The Impact of the Unstructured Contacts Component in Influenza Pandemic Modeling
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{"title"=>"The impact of the unstructured contacts component in influenza pandemic modeling", "type"=>"journal", "authors"=>[{"first_name"=>"Marco", "last_name"=>"Ajelli", "scopus_author_id"=>"36024857000"}, {"first_name"=>"Stefano", "last_name"=>"Merler", "scopus_author_id"=>"6602452903"}], "year"=>2008, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"22423310", "doi"=>"10.1371/journal.pone.0001519", "sgr"=>"45149124404", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "scopus"=>"2-s2.0-45149124404", "issn"=>"19326203", "pui"=>"351832967"}, "id"=>"93b078b2-b0b0-337c-8650-2f6d372c614c", "abstract"=>"BACKGROUND: Individual based models have become a valuable tool for modeling the spatiotemporal dynamics of epidemics, e.g. influenza pandemic, and for evaluating the effectiveness of intervention strategies. While specific contacts among individuals into diverse environments (family, school/workplace) can be modeled in a standard way by employing available socio-demographic data, all the other (unstructured) contacts can be dealt with by adopting very different approaches. This can be achieved for instance by employing distance-based models or by choosing unstructured contacts in the local communities or by employing commuting data.\\n\\nMETHODS/RESULTS: Here we show how diverse choices can lead to different model outputs and thus to a different evaluation of the effectiveness of the containment/mitigation strategies. Sensitivity analysis has been conducted for different values of the first generation index G(0), which is the average number of secondary infections generated by the first infectious individual in a completely susceptible population and by varying the seeding municipality. Among the different considered models, attack rate ranges from 19.1% to 25.7% for G(0) = 1.1, from 47.8% to 50.7% for G(0) = 1.4 and from 62.4% to 67.8% for G(0) = 1.7. Differences of about 15 to 20 days in the peak day have been observed. As regards spatial diffusion, a difference of about 100 days to cover 200 km for different values of G(0) has been observed.\\n\\nCONCLUSION: To reduce uncertainty in the models it is thus important to employ data, which start being available, on contacts on neglected but important activities (leisure time, sport mall, restaurants, etc.) and time-use data for improving the characterization of the unstructured contacts. Moreover, all the possible effects of different assumptions should be considered for taking public health decisions: not only sensitivity analysis to various model parameters should be performed, but intervention options should be based on the analysis and comparison of different modeling choices.", "link"=>"http://www.mendeley.com/research/impact-unstructured-contacts-component-influenza-pandemic-modeling", "reader_count"=>31, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Librarian"=>1, "Researcher"=>9, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>7, "Other"=>1, "Student > Bachelor"=>2, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>3, "Professor"=>4}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Librarian"=>1, "Researcher"=>9, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>7, "Other"=>1, "Student > Bachelor"=>2, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>3, "Professor"=>4}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Engineering"=>1, "Nursing and Health Professions"=>1, "Mathematics"=>5, "Agricultural and Biological Sciences"=>8, "Medicine and Dentistry"=>5, "Sports and Recreations"=>1, "Physics and Astronomy"=>3, "Psychology"=>1, "Computer Science"=>2, "Immunology and Microbiology"=>1, "Economics, Econometrics and Finance"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>5}, "Sports and Recreations"=>{"Sports and Recreations"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>3}, "Psychology"=>{"Psychology"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>8}, "Computer Science"=>{"Computer Science"=>2}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Mathematics"=>{"Mathematics"=>5}, "Unspecified"=>{"Unspecified"=>2}}, "reader_count_by_country"=>{"United