Bursty Communication Patterns Facilitate Spreading in a Threshold-Based Epidemic Dynamics
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{"title"=>"Bursty Communication Patterns Facilitate Spreading in a Threshold-Based Epidemic Dynamics", "type"=>"journal", "authors"=>[{"first_name"=>"Taro", "last_name"=>"Takaguchi", "scopus_author_id"=>"36624362900"}, {"first_name"=>"Naoki", "last_name"=>"Masuda", "scopus_author_id"=>"55413167500"}, {"first_name"=>"Petter", "last_name"=>"Holme", "scopus_author_id"=>"7003326827"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "arxiv"=>"1206.2097", "scopus"=>"2-s2.0-84880445836", "pui"=>"369379119", "doi"=>"10.1371/journal.pone.0068629", "isbn"=>"1932-6203", "sgr"=>"84880445836", "pmid"=>"23894326"}, "id"=>"a269f638-d762-367f-853d-1863576160c4", "abstract"=>"Records of social interactions provide us with new sources of data for understanding how interaction patterns affect collective dynamics. Such human activity patterns are often bursty, i.e., they consist of short periods of intense activity followed by long periods of silence. This burstiness has been shown to affect spreading phenomena; it accelerates epidemic spreading in some cases and slows it down in other cases. We investigate a model of history-dependent contagion. In our model, repeated interactions between susceptible and infected individuals in a short period of time is needed for a susceptible individual to contract infection. We carry out numerical simulations on real temporal network data to find that bursty activity patterns facilitate epidemic spreading in our model.", "link"=>"http://www.mendeley.com/research/bursty-communication-patterns-facilitate-spreading-thresholdbased-epidemic-dynamics", "reader_count"=>45, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>3, "Researcher"=>13, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>18, "Student > Master"=>7, "Professor"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>3, "Researcher"=>13, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>18, "Student > Master"=>7, "Professor"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>4, "Unspecified"=>5, "Environmental Science"=>1, "Mathematics"=>5, "Agricultural and Biological Sciences"=>2, "Physics and Astronomy"=>16, "Computer Science"=>10, "Decision Sciences"=>1, "Social Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>4}, "Social Sciences"=>{"Social Sciences"=>1}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>16}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>2}, "Computer Science"=>{"Computer Science"=>10}, "Mathematics"=>{"Mathematics"=>5}, "Unspecified"=>{"Unspecified"=>5}, "Environmental Science"=>{"Environmental Science"=>1}}, "reader_count_by_country"=>{"United States"=>1, "Japan"=>3, "United Kingdom"=>1, "Switzerland"=>3, "Spain"=>2}, "group_count"=>1}

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Relative Metric

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