Combining Epidemiological and Genetic Networks Signifies the Importance of Early Treatment in HIV-1 Transmission
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{"title"=>"Combining Epidemiological and Genetic Networks Signifies the Importance of Early Treatment in HIV-1 Transmission", "type"=>"journal", "authors"=>[{"first_name"=>"Narges", "last_name"=>"Zarrabi", "scopus_author_id"=>"36681057600"}, {"first_name"=>"Mattia", "last_name"=>"Prosperi", "scopus_author_id"=>"22036230400"}, {"first_name"=>"Robert G.", "last_name"=>"Belleman", "scopus_author_id"=>"6508058629"}, {"first_name"=>"Manuela", "last_name"=>"Colafigli", "scopus_author_id"=>"6506695797"}, {"first_name"=>"Andrea", "last_name"=>"de Luca", "scopus_author_id"=>"7201948233"}, {"first_name"=>"Peter M.A.", "last_name"=>"Sloot", "scopus_author_id"=>"7006010423"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"isbn"=>"1932-6203", "scopus"=>"2-s2.0-84867057024", "pui"=>"365769425", "doi"=>"10.1371/journal.pone.0046156", "sgr"=>"84867057024", "pmid"=>"23029421", "issn"=>"19326203"}, "id"=>"a1eeb4cc-7664-3d78-883f-863fcb167f84", "abstract"=>"Inferring disease transmission networks is important in epidemiology in order to understand and prevent the spread of infectious diseases. Reconstruction of the infection transmission networks requires insight into viral genome data as well as social interactions. For the HIV-1 epidemic, current research either uses genetic information of patients' virus to infer the past infection events or uses statistics of sexual interactions to model the network structure of viral spreading. Methods for a reliable reconstruction of HIV-1 transmission dynamics, taking into account both molecular and societal data are still lacking. The aim of this study is to combine information from both genetic and epidemiological scales to characterize and analyse a transmission network of the HIV-1 epidemic in central Italy.We introduce a novel filter-reduction method to build a network of HIV infected patients based on their social and treatment information. The network is then combined with a genetic network, to infer a hypothetical infection transmission network. We apply this method to a cohort study of HIV-1 infected patients in central Italy and find that patients who are highly connected in the network have longer untreated infection periods. We also find that the network structures for homosexual males and heterosexual populations are heterogeneous, consisting of a majority of 'peripheral nodes' that have only a few sexual interactions and a minority of 'hub nodes' that have many sexual interactions. Inferring HIV-1 transmission networks using this novel combined approach reveals remarkable correlations between high out-degree individuals and longer untreated infection periods. These findings signify the importance of early treatment and support the potential benefit of wide population screening, management of early diagnoses and anticipated antiretroviral treatment to prevent viral transmission and spread. The approach presented here for reconstructing HIV-1 transmission networks can have important repercussions in the design of intervention strategies for disease control.", "link"=>"http://www.mendeley.com/research/combining-epidemiological-genetic-networks-signifies-importance-early-treatment-hiv1-transmission", "reader_count"=>38, "reader_count_by_academic_status"=>{"Researcher"=>10, "Student > Ph. D. Student"=>11, "Student > Postgraduate"=>5, "Student > Master"=>4, "Other"=>2, "Student > Bachelor"=>3, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Researcher"=>10, "Student > Ph. D. Student"=>11, "Student > Postgraduate"=>5, "Student > Master"=>4, "Other"=>2, "Student > Bachelor"=>3, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Environmental Science"=>2, "Biochemistry, Genetics and Molecular Biology"=>2, "Nursing and Health Professions"=>2, "Mathematics"=>2, "Agricultural and Biological Sciences"=>11, "Medicine and Dentistry"=>5, "Psychology"=>1, "Social Sciences"=>4, "Computer Science"=>2, "Immunology and Microbiology"=>2, "Veterinary Science and Veterinary Medicine"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>5}, "Social Sciences"=>{"Social Sciences"=>4}, "Psychology"=>{"Psychology"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>11}, "Computer Science"=>{"Computer Science"=>2}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>2}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>3}, "Environmental Science"=>{"Environmental Science"=>2}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>1}}, "reader_count_by_country"=>{"Netherlands"=>2, "United States"=>1, "Italy"=>1, "United