A Metric of Influential Spreading during Contagion Dynamics through the Air Transportation Network
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{"title"=>"A metric of influential spreading during contagion dynamics through the air transportation network", "type"=>"journal", "authors"=>[{"first_name"=>"Christos", "last_name"=>"Nicolaides", "scopus_author_id"=>"36911423800"}, {"first_name"=>"Luis", "last_name"=>"Cueto-Felgueroso", "scopus_author_id"=>"23567554100"}, {"first_name"=>"Marta C.", "last_name"=>"González", "scopus_author_id"=>"36913172600"}, {"first_name"=>"Ruben", "last_name"=>"Juanes", "scopus_author_id"=>"15053200600"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"22829902", "doi"=>"10.1371/journal.pone.0040961", "sgr"=>"84864053195", "isbn"=>"1932-6203", "scopus"=>"2-s2.0-84864053195", "issn"=>"19326203", "pui"=>"365284959"}, "id"=>"e60774b1-ebc4-392e-b706-72be5c86aacc", "abstract"=>"The spread of infectious diseases at the global scale is mediated by long-range human travel. Our ability to predict the impact of an outbreak on human health requires understanding the spatiotemporal signature of early-time spreading from a specific location. Here, we show that network topology, geography, traffic structure and individual mobility patterns are all essential for accurate predictions of disease spreading. Specifically, we study contagion dynamics through the air transportation network by means of a stochastic agent-tracking model that accounts for the spatial distribution of airports, detailed air traffic and the correlated nature of mobility patterns and waiting-time distributions of individual agents. From the simulation results and the empirical air-travel data, we formulate a metric of influential spreading--the geographic spreading centrality--which accounts for spatial organization and the hierarchical structure of the network traffic, and provides an accurate measure of the early-time spreading power of individual nodes.", "link"=>"http://www.mendeley.com/research/metric-influential-spreading-during-contagion-dynamics-through-air-transportation-network", "reader_count"=>73, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>9, "Librarian"=>1, "Researcher"=>11, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>22, "Student > Postgraduate"=>3, "Student > Master"=>10, "Other"=>1, "Student > Bachelor"=>2, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>3, "Professor"=>6}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>9, "Librarian"=>1, "Researcher"=>11, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>22, "Student > Postgraduate"=>3, "Student > Master"=>10, "Other"=>1, "Student > Bachelor"=>2, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>3, "Professor"=>6}, "reader_count_by_subject_area"=>{"Unspecified"=>6, "Agricultural and Biological Sciences"=>10, "Veterinary Science and Veterinary Medicine"=>2, "Business, Management and Accounting"=>1, "Computer Science"=>11, "Earth and Planetary Sciences"=>2, "Engineering"=>7, "Environmental Science"=>2, "Biochemistry, Genetics and Molecular Biology"=>1, "Mathematics"=>4, "Medicine and Dentistry"=>7, "Physics and Astronomy"=>11, "Social Sciences"=>6, "Immunology and Microbiology"=>1, "Linguistics"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>7}, "Social Sciences"=>{"Social Sciences"=>6}, "Physics and Astronomy"=>{"Physics and Astronomy"=>11}, "Mathematics"=>{"Mathematics"=>4}, "Unspecified"=>{"Unspecified"=>6}, "Environmental Science"=>{"Environmental Science"=>2}, "Engineering"=>{"Engineering"=>7}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>2}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>10}, "Computer Science"=>{"Computer Science"=>11}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Linguistics"=>{"Linguistics"=>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"=>{"Colombia"=>1, "United States"=>6, "Sri Lanka"=>1, "United Kingdom"=>1, "Malaysia"=>1, "Thailand"=>1, "Portugal"=>1, "Switzerland"=>1, "Spain"=>1, "Brazil"=>2, "Italy"=>1, "Israel"=>1, "Chile"=>1}, "group_count"=>5}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/606135"], "description"=>"<p>(<i>a</i>) World map with the location of the 1833 airports in the US database from the Federal Aviation Administration (<a href=\"http://www.faa.gov\" target=\"_blank\">www.faa.gov</a>). (<i>b</i>) Waiting time distributions at connecting and destination airports (from <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040961#pone.0040961-Barnhart1\" target=\"_blank\">[26]</a>), and at the “home” airport. (<i>c</i>) Illustration of a 1-year travel history of an individual with “home” at San Francisco International Airport (SFO). (<i>d</i>) Graphical representation of the probabilities for exploration and preferential visit of the same individual, after the 1-year “training period.” During exploration the agent visits a new airport while during preferential visit the agent visits a previously-visited place with probability proportional to the frequency of previous visits to that location.