Simulated Epidemics in an Empirical Spatiotemporal Network of 50,185 Sexual Contacts
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{"title"=>"Simulated epidemics in an empirical spatiotemporal network of 50,185 sexual contacts", "type"=>"journal", "authors"=>[{"first_name"=>"Luis E.C.", "last_name"=>"Rocha", "scopus_author_id"=>"35785154600"}, {"first_name"=>"Fredrik", "last_name"=>"Liljeros", "scopus_author_id"=>"6602137931"}, {"first_name"=>"Petter", "last_name"=>"Holme", "scopus_author_id"=>"7003326827"}], "year"=>2011, "source"=>"PLoS Computational Biology", "identifiers"=>{"pui"=>"361550329", "sgr"=>"79953671188", "issn"=>"1553734X", "arxiv"=>"1006.2856", "pmid"=>"21445228", "scopus"=>"2-s2.0-79953671188", "doi"=>"10.1371/journal.pcbi.1001109", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)"}, "id"=>"3aca8fdc-aaf5-3ac5-b5d5-8b494f0f2a3e", "abstract"=>"Sexual contact patterns, both in their temporal and network structure, can influence the spread of sexually transmitted infections (STI). Most previous literature has focused on effects of network topology; few studies have addressed the role of temporal structure. We simulate disease spread using SI and SIR models on an empirical temporal network of sexual contacts in high-end prostitution. We compare these results with several other approaches, including randomization of the data, classic mean-field approaches, and static network simulations. We observe that epidemic dynamics in this contact structure have well-defined, rather high epidemic thresholds. Temporal effects create a broad distribution of outbreak sizes, even if the per-contact transmission probability is taken to its hypothetical maximum of 100%. In general, we conclude that the temporal correlations of our network accelerate outbreaks, especially in the early phase of the epidemics, while the network topology (apart from the contact-rate distribution) slows them down. We find that the temporal correlations of sexual contacts can significantly change simulated outbreaks in a large empirical sexual network. Thus, temporal structures are needed alongside network topology to fully understand the spread of STIs. On a side note, our simulations further suggest that the specific type of commercial sex we investigate is not a reservoir of major importance for HIV.", "link"=>"http://www.mendeley.com/research/simulated-epidemics-empirical-spatiotemporal-network-50185-sexual-contacts", "reader_count"=>133, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>10, "Librarian"=>1, "Student > Doctoral Student"=>4, "Researcher"=>30, "Student > Ph. D. Student"=>51, "Student > Postgraduate"=>2, "Student > Master"=>13, "Other"=>1, "Student > Bachelor"=>5, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>3, "Professor"=>11}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>10, "Librarian"=>1, "Student > Doctoral Student"=>4, "Researcher"=>30, "Student > Ph. D. Student"=>51, "Student > Postgraduate"=>2, "Student > Master"=>13, "Other"=>1, "Student > Bachelor"=>5, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>3, "Professor"=>11}, "reader_count_by_subject_area"=>{"Unspecified"=>6, "Agricultural and Biological Sciences"=>19, "Arts and Humanities"=>1, "Veterinary Science and Veterinary Medicine"=>2, "Business, Management and Accounting"=>2, "Computer Science"=>18, "Earth and Planetary Sciences"=>1, "Economics, Econometrics and Finance"=>3, "Engineering"=>10, "Environmental Science"=>3, "Biochemistry, Genetics and Molecular Biology"=>1, "Mathematics"=>15, "Medicine and Dentistry"=>8, "Physics and Astronomy"=>34, "Psychology"=>2, "Social Sciences"=>8}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>8}, "Social Sciences"=>{"Social Sciences"=>8}, "Physics and Astronomy"=>{"Physics and Astronomy"=>34}, "Psychology"=>{"Psychology"=>2}, "Mathematics"=>{"Mathematics"=>15}, "Unspecified"=>{"Unspecified"=>6}, "Environmental Science"=>{"Environmental Science"=>3}, "Arts and Humanities"=>{"Arts and Humanities"=>1}, "Engineering"=>{"Engineering"=>10}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>19}, "Computer Science"=>{"Computer Science"=>18}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>2}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>2}}, "reader_count_by_country"=>{"United States"=>8, "Japan"=>1, "United Kingdom"=>3, "Switzerland"=>4, "Portugal"=>1, "Spain"=>1, "New Zealand"=>1, "Canada"=>1, "Netherlands"=>1, "Brazil"=>2, "Italy"=>2, "South Africa"=>1, "Australia"=>1, "Germany"=>2}, "group_count"=>9}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/790848"], "description"=>"<p>Panel A displays the average outbreak size 〈Ω〉 as a function of the transmission probability. The line is a linear trend least square fitted to the data in the interval 0.3≤ρ≤1. The abscissa is in log-scale. Panel B shows the threshold ρ-value (estimated by the crossing of the linear fitting and the zero-size outbreak line) as a function of the beginning of the sampling window.