Kingdom"=>2}, "group_count"=>0}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/301870", "https://ndownloader.figshare.com/files/301913", "https://ndownloader.figshare.com/files/301949", "https://ndownloader.figshare.com/files/301987", "https://ndownloader.figshare.com/files/302047", "https://ndownloader.figshare.com/files/302075", "https://ndownloader.figshare.com/files/302098", "https://ndownloader.figshare.com/files/302133", "https://ndownloader.figshare.com/files/302183", "https://ndownloader.figshare.com/files/302229", "https://ndownloader.figshare.com/files/302287", "https://ndownloader.figshare.com/files/302335", "https://ndownloader.figshare.com/files/302384", "https://ndownloader.figshare.com/files/302463"], "description"=>"<div><p>Inferring disease transmission networks is important in epidemiology in order to understand and prevent the spread of infectious diseases. Reconstruction of the infection transmission networks requires insight into viral genome data as well as social interactions. For the HIV-1 epidemic, current research either uses genetic information of patients' virus to infer the past infection events or uses statistics of sexual interactions to model the network structure of viral spreading. Methods for a reliable reconstruction of HIV-1 transmission dynamics, taking into account both molecular and societal data are still lacking. The aim of this study is to combine information from both genetic and epidemiological scales to characterize and analyse a transmission network of the HIV-1 epidemic in central Italy.</p> <p>We introduce a novel filter-reduction method to build a network of HIV infected patients based on their social and treatment information. The network is then combined with a genetic network, to infer a hypothetical infection transmission network. We apply this method to a cohort study of HIV-1 infected patients in central Italy and find that patients who are highly connected in the network have longer untreated infection periods. We also find that the network structures for homosexual males and heterosexual populations are heterogeneous, consisting of a majority of ‘peripheral nodes’ that have only a few sexual interactions and a minority of ‘hub nodes’ that have many sexual interactions. Inferring HIV-1 transmission networks using this novel combined approach reveals remarkable correlations between high out-degree individuals and longer untreated infection periods. These findings signify the importance of early treatment and support the potential benefit of wide population screening, management of early diagnoses and anticipated antiretroviral treatment to prevent viral transmission and spread. The approach presented here for reconstructing HIV-1 transmission networks can have important repercussions in the design of intervention strategies for disease control.</p> </div>", "links"=>[], "tags"=>["combining", "epidemiological", "networks", "signifies", "hiv-1"], "article_id"=>119537, "categories"=>["Information And Computing Sciences", "Cancer", "Biological Sciences", "Immunology", "Biotechnology"], "users"=>["Narges Zarrabi", "Mattia Prosperi", "Robert G. Belleman", "Manuela Colafigli", "Andrea De Luca", "Peter M. A. Sloot"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0046156.s001", "https://dx.doi.org/10.1371/journal.pone.0046156.s002", "https://dx.doi.org/10.1371/journal.pone.0046156.s003", "https://dx.doi.org/10.1371/journal.pone.0046156.s004", "https://dx.doi.org/10.1371/journal.pone.0046156.s005", "https://dx.doi.org/10.1371/journal.pone.0046156.s006", "https://dx.doi.org/10.1371/journal.pone.0046156.s007", "https://dx.doi.org/10.1371/journal.pone.0046156.s008", "https://dx.doi.org/10.1371/journal.pone.0046156.s009", "https://dx.doi.org/10.1371/journal.pone.0046156.s010", "https://dx.doi.org/10.1371/journal.pone.0046156.s011", "https://dx.doi.org/10.1371/journal.pone.0046156.s012", "https://dx.doi.org/10.1371/journal.pone.0046156.s013", "https://dx.doi.org/10.1371/journal.pone.0046156.s014"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Combining_Epidemiological_and_Genetic_Networks_Signifies_the_Importance_of_Early_Treatment_in_HIV_1_Transmission/119537", "title"=>"Combining Epidemiological and Genetic Networks Signifies the Importance of Early Treatment in HIV-1 Transmission", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-09-28 02:38:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/566650"], "description"=>"<p>Starting from an undirected fully-connected network of all HIV sequences in the data, a set of social/sexual filters is applied to obtain an undirected filtered network. To convert the network to a directed one a seroconversion function is applied, deriving a contact network.</p>", "links"=>[], "tags"=>["constructing", "networks", "filter-reduction"], "article_id"=>237139, "categories"=>["Infectious Diseases", "Biological Sciences", "Information And Computing Sciences", "Biotechnology", "Immunology"], "users"=>["Narges Zarrabi", "Mattia Prosperi", "Robert G. Belleman", "Manuela Colafigli", "Andrea De Luca", "Peter M. A. Sloot"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0046156.