</p>", "links"=>[], "tags"=>["elements", "empirical", "mobility"], "article_id"=>276625, "categories"=>["Physics", "Biological Sciences", "Infectious Diseases", "Biotechnology"], "users"=>["Christos Nicolaides", "Luis Cueto-Felgueroso", "Marta C. González", "Ruben Juanes"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0040961.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pictorial_view_of_the_key_elements_of_our_empirical_model_of_human_mobility_through_the_air_transportation_network_/276625", "title"=>"Pictorial view of the key elements of our empirical model of human mobility through the air transportation network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 02:12:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/606253"], "description"=>"<p>We used a value of the recovery rate days. We initialized the epidemic with 10 infected individuals chosen randomly across the network. We used a population of individuals, and average our results over 200 realizations. (Inset) The global attack for larger values of exhibits smaller differences among models, except for those between annealed and quenched transition rates at the nodes, as evidenced by the simulation results of Model 1 vs. the other models.</p>", "links"=>[], "tags"=>["carlo", "reproductive", "models", "explained"], "article_id"=>276740, "categories"=>["Physics", "Biological Sciences", "Infectious Diseases", "Biotechnology"], "users"=>["Christos Nicolaides", "Luis Cueto-Felgueroso", "Marta C. González", "Ruben Juanes"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0040961.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Monte_Carlo_study_of_the_global_attack_of_an_epidemic_as_a_function_of_the_reproductive_number_for_the_different_models_explained_in_the_text_/276740", "title"=>"Monte Carlo study of the global attack of an epidemic as a function of the reproductive number , for the different models explained in the text.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 02:12:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/606327"], "description"=>"<p>(<i>a</i>) Global attack as a function of reproductive number, for five different airports (see inset). We initialize the disease by infecting 10 randomly chosen individuals inside the subpopulation of consideration. We use days. Each point is the result of a Monte Carlo study averaging over 200 reaction and 20 mobility realizations and using individuals. (<i>b</i>) Ranking of the 40 major airports in US in terms of their spreading ability measured by the normalized global attack. We compare the normalized global-attack ranking curve (black diamonds) to the ones that result from considering the airport’s normalized degree (magenta squares) and the airport’s normalized traffic (brown triangles). Also shown is the ranking of the airports shown in (<i>a</i>). Both degree and traffic provide effective rankings of influential late-time spreaders, which in this case can be understood from the good cross-correlation between the two (inset).</p>", "links"=>[], "tags"=>["spreading", "originates"], "article_id"=>276826, "categories"=>["Physics", "Biological Sciences", "Infectious Diseases", "Biotechnology"], "users"=>["Christos Nicolaides", "Luis Cueto-Felgueroso", "Marta C. González", "Ruben Juanes"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0040961.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Late_time_spreading_ability_of_different_airports_measured_by_the_global_attack_of_an_SIR_epidemic_that_originates_at_each_airport_/276826", "title"=>"Late-time spreading ability of different airports, measured by the global attack of an SIR epidemic that originates at each airport.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 02:13:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/606400"], "description"=>"<p>We initialize the disease by infecting 10 individuals from each specific airport (see inset), and use days. Each point is the result of a Monte Carlo study averaging over 100 reaction and 20 mobility realizations and using individuals. (Inset) Graphical representation of the mean position of infected individuals, 10 days after the outbreak from three different locations. The circle radius denotes the geographic extension of the infectious cloud (as measured by the square root of the Mean Square Displacement <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040961#pone.0040961-Nicolaides1\" target=\"_blank\">[12]</a> of infected individuals) while their color represents the number of infected at the same time (dark colors denote large number of infected).</p>", "links"=>[], "tags"=>["spreaders", "normalized", "early-time", "displacement", "infectious"], "article_id"=>276890, "categories"=>["Physics", "Biological Sciences", "Infectious Diseases", "Biotechnology"], "users"=>["Christos Nicolaides", "Luis Cueto-Felgueroso", "Marta C. González", "Ruben Juanes"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0040961.