transmission. Surprisingly, we do find high values for set-point virus load heritability, and argue that high heritability estimates can be caused by the 'footprints' left by differing hosts' immune systems on the virus.", "link"=>"http://www.mendeley.com/research/immunoepidemiological-modeling-hiv1-predicts-high-heritability-setpoint-virus-load-while-selection-c", "reader_count"=>19, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Researcher"=>6, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>5, "Student > Master"=>1, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Researcher"=>6, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>5, "Student > Master"=>1, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Unspecified"=>1, "Biochemistry, Genetics and Molecular Biology"=>1, "Agricultural and Biological Sciences"=>7, "Medicine and Dentistry"=>2, "Social Sciences"=>1, "Computer Science"=>3, "Immunology and Microbiology"=>3}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Social Sciences"=>{"Social Sciences"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>7}, "Computer Science"=>{"Computer Science"=>3}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"Netherlands"=>1, "Hungary"=>1, "United States"=>1}, "group_count"=>0}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1848033"], "description"=>"<p>(A) The panel shows a contour plot of heritability (, gray lines, red/yellow faces) as a function of the maximal virus load () and the expected similarity between binding repertoires (). On top of the heritability contour plot, the blue lines indicate the contours of . The heavy blue contour corresponds to the transmission-optimal . (B) Distributions of the overlap between pairs of binding repertoires. The black bars correspond to European HLA-haplotypes and a clade B virus (sampled in the Netherlands). The gray bars correspond to Sub-Saharan HLA-haplotypes and a clade C virus (sampled in South Africa). The distributions were simulated by sampling a HLA-haplotype pairs. (C) Statistics on the sampled distributions as in panel B. The left panel shows the medians of the similarity distributions for strains representative of clade B (black dots) and clade C (gray dots). The difference is significant (Mann-Withney -test, , *). The right panel depicts the -statistic for all clade B and clade C pairs. The mean of the -statistics is significantly larger than (-test , ***).</p>", "links"=>[], "tags"=>["load", "hiv", "CTL Escape Dominates Virulence Evolution", "virus", "heritability estimates", "model", "Predicts High Heritability", "host"], "article_id"=>1274867, "categories"=>["Uncategorised"], "users"=>["Christiaan H. van Dorp", "Michiel van Boven", "Rob J. de Boer"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003899.g008", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Host_heterogeneity_and_heritability_/1274867", "title"=>"Host-heterogeneity and heritability.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-12-18 03:41:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/1848025"], "description"=>"<p>(A) The average number of escape (solid) and deleterious (dashed) mutations at the end of the acute phase for different . The resulting graph is barely dependent on within the range (not shown). However, we choose such that the mean set-point virus load () equals , cf. the blue, dashed line in panel D (this also holds for panels B and C). The gray band indicates the percentiles for the number of escape mutations in the acute phase. (B) The mean fraction of immune responses (solid) and deleterious mutations (dashed) that remain after the acute phase. The resulting graph is barely dependent on within the range (not shown). (C) Heritability as a function of the mutation rate (upper red line). The black line below corresponds to the contribution of the immunological footprint to heritability, as estimated with the SEM. (D) Heritability of set-point virus load for different combinations of and for the standard model in steady state. The blue, dashed line indicates the contour where the equals .</p>", "links"=>[], "tags"=>["load", "hiv", "CTL Escape Dominates Virulence Evolution", "virus", "heritability estimates", "model", "Predicts High Heritability", "host"], "article_id"=>1274860, "categories"=>["Uncategorised"], "users"=>["Christiaan H. van Dorp", "Michiel van Boven", "Rob J. de Boer"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003899.g005", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Heritability_of_set_point_and_the_number_of_mutations_during_the_acute_phase_/1274860", "title"=>"Heritability of set-point and the number of mutations during the acute phase.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-12-18 03:41:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/1848021"], "description"=>"<p>The contours show the mean set-point virus load in the population-level equilibrium. The heavy black line indicates the graph of , i.e., the value for which is optimal for transmission, given the mutation rate . (A) The standard model: A peaked TP and a heterogeneous host population. (B) Control 1: A flat TP and a heterogeneous population. (C) Control 2: A peaked TP and a homogeneous population.