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Workflow_for_constructing_networks_using_the_filter_reduction_method_/237139", "title"=>"Workflow for constructing networks using the filter-reduction method.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-09-28 01:58:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/566740"], "description"=>"<p>Visualization of the contact network consisting of three sub-networks corresponding to the major HIV-1 transmission risk groups: MSM (yellow), Heterosexual (red), and IDU (green).</p>", "links"=>[], "tags"=>["public health and epidemiology", "Infectious diseases", "Computational biology", "epidemiology", "computer science"], "article_id"=>237226, "categories"=>["Infectious Diseases", "Biological Sciences", "Information And Computing Sciences", "Biotechnology", "Immunology"], "users"=>["Narges Zarrabi", "Mattia Prosperi", "Robert G. Belleman", "Manuela Colafigli", "Andrea De Luca", "Peter M. A. Sloot"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0046156.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_contact_network_/237226", "title"=>"The contact network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-09-28 02:00:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/566832"], "description"=>"<p>The cumulative total- (black), in- (blue), and out-degree (pink) distributions for the entire network (all risk groups), MSM, Heterosexual, and IDU risk groups plotted in log-log scale.</p>", "links"=>[], "tags"=>["distributions"], "article_id"=>237327, "categories"=>["Infectious Diseases", "Biological Sciences", "Information And Computing Sciences", "Biotechnology", "Immunology"], "users"=>["Narges Zarrabi", "Mattia Prosperi", "Robert G. Belleman", "Manuela Colafigli", "Andrea De Luca", "Peter M. A. Sloot"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0046156.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Degree_distributions_of_the_contact_network_/237327", "title"=>"Degree distributions of the contact network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-09-28 02:02:07"}
  • {"files"=>["https://ndownloader.figshare.com/files/566998"], "description"=>"<p>The colouring trend in the patient's estimated seroconversion year, ranging from 1982 (blue) to 2008 (red).</p>", "links"=>[], "tags"=>["inferred", "coloured"], "article_id"=>237490, "categories"=>["Infectious Diseases", "Biological Sciences", "Information And Computing Sciences", "Biotechnology", "Immunology"], "users"=>["Narges Zarrabi", "Mattia Prosperi", "Robert G. Belleman", "Manuela Colafigli", "Andrea De Luca", "Peter M. A. Sloot"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0046156.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_inferred_contact_network_coloured_based_on_estimated_year_of_seroconversion_/237490", "title"=>"The inferred contact network coloured based on estimated year of seroconversion.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-09-28 02:04:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/567163"], "description"=>"<p><i>UIP</i> vs. the out-going degree of nodes in the MSM, Heterosexual, IDU and all risk groups populations. The Pearson's correlation coefficients, 95% confidence intervals and p-values are depicted on each graph.</p>", "links"=>[], "tags"=>["public health and epidemiology", "Infectious diseases", "Computational biology", "epidemiology", "computer science"], "article_id"=>237647, "categories"=>["Infectious Diseases", "Biological Sciences", "Information And Computing Sciences", "Biotechnology", "Immunology"], "users"=>["Narges Zarrabi", "Mattia Prosperi", "Robert G. Belleman", "Manuela Colafigli", "Andrea De Luca", "Peter M. A. Sloot"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0046156.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Untreated_infection_period_UIP_versus_out_degree_/237647", "title"=>"Untreated infection period (UIP) versus out-degree.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-09-28 02:07:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/567300"], "description"=>"<p>The hypothetical transmission network of the entire population obtained from computing the intersection of the contact and the genetic network. Patients are colored based on their risk groups: MSM (yellow), Heterosexual (red), IDU (green) and blood products (cyan).