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Ranking_of_influential_spreaders_by_the_normalized_early_time_mean_square_displacement_of_infectious_individuals_/276890", "title"=>"Ranking of influential spreaders by the normalized early-time mean square displacement of infectious individuals.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 02:13:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/606509"], "description"=>"<p>(<i>a</i>) for different reproductive numbers, 10 days after the disease is initiated. (<i>b</i>) at different times after the initiation of the disease. We use and days. Each point in the above plots is the result of a Monte Carlo study averaging over 100 reaction and 20 mobility realizations and using individuals.</p>", "links"=>[], "tags"=>["spreaders", "normalized", "early-time"], "article_id"=>277011, "categories"=>["Physics", "Biological Sciences", "Infectious Diseases", "Biotechnology"], "users"=>["Christos Nicolaides", "Luis Cueto-Felgueroso", "Marta C. González", "Ruben Juanes"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0040961.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Ranking_of_influential_spreaders_by_the_normalized_early_time_Total_Square_Displacement_/277011", "title"=>"Ranking of influential spreaders by the normalized early-time Total Square Displacement.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 02:14:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/606599"], "description"=>"<p>Shown are the results from the model simulations (black triangles), and comparison with the ranking provided by existing metrics of centrality and late-time influential spreading. (<i>a</i>) Normalized degree. (<i>b</i>) Normalized traffic. (<i>c</i>) Normalized betweenness centrality. (<i>d</i>) Normalized -shell centrality.</p>", "links"=>[], "tags"=>["early-time", "spreaders"], "article_id"=>277094, "categories"=>["Physics", "Biological Sciences", "Infectious Diseases", "Biotechnology"], "users"=>["Christos Nicolaides", "Luis Cueto-Felgueroso", "Marta C. González", "Ruben Juanes"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0040961.g006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Ranking_of_influential_early_time_spreaders_by_existing_metrics_/277094", "title"=>"Ranking of influential early-time spreaders by existing metrics.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 02:14:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/606754"], "description"=>"<p>(<i>a</i>) Shown is the TSD-ranking for individual realizations of two null networks testing the influence of (1) geographic locations of the nodes, and (2) heterogeneity in the traffic of the links. The dissimilarity between those rankings and that from the original network model strongly suggests that any effective measure of influential early-time spreaders must incorporate geography and traffic quenched disorder. (<i>b</i>) TSD-ranking for a simplified model of human mobility. Removing the detailed patterns of mobility affects the evolution of the predicted TSD (see inset for HNL airport) but does not affect the early-time spreading ranking significantly.</p>", "links"=>[], "tags"=>["spatial", "quenched", "mobility", "early-time"], "article_id"=>277245, "categories"=>["Physics", "Biological Sciences", "Infectious Diseases", "Biotechnology"], "users"=>["Christos Nicolaides", "Luis Cueto-Felgueroso", "Marta C. González", "Ruben Juanes"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0040961.g007"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Role_of_spatial_organization_traffic_quenched_disorder_and_mobility_patterns_on_early_time_spreading_/277245", "title"=>"Role of spatial organization, traffic quenched disorder, and mobility patterns, on early-time spreading.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 02:15:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/606831"], "description"=>"<p>The GSC metric predictions are in quantitative agreement with the results from the Monte Carlo study on the empirical model.</p>", "links"=>[], "tags"=>["spreaders", "times", "geographic", "spreading", "centrality"], "article_id"=>277322, "categories"=>["Physics", "Biological Sciences", "Infectious Diseases", "Biotechnology"], "users"=>["Christos Nicolaides", "Luis Cueto-Felgueroso", "Marta C. González", "Ruben Juanes"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0040961.g008"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Ranking_of_influential_spreaders_at_early_times_from_the_geographic_spreading_centrality_GSC_/277322", "title"=>"Ranking of influential spreaders at early times from the geographic spreading centrality (GSC).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 02:16:18"}

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Relative Metric

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