States"=>1, "France"=>2, "Australia"=>1, "India"=>1}, "group_count"=>3}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/462307", "https://ndownloader.figshare.com/files/462359", "https://ndownloader.figshare.com/files/462382", "https://ndownloader.figshare.com/files/462409", "https://ndownloader.figshare.com/files/462458", "https://ndownloader.figshare.com/files/462487", "https://ndownloader.figshare.com/files/462506", "https://ndownloader.figshare.com/files/462531", "https://ndownloader.figshare.com/files/462558", "https://ndownloader.figshare.com/files/462589", "https://ndownloader.figshare.com/files/462617", "https://ndownloader.figshare.com/files/462638"], "description"=>"<div><h3>Background</h3><p>Individual based models have become a valuable tool for modeling the spatiotemporal dynamics of epidemics, e.g. influenza pandemic, and for evaluating the effectiveness of intervention strategies. While specific contacts among individuals into diverse environments (family, school/workplace) can be modeled in a standard way by employing available socio-demographic data, all the other (unstructured) contacts can be dealt with by adopting very different approaches. This can be achieved for instance by employing distance-based models or by choosing unstructured contacts in the local communities or by employing commuting data.</p><h3>Methods/Results</h3><p>Here we show how diverse choices can lead to different model outputs and thus to a different evaluation of the effectiveness of the containment/mitigation strategies. Sensitivity analysis has been conducted for different values of the first generation index <em>G<sub>0 </sub>,</em> which is the average number of secondary infections generated by the first infectious individual in a completely susceptible population and by varying the seeding municipality. Among the different considered models, attack rate ranges from 19.1% to 25.7% for G<em><sub>0</sub></em> = 1.1, from 47.8% to 50.7% for <em>G<sub>0</sub></em> = 1.4 and from 62.4% to 67.8% for <em>G<sub>0</sub></em> = 1.7. Differences of about 15 to 20 days in the peak day have been observed. As regards spatial diffusion, a difference of about 100 days to cover 200 km for different values of <em>G<sub>0</sub></em> has been observed.</p><h3>Conclusion</h3><p>To reduce uncertainty in the models it is thus important to employ data, which start being available, on contacts on neglected but important activities (leisure time, sport mall, restaurants, etc.) and time-use data for improving the characterization of the unstructured contacts. Moreover, all the possible effects of different assumptions should be considered for taking public health decisions: not only sensitivity analysis to various model parameters should be performed, but intervention options should be based on the analysis and comparison of different modeling choices.</p></div>", "links"=>[], "tags"=>["unstructured", "contacts", "influenza", "pandemic", "modeling"], "article_id"=>151113, "categories"=>["Mathematics", "Medicine"], "users"=>["Marco Ajelli", "Stefano Merler"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0001519.s001", "https://dx.doi.org/10.1371/journal.pone.0001519.s002", "https://dx.doi.org/10.1371/journal.pone.0001519.s003", "https://dx.doi.org/10.1371/journal.pone.0001519.s004", "https://dx.doi.org/10.1371/journal.pone.0001519.s005", "https://dx.doi.org/10.1371/journal.pone.0001519.s006", "https://dx.doi.org/10.1371/journal.pone.0001519.s007", "https://dx.doi.org/10.1371/journal.pone.0001519.s008", "https://dx.doi.org/10.1371/journal.pone.0001519.s009", "https://dx.doi.org/10.1371/journal.pone.0001519.s010", "https://dx.doi.org/10.1371/journal.pone.0001519.s011", "https://dx.doi.org/10.1371/journal.pone.0001519.s012"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/The_Impact_of_the_Unstructured_Contacts_Component_in_Influenza_Pandemic_Modeling/151113", "title"=>"The Impact of the Unstructured Contacts Component in Influenza Pandemic Modeling", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2008-01-30 00:18:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/939776"], "description"=>"<p>Spreading time from the seeding municipality as a function of the distance for different values of <i>G<sub>0</sub></i> and different seeding municipalities: Rome (first row), Cagliari (second row), a small isolated village (third row). Model M in orange, M+T in red, L in cyan, L+T in blue, and S in green.</p>", "links"=>[], "tags"=>["seeding", "municipality", "rome", "cagliari"], "article_id"=>610217, "categories"=>["Mathematics", "Medicine"], "users"=>["Marco Ajelli", "Stefano Merler"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0001519.