</p>", "links"=>[], "tags"=>["hypothetical"], "article_id"=>237792, "categories"=>["Infectious Diseases", "Biological Sciences", "Information And Computing Sciences", "Biotechnology", "Immunology"], "users"=>["Narges Zarrabi", "Mattia Prosperi", "Robert G. Belleman", "Manuela Colafigli", "Andrea De Luca", "Peter M. A. Sloot"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0046156.g006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_hypothetical_transmission_network_/237792", "title"=>"The hypothetical transmission network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-09-28 02:09:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/567433"], "description"=>"<p>Cumulative total- (black), in- (blue), and out- (pink) degree distributions of the hypothetical transmission network of the MSM, heterosexual, IDU and all risk groups plotted in log-log scale.</p>", "links"=>[], "tags"=>["distributions", "hypothetical"], "article_id"=>237923, "categories"=>["Infectious Diseases", "Biological Sciences", "Information And Computing Sciences", "Biotechnology", "Immunology"], "users"=>["Narges Zarrabi", "Mattia Prosperi", "Robert G. Belleman", "Manuela Colafigli", "Andrea De Luca", "Peter M. A. Sloot"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0046156.g007"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Degree_distributions_of_the_hypothetical_transmission_network_/237923", "title"=>"Degree distributions of the hypothetical transmission network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-09-28 02:12:03"}
  • {"files"=>["https://ndownloader.figshare.com/files/567547"], "description"=>"<p>The statistics of patients characteristics (total n = 655, subtype B patients, excluding entries with unknown risk group).</p>", "links"=>[], "tags"=>["patients", "characteristics", "subtype", "excluding", "entries"], "article_id"=>238032, "categories"=>["Infectious Diseases", "Biological Sciences", "Information And Computing Sciences", "Biotechnology", "Immunology"], "users"=>["Narges Zarrabi", "Mattia Prosperi", "Robert G. Belleman", "Manuela Colafigli", "Andrea De Luca", "Peter M. A. Sloot"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0046156.t008"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_statistics_of_patients_characteristics_total_n_8202_8202_655_subtype_B_patients_excluding_entries_with_unknown_risk_group_/238032", "title"=>"The statistics of patients characteristics (total n = 655, subtype B patients, excluding entries with unknown risk group).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-09-28 02:13:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/567591"], "description"=>"<p>Results of a multi-variable regression analysis showing the factors associated with high out-degree nodes. The out-degree is the dependent variable in the analysis, and age, viral load, UIP, gender, and In-degree are independent variables.</p>", "links"=>[], "tags"=>["out-degree"], "article_id"=>238082, "categories"=>["Infectious Diseases", "Biological Sciences", "Information And Computing Sciences", "Biotechnology", "Immunology"], "users"=>["Narges Zarrabi", "Mattia Prosperi", "Robert G. Belleman", "Manuela Colafigli", "Andrea De Luca", "Peter M. A. Sloot"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0046156.t006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Factors_associated_with_out_degree_nodes_/238082", "title"=>"Factors associated with out-degree nodes.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-09-28 02:14:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/567625"], "description"=>"<p>Both inferred and randomized networks are of the same size in terms of number of nodes and edges. The properties of the randomized network is an average over the properties of 5 random networks.</p>", "links"=>[], "tags"=>["hypothetical"], "article_id"=>238119, "categories"=>["Infectious Diseases", "Biological Sciences", "Information And Computing Sciences", "Biotechnology", "Immunology"], "users"=>["Narges Zarrabi", "Mattia Prosperi", "Robert G. Belleman", "Manuela Colafigli", "Andrea De Luca", "Peter M. A. Sloot"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0046156.t007"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Properties_of_the_hypothetical_transmission_network_against_random_networks_/238119", "title"=>"Properties of the hypothetical transmission network against random networks.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-09-28 02:15:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/567658"], "description"=>"<p>Basic parameters of the data (total-, in- and out-degree distributions of the MSM, heterosexual, IDU and all risk groups), along with their power-law fits and the corresponding p-value. Goodness-of-fit tests compare the observed data to the hypothesized power-law distribution. If the resulting p-value is greater than 0.1, power-law is plausible for the data (statistically significant values are denoted in bold).