</p>", "links"=>[], "tags"=>["si"], "article_id"=>461222, "categories"=>["Computational Biology", "Physics", "Neuroscience", "Medicine"], "users"=>["Luis E. C. Rocha", "Fredrik Liljeros", "Petter Holme"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1001109.g004", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Epidemic_threshold_for_SI_model_/461222", "title"=>"Epidemic threshold for SI model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-17 00:20:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/790788"], "description"=>"<p>In panel A, we plot the probability distribution <i>P</i>(Ω) of the outbreak sizes Ω for different transmission probabilities, in the SI model, for the original network, and in panel B for the network considering only encounters with oral sex without condom, and mouth kissing (ρ = 1 and SI model).</p>", "links"=>[], "tags"=>["outbreak", "distributions", "contagion"], "article_id"=>461162, "categories"=>["Computational Biology", "Physics", "Neuroscience", "Medicine"], "users"=>["Luis E. C. Rocha", "Fredrik Liljeros", "Petter Holme"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1001109.g003", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_the_outbreak_size_distributions_for_different_contagion_pathways_/461162", "title"=>"Comparison of the outbreak size distributions for different contagion pathways.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-17 00:19:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/395479", "https://ndownloader.figshare.com/files/395492"], "description"=>"<div><p>Sexual contact patterns, both in their temporal and network structure, can influence the spread of sexually transmitted infections (STI). Most previous literature has focused on effects of network topology; few studies have addressed the role of temporal structure. We simulate disease spread using SI and SIR models on an empirical temporal network of sexual contacts in high-end prostitution. We compare these results with several other approaches, including randomization of the data, classic mean-field approaches, and static network simulations. We observe that epidemic dynamics in this contact structure have well-defined, rather high epidemic thresholds. Temporal effects create a broad distribution of outbreak sizes, even if the per-contact transmission probability is taken to its hypothetical maximum of 100%. In general, we conclude that the temporal correlations of our network accelerate outbreaks, especially in the early phase of the epidemics, while the network topology (apart from the contact-rate distribution) slows them down. We find that the temporal correlations of sexual contacts can significantly change simulated outbreaks in a large empirical sexual network. Thus, temporal structures are needed alongside network topology to fully understand the spread of STIs. On a side note, our simulations further suggest that the specific type of commercial sex we investigate is not a reservoir of major importance for HIV.</p></div>", "links"=>[], "tags"=>["simulated", "epidemics", "empirical", "spatiotemporal", "contacts"], "article_id"=>138130, "categories"=>["Medicine", "Neuroscience", "Physics", "Biological Sciences"], "users"=>["Luis E. C. Rocha", "Fredrik Liljeros", "Petter Holme"], "doi"=>[nil, nil], "stats"=>{"downloads"=>35, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Simulated_Epidemics_in_an_Empirical_Spatiotemporal_Network_of_50_185_Sexual_Contacts/138130", "title"=>"Simulated Epidemics in an Empirical Spatiotemporal Network of 50,185 Sexual Contacts", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2011-03-17 02:15:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/791039"], "description"=>"<p>The increase in the number of infected vertices, Ω, using the simulated SI<b><sub>1</sub></b>I<b><sub>2</sub></b> model in panel A original network and panel B in a randomized version (RDT network).