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spreading_time_from_the_seeding_municipality_as_a_function_of_the_distance_for_different_values_of_G_0_and_different_seeding_municipalities_Rome_first_row_Cagliari_second_row_a_small_isolated_village_third_row_Model_M_in_orange_M_T_in_red_L_in_cyan_L_T_i/610217", "title"=>"Spreading time from the seeding municipality as a function of the distance for different values of <i>G<sub>0</sub></i> and different seeding municipalities: Rome (first row), Cagliari (second row), a small isolated village (third row). Model M in orange, M+T in red, L in cyan, L+T in blue, and S in green.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-01-30 00:03:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/939939"], "description"=>"<p>Infection is seeded in Cagliari (Sardinia island) and <i>G<sub>0</sub></i> = 1.7. Coloured areas (model M in orange, M+T in red, L in cyan, L+T in blue, and S in green) indicate presence of at least one infected, infectious or removed individual.</p>", "links"=>[], "tags"=>["40", "50", "60"], "article_id"=>610385, "categories"=>["Mathematics", "Medicine"], "users"=>["Marco Ajelli", "Stefano Merler"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0001519.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spatiotemporal_dynamics_at_40_left_50_center_and_60_right_days_/610385", "title"=>"Spatiotemporal dynamics at 40 (left), 50 (center) and 60 (right) days.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-01-30 00:06:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/940091"], "description"=>"<p>Colored areas (model M in orange, M+T in red, L in cyan, L+T in blue, and S in green) indicate presence of at least one infected, infectious or removed individual.</p>", "links"=>[], "tags"=>["40", "60", "corresponding", "exponential", "seeded", "rome"], "article_id"=>610538, "categories"=>["Mathematics", "Medicine"], "users"=>["Marco Ajelli", "Stefano Merler"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0001519.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spatiotemporal_dynamics_at_40_on_the_left_and_60_on_the_right_days_roughly_corresponding_to_the_begin_and_the_end_of_the_exponential_growth_phase_Infection_is_seeded_in_Rome_and_G_0__1_7_/610538", "title"=>"Spatiotemporal dynamics at 40 (on the left) and 60 (on the right) days, roughly corresponding to the begin and the end of the exponential growth phase. Infection is seeded in Rome and <i>G<sub>0</sub></i> = 1.7.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-01-30 00:08:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/940179"], "description"=>"<p>Simulation are initialized with 30 infected individuals, to reduce the stochastic variability observed in first days of the epidemic. Models are: M in orange, M+T in red, L in cyan, L+T in blue, and S in green.</p>", "links"=>[], "tags"=>["cases", "seeding", "rome", "cagliari", "italy"], "article_id"=>610622, "categories"=>["Mathematics", "Medicine"], "users"=>["Marco Ajelli", "Stefano Merler"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0001519.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_of_daily_cases_for_different_values_of_the_first_generation_index_and_different_seeding_municipalities_Rome_first_row_Cagliari_second_row_a_small_isolated_village_in_the_north_of_Italy_third_row_/610622", "title"=>"Number of daily cases for different values of the first generation index and different seeding municipalities: Rome (first row), Cagliari (second row), a small isolated village in the north of Italy (third row).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-01-30 00:10:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/940312"], "description"=>"<p>Models M in orange, L in cyan and S in green.</p>", "links"=>[], "tags"=>["infected"], "article_id"=>610761, "categories"=>["Mathematics", "Medicine"], "users"=>["Marco Ajelli", "Stefano Merler"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0001519.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Proportion_of_the_infected_population_by_age_for_G_0__1_4_top_and_G_0__1_7_bottom_/610761", "title"=>"Proportion of the infected population by age for <i>G<sub>0</sub></i> = 1.4 (top) and <i>G<sub>0</sub></i> = 1.7 (bottom).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-01-30 00:12:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/940384"], "description"=>"<p>Final attack rates (with standard deviation) of the different models considered for different <i>G<sub>0</sub></i> values.</p>", "links"=>[], "tags"=>["mathematics", "infectious diseases/epidemiology and control of infectious diseases"], "article_id"=>610829, "categories"=>["Mathematics", "Medicine"], "users"=>["Marco Ajelli", "Stefano Merler"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0001519.