</p>", "links"=>[], "tags"=>["parameters"], "article_id"=>238152, "categories"=>["Infectious Diseases", "Biological Sciences", "Information And Computing Sciences", "Biotechnology", "Immunology"], "users"=>["Narges Zarrabi", "Mattia Prosperi", "Robert G. Belleman", "Manuela Colafigli", "Andrea De Luca", "Peter M. A. Sloot"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0046156.t004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Basic_parameters_of_the_data_and_the_power_law_fit_/238152", "title"=>"Basic parameters of the data and the power law fit.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-09-28 02:15:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/567692"], "description"=>"<p>For each degree distribution we give a p-value for the fit to the power-law model and likelihood ratios (<i>LR</i>) for the alternatives. We also quote p-values for the significance of each of the likelihood ratio tests. Significant p-values are denoted in bold. Positive values of the likelihood ratios indicate that the power-law model is favored over the alternative. The final column of the table lists the judgment of the statistical support for the power-law hypothesis for each distribution. “Moderate” indicates that the power-law is a good fit but there are other plausible alternatives as well; “good” indicates that the power-law is a good fit and that none of the alternatives considered is plausible.</p>", "links"=>[], "tags"=>["ratios"], "article_id"=>238180, "categories"=>["Infectious Diseases", "Biological Sciences", "Information And Computing Sciences", "Biotechnology", "Immunology"], "users"=>["Narges Zarrabi", "Mattia Prosperi", "Robert G. Belleman", "Manuela Colafigli", "Andrea De Luca", "Peter M. A. Sloot"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0046156.t005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Test_of_power_law_behavior_in_the_data_and_likelihood_ratios_of_alternative_distributions_/238180", "title"=>"Test of power law behavior in the data and likelihood ratios of alternative distributions.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-09-28 02:16:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/567735"], "description"=>"<p>Properties of the contact network.</p>", "links"=>[], "tags"=>["public health and epidemiology", "Infectious diseases", "Computational biology", "epidemiology", "computer science"], "article_id"=>238221, "categories"=>["Infectious Diseases", "Biological Sciences", "Information And Computing Sciences", "Biotechnology", "Immunology"], "users"=>["Narges Zarrabi", "Mattia Prosperi", "Robert G. Belleman", "Manuela Colafigli", "Andrea De Luca", "Peter M. A. Sloot"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0046156.t002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Properties_of_the_contact_network_/238221", "title"=>"Properties of the contact network.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-09-28 02:17:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/567770"], "description"=>"<p>Properties of the hypothetical transmission network.</p>", "links"=>[], "tags"=>["hypothetical"], "article_id"=>238264, "categories"=>["Infectious Diseases", "Biological Sciences", "Information And Computing Sciences", "Biotechnology", "Immunology"], "users"=>["Narges Zarrabi", "Mattia Prosperi", "Robert G. Belleman", "Manuela Colafigli", "Andrea De Luca", "Peter M. A. Sloot"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0046156.t003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Properties_of_the_hypothetical_transmission_network_/238264", "title"=>"Properties of the hypothetical transmission network.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-09-28 02:17:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/567809"], "description"=>"<p>Rules for social/sexual filters. gender (<i>g</i>), risk group (<i>r</i>), therapy date (<i>t</i>), estimated seroconversion date (<i>s</i>).</p>", "links"=>[], "tags"=>["filters", "constructing"], "article_id"=>238303, "categories"=>["Infectious Diseases", "Biological Sciences", "Information And Computing Sciences", "Biotechnology", "Immunology"], "users"=>["Narges Zarrabi", "Mattia Prosperi", "Robert G. Belleman", "Manuela Colafigli", "Andrea De Luca", "Peter M. A. Sloot"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0046156.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Social_sexual_filters_for_constructing_a_contact_network_/238303", "title"=>"Social/sexual filters for constructing a contact network.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-09-28 02:18:23"}

PMC Usage Stats | Further Information

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  • {"unique-ip"=>"9", "full-text"=>"9", "pdf"=>"5", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2015", "month"=>"1"}
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Relative Metric

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