</p>", "links"=>[], "tags"=>["outbreak"], "article_id"=>461427, "categories"=>["Computational Biology", "Physics", "Neuroscience", "Medicine"], "users"=>["Luis E. C. Rocha", "Fredrik Liljeros", "Petter Holme"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1001109.g007", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Evolution_of_the_outbreak_size_for_SI_1_I_2_model_/461427", "title"=>"Evolution of the outbreak size for SI<sub>1</sub>I<sub>2</sub> model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-17 00:23:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/790897"], "description"=>"<p>Outbreak size versus transmission probabilities for (A–C) SI and (D–F) SIR epidemic models. Each panel shows the results for the empirical and for a random network. The abscissa is in log-scale.</p>", "links"=>[], "tags"=>["probability", "outbreak", "si"], "article_id"=>461270, "categories"=>["Computational Biology", "Physics", "Neuroscience", "Medicine"], "users"=>["Luis E. C. Rocha", "Fredrik Liljeros", "Petter Holme"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1001109.g005", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Transmission_probability_and_outbreak_size_for_SI_and_SIR_models_/461270", "title"=>"Transmission probability and outbreak size for SI and SIR models.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-17 00:21:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/790742"], "description"=>"<p>The panel shows the evolution of 〈Ω〉<sub>rel</sub>, the number of infected vertices for lower transmission probabilities (0.001≤ρ≤0.3) relative to the number of infected vertices when we use the maximum transmission probability (ρ = 1). The ordinate is in log-scale.</p>", "links"=>[], "tags"=>["probabilities", "si"], "article_id"=>461114, "categories"=>["Computational Biology", "Physics", "Neuroscience", "Medicine"], "users"=>["Luis E. C. Rocha", "Fredrik Liljeros", "Petter Holme"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1001109.g002", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Evolution_of_the_infection_for_low_transmission_probabilities_in_the_SI_model_/461114", "title"=>"Evolution of the infection for low transmission probabilities in the SI model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-17 00:18:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/791002"], "description"=>"<p>Panel A shows the average outbreak size 〈Ω〉 as a function of the duration of the infective stage <i>δ</i>. The line is a second-order polynomial fit to the data in the interval 42 days ≤<i>δ</i>≤112 days. Panel B shows the time of such crossing point as a function of the beginning of the sampling window, <i>T</i><sub>0</sub>.</p>", "links"=>[], "tags"=>["infectious diseases/sexually transmitted diseases", "physics/interdisciplinary physics", "public health and epidemiology/epidemiology", "public health and epidemiology/infectious diseases"], "article_id"=>461370, "categories"=>["Computational Biology", "Physics", "Neuroscience", "Medicine"], "users"=>["Luis E. C. Rocha", "Fredrik Liljeros", "Petter Holme"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1001109.g006", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Epidemic_threshold_for_SIR_model_for_961_8202_8202_1_/461370", "title"=>"Epidemic threshold for SIR model for ρ = 1.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-17 00:22:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/790681"], "description"=>"<p>In A–C, we plot the time evolution of the fraction of infected vertices 〈Ω〉. The curves correspond to SI epidemics in the original network (full line) and in its randomized versions: panel A represents swapping time stamps (RD); B shows rewiring of the edges and keeping the sellers' time correlations (RT); and panel C depicts simultaneous randomization of time stamps and edges (RDT).</p>", "links"=>[], "tags"=>["topological", "correlations"], "article_id"=>461055, "categories"=>["Computational Biology", "Physics", "Neuroscience", "Medicine"], "users"=>["Luis E. C. Rocha", "Fredrik Liljeros", "Petter Holme"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1001109.g001", "stats"=>{"downloads"=>4, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Temporal_and_topological_correlations_effect_on_epidemics_/461055", "title"=>"Temporal and topological correlations effect on epidemics.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-17 00:17:35"}

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Relative Metric

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