</p>", "links"=>[], "tags"=>["load", "hiv", "CTL Escape Dominates Virulence Evolution", "virus", "heritability estimates", "model", "Predicts High Heritability", "host"], "article_id"=>1274856, "categories"=>["Uncategorised"], "users"=>["Christiaan H. van Dorp", "Michiel van Boven", "Rob J. de Boer"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003899.g004", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Exploration_of_the_parameter_space_in_three_scenarios_/1274856", "title"=>"Exploration of the parameter space in three scenarios.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-12-18 03:41:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/1848035"], "description"=>"<p>Agents and events in the model.</p>", "links"=>[], "tags"=>["load", "hiv", "CTL Escape Dominates Virulence Evolution", "virus", "heritability estimates", "model", "Predicts High Heritability", "host"], "article_id"=>1274869, "categories"=>["Uncategorised"], "users"=>["Christiaan H. van Dorp", "Michiel van Boven", "Rob J. de Boer"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003899.t002", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Agents_and_events_in_the_model_/1274869", "title"=>"Agents and events in the model.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-12-18 03:41:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/1848034"], "description"=>"<p>Notes: (1) is chosen larger than observed numbers of immune responses <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003899#pcbi.1003899-Liu1\" target=\"_blank\">[25]</a>, <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003899#pcbi.1003899-Henn1\" target=\"_blank\">[27]</a>, <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003899#pcbi.1003899-Liu2\" target=\"_blank\">[43]</a>, since we predict that viruses have escape mutations at infection, and do not escape all CTL responses. is chosen to get reasonable variance in spVL, while limiting individuals with a very small binding repertoire. (2) About of all possible peptides from HIV-1's proteome of a.a. (3) During the chronic phase, the escape rate slows down markedly <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003899#pcbi.1003899-Henn1\" target=\"_blank\">[27]</a>, <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003899#pcbi.1003899-Asquith1\" target=\"_blank\">[60]</a>, hence we take . The AIDS phase is sometimes preceded by escape from critical immune responses <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003899#pcbi.1003899-Nowak1\" target=\"_blank\">[75]</a>, and modeling suggests that escape rate speeds up towards the late disease phase <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003899#pcbi.1003899-vanDeutekom1\" target=\"_blank\">[46]</a>. Therefore we set . (4) Both reports on fast <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003899#pcbi.1003899-Fernandez1\" target=\"_blank\">[76]</a> and very slow <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003899#pcbi.1003899-Fryer1\" target=\"_blank\">[77]</a> reversion exist. We choose in the order of magnitude of the ratio fitness cost and escape benefit. (5) The model for virus load was taken from <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003899#pcbi.1003899-Schmid1\" target=\"_blank\">[35]</a>. During the acute phase, merely represents the virus fitness. (6) The magnitude is chosen to be in estimated ranges <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003899#pcbi.1003899-Kadolsky1\" target=\"_blank\">[31]</a>, <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003899#pcbi.1003899-Kiepiela1\" target=\"_blank\">[32]</a>. Since escape appears to be faster than reversion, we choose . Although several studies find that a CTL response to Gag gives a fold higher fitness cost than <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003899#pcbi.1003899-Goepfert1\" target=\"_blank\">[29]</a>, <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003899#pcbi.1003899-Kiepiela1\" target=\"_blank\">[32]</a>, we take as an average fitness cost. (7) The parameters and were taken from <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003899#pcbi.1003899-Fraser1\" target=\"_blank\">[17]</a>. The parameters for the Hill functions and are: , , , , , .</p><p>Parameters and variables of the (standard) model.</p>", "links"=>[], "tags"=>["load", "hiv", "CTL Escape Dominates Virulence Evolution", "virus", "heritability estimates", "model", "Predicts High Heritability", "host"], "article_id"=>1274868, "categories"=>["Uncategorised"], "users"=>["Christiaan H. van Dorp", "Michiel van Boven", "Rob J. de Boer"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003899.t001", "stats"=>{"downloads"=>7, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Parameters_and_variables_of_the_standard_model_/1274868", "title"=>"Parameters and variables of the (standard) model.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-12-18 03:41:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/1848029"], "description"=>"<p>Shown is a directed, acyclic graph (DAG) representing the SEM. The arrows indicate dependencies between the variables. The numbers above the arrows are the fitted weights (all highly significant: ), and the size of these weights is also represented by the thickness of the arrows. The data for this example comes from a simulation of the standard model, with and .