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Final_attack_rates_/610829", "title"=>"Final attack rates", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2008-01-30 00:13:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/940408"], "description"=>"<p>Basic reproductive numbers <i>R<sub>0</sub></i> (with standard deviation) of the different models considered for different <i>G<sub>0</sub></i> values.</p>", "links"=>[], "tags"=>["reproductive"], "article_id"=>610850, "categories"=>["Mathematics", "Medicine"], "users"=>["Marco Ajelli", "Stefano Merler"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0001519.t002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Basic_reproductive_numbers_/610850", "title"=>"Basic reproductive numbers", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2008-01-30 00:14:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/940427"], "description"=>"<p>Peak day (with standard deviation) of the different models considered for different <i>G<sub>0</sub></i> values.</p>", "links"=>[], "tags"=>["mathematics", "infectious diseases/epidemiology and control of infectious diseases"], "article_id"=>610870, "categories"=>["Mathematics", "Medicine"], "users"=>["Marco Ajelli", "Stefano Merler"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0001519.t003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Peak_day_/610870", "title"=>"Peak day", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2008-01-30 00:14:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/940458"], "description"=>"<p>Peak day (with standard deviation) for different seeding municipality and different values of the first generation index <i>G<sub>0</sub></i>. Roma is the largest city of Italy (2,546,804 inhabitants), located in the central Italy; Cagliari is a city (164,249 inhabitants) in the Sardinia island; Luserna is a small isolated village (297 inhabitants) in the northern of Italy; Turin is a big city (865,263 inhabitants), located in the northern Italy; Vieste is a small town (13,430 inhabitants) in the southern Italy.</p>", "links"=>[], "tags"=>["seeding"], "article_id"=>610896, "categories"=>["Mathematics", "Medicine"], "users"=>["Marco Ajelli", "Stefano Merler"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0001519.t004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Peak_day_for_different_seeding_municipality_/610896", "title"=>"Peak day for different seeding municipality", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2008-01-30 00:14:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/940478"], "description"=>"<p>Probability (with standard deviation) of a large outbreak for the different models considered and for different <i>G<sub>0</sub></i> values.</p>", "links"=>[], "tags"=>["mathematics", "infectious diseases/epidemiology and control of infectious diseases"], "article_id"=>610916, "categories"=>["Mathematics", "Medicine"], "users"=>["Marco Ajelli", "Stefano Merler"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0001519.t005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Probability_of_a_large_outbreak_/610916", "title"=>"Probability of a large outbreak", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2008-01-30 00:15:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/940503"], "description"=>"<p>Transmission rates estimated for the five models considered and for three different values of the first generation index. β<i><sub>h</sub></i>, β<i><sub>s.</sub></i> and β<i><sub>w</sub></i> are the transmission rates in households, schools and workplaces respectively. β<i><sub>u</sub></i> is the transmission rate for unstructured contacts (details are given in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0001519#pone.0001519.s001\" target=\"_blank\">Text S1</a>).</p>", "links"=>[], "tags"=>["mathematics", "infectious diseases/epidemiology and control of infectious diseases"], "article_id"=>610949, "categories"=>["Mathematics", "Medicine"], "users"=>["Marco Ajelli", "Stefano Merler"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0001519.t006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Transmission_rates_/610949", "title"=>"Transmission rates", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2008-01-30 00:15:49"}

PMC Usage Stats | Further Information

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Relative Metric

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