</p>", "links"=>[], "tags"=>["load", "hiv", "CTL Escape Dominates Virulence Evolution", "virus", "heritability estimates", "model", "Predicts High Heritability", "host"], "article_id"=>1274863, "categories"=>["Uncategorised"], "users"=>["Christiaan H. van Dorp", "Michiel van Boven", "Rob J. de Boer"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003899.g007", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_structural_equation_model_SEM_used_for_quantifying_the_immunological_footprint_/1274863", "title"=>"The structural equation model (SEM) used for quantifying the immunological footprint.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-12-18 03:41:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/1848028"], "description"=>"<p>For each a thin gray line indicates the curve . The heavy black line separates the region of the parameter space (between-host adaptation) where for all .</p>", "links"=>[], "tags"=>["load", "hiv", "CTL Escape Dominates Virulence Evolution", "virus", "heritability estimates", "model", "Predicts High Heritability", "host"], "article_id"=>1274862, "categories"=>["Uncategorised"], "users"=>["Christiaan H. van Dorp", "Michiel van Boven", "Rob J. de Boer"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003899.g006", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_bifurcation_in_the_homogeneous_model_/1274862", "title"=>"The bifurcation in the homogeneous model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-12-18 03:41:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/1847992"], "description"=>"<p>The graphs show the number of escape mutations (purple, top), the number of deleterious mutations (green, middle) and the virus load (blue, bottom). The mean number of mutations or virus load (the heavy lines) is based on simulations (the thin step-wise lines). The dots indicate that a host died. All infections start with a virus with mutations. The acute phase of the infection (the first ) is displayed magnified on the left of each plot, and a couple of simulations are highlighted in black. (A) The escape rate equals , and . (B) The escape rate equals , and . Other parameters are listed in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003899#pcbi-1003899-t001\" target=\"_blank\">Table 1</a>.</p>", "links"=>[], "tags"=>["load", "hiv", "CTL Escape Dominates Virulence Evolution", "virus", "heritability estimates", "model", "Predicts High Heritability", "host"], "article_id"=>1274844, "categories"=>["Uncategorised"], "users"=>["Christiaan H. van Dorp", "Michiel van Boven", "Rob J. de Boer"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003899.g002", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_within_host_model_for_immune_escape_for_different_mutation_rates_/1274844", "title"=>"The within-host model for immune escape for different mutation rates.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-12-18 03:41:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/1848047"], "description"=>"<div><p>It has been suggested that HIV-1 has evolved its set-point virus load to be optimized for transmission. Previous epidemiological models and studies into the heritability of set-point virus load confirm that this mode of adaptation within the human population is feasible. However, during the many cycles of replication between infection of a host and transmission to the next host, HIV-1 is under selection for escape from immune responses, and not transmission. Here we investigate with computational and mathematical models how these two levels of selection, within-host and between-host, are intertwined. We find that when the rate of immune escape is comparable to what has been observed in patients, immune selection within hosts is dominant over selection for transmission. Surprisingly, we do find high values for set-point virus load heritability, and argue that high heritability estimates can be caused by the ‘footprints’ left by differing hosts' immune systems on the virus.</p></div>", "links"=>[], "tags"=>["load", "hiv", "CTL Escape Dominates Virulence Evolution", "virus", "heritability estimates", "model", "Predicts High Heritability", "host"], "article_id"=>1274876, "categories"=>["Uncategorised"], "users"=>["Christiaan H. van Dorp", "Michiel van Boven", "Rob J. de Boer"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003899", "stats"=>{"downloads"=>0, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Immuno_epidemiological_Modeling_of_HIV_1_Predicts_High_Heritability_of_the_Set_Point_Virus_Load_while_Selection_for_CTL_Escape_Dominates_Virulence_Evolution_/1274876", "title"=>"Immuno-epidemiological Modeling of HIV-1 Predicts High Heritability of the Set-Point Virus Load, while Selection for CTL Escape Dominates Virulence Evolution", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-12-18 03:41:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/1848036"], "description"=>"<p>The functions for are here defined by , if the patients disease is in the acute phase, and similarly for the asymptomatic phase, and for the AIDS phase. The function describes the viral load during the asymptomatic phase (that may not be constant due to escape mutations and reversions). The shape parameter for the Gamma distribution was estimated by Fraser et al. <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003899#pcbi.1003899-Fraser1\" target=\"_blank\">[17]</a>.</p><p>Threshold distributions, rates, and actions for the events in the model.</p>", "links"=>[], "tags"=>["load", "hiv", "CTL Escape Dominates Virulence Evolution", "virus", "heritability estimates", "model", "Predicts High Heritability", "host"], "article_id"=>1274870, "categories"=>["Uncategorised"], "users"=>["Christiaan H. van Dorp", "Michiel van Boven", "Rob J. de Boer"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003899.t003", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Threshold_distributions_rates_and_actions_for_the_events_in_the_model_/1274870", "title"=>"Threshold distributions, rates, and actions for the events in the model.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-12-18 03:41:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/1847983"], "description"=>"<p>The virus in the figure has potential epitopes (the rectangles), of which have a mutation (the open rectangles). ‘pMHC’ denotes the peptide-HLA complex. (Host A) Host A's HLA molecules can not bind peptides 1 and 4 (neither the wild-type, nor the mutant), but they can bind the wild-type of peptides 2 and 5. Thus, the purple rectangles denote immune escape mutations and the green (dotted) rectangles represent deleterious mutations. Since peptides 2 and 5 are mutated, they are escape epitopes in host A. The HLA molecules of host A can bind peptides 3 and 6, and hence peptides 3 and 6 are the epitopes for host A. During the infectious lifetime of host A, epitopes 3 and 6 may escape, and the mutated peptides 1 and 4 may revert to the wild-type. (Host B) The HLA molecules of host B bind less peptides of the wild-type virus () than host A (); host B mounts a single CTL response against peptide . The HLA molecules of host B can also bind the wild-type of peptide 1, but this peptide is mutated, and hence peptide 1 is an escape epitope in host B. During host B's infection, epitope 3 may escape, and peptides 2, 4 and 5 may revert to the wild-type.</p>", "links"=>[], "tags"=>["load", "hiv", "CTL Escape Dominates Virulence Evolution", "virus", "heritability estimates", "model", "Predicts High Heritability", "host"], "article_id"=>1274835, "categories"=>["Uncategorised"], "users"=>["Christiaan H. van Dorp", "Michiel van Boven", "Rob J. de Boer"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003899.g001", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_phenotype_of_a_virus_differs_between_hosts_depending_on_the_hosts_HLA_haplotype_/1274835", "title"=>"The phenotype of a virus differs between hosts, depending on the hosts' HLA haplotype.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-12-18 03:41:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/1848017"], "description"=>"<p>The parameters are as follows: The maximal virus load equals , and the population size equals . (A) The escape mutation rate in the acute phase equals . (B) The escape mutation rate in the acute phase equals . The other parameters are listed in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003899#pcbi-1003899-t001\" target=\"_blank\">Table 1</a>. The simulations were started with infected individuals that were infected with a virus with mutations. The heavy lines in the graph of the set-point (spVL) and the number of mutations (# mutations) denote the population-wide average, i.e., and , respectively. The light bands denote the percentiles, and the dots indicate the spVL of the receiver of a transmission couple (spVL) and the number of mutations of the transmitted strain (# mutations). In the graphs of the spVL, the dashed black line indicates the mean set-point that maximizes the transmission potential of HIV-1.</p>", "links"=>[], "tags"=>["load", "hiv", "CTL Escape Dominates Virulence Evolution", "virus", "heritability estimates", "model", "Predicts High Heritability", "host"], "article_id"=>1274852, "categories"=>["Uncategorised"], "users"=>["Christiaan H. van Dorp", "Michiel van Boven", "Rob J. de Boer"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003899.g003", "stats"=>{"downloads"=>0, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Two_simulations_of_HIV_1_epidemics_with_two_different_mutation_rates_/1274852", "title"=>"Two simulations of HIV-1 epidemics with two different mutation rates.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-12-18 03:41:56"}

PMC Usage Stats | Further Information

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  • {"unique-ip"=>"2", "full-text"=>"2", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
  • {"unique-ip"=>"5", "full-text"=>"4", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
  • {"unique-ip"=>"5", "full-text"=>"5", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"10"}
  • {"unique-ip"=>"8", "full-text"=>"2", "pdf"=>"9", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"12"}

Relative Metric

{"start_date"=>"2014-01-01T00:00:00Z", "end_date"=>"2014-12-31T00:00:00Z", "subject_areas"=>[]}
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