Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases
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{"title"=>"Social contacts and mixing patterns relevant to the spread of infectious diseases", "type"=>"journal", "authors"=>[{"first_name"=>"Joël", "last_name"=>"Mossong", "scopus_author_id"=>"6602615798"}, {"first_name"=>"Niel", "last_name"=>"Hens", "scopus_author_id"=>"35233377500"}, {"first_name"=>"Mark", "last_name"=>"Jit", "scopus_author_id"=>"23990626200"}, {"first_name"=>"Philippe", "last_name"=>"Beutels", "scopus_author_id"=>"7004335473"}, {"first_name"=>"Kari", "last_name"=>"Auranen", "scopus_author_id"=>"6603530742"}, {"first_name"=>"Rafael", "last_name"=>"Mikolajczyk", "scopus_author_id"=>"6603079683"}, {"first_name"=>"Marco", "last_name"=>"Massari", "scopus_author_id"=>"12240959300"}, {"first_name"=>"Stefania", "last_name"=>"Salmaso", "scopus_author_id"=>"7005363010"}, {"first_name"=>"Gianpaolo Scalia", "last_name"=>"Tomba", "scopus_author_id"=>"22136694200"}, {"first_name"=>"Jacco", "last_name"=>"Wallinga", "scopus_author_id"=>"7003807945"}, {"first_name"=>"Janneke", "last_name"=>"Heijne", "scopus_author_id"=>"23990865400"}, {"first_name"=>"Malgorzata", "last_name"=>"Sadkowska-Todys", "scopus_author_id"=>"6602753073"}, {"first_name"=>"Magdalena", "last_name"=>"Rosinska", "scopus_author_id"=>"6602874055"}, {"first_name"=>"W. John", "last_name"=>"Edmunds", "scopus_author_id"=>"7006629172"}], "year"=>2008, "source"=>"PLoS Medicine", "identifiers"=>{"sgr"=>"41549103774", "pmid"=>"18366252", "arxiv"=>"arXiv:1011.1669v3", "pui"=>"351471689", "isbn"=>"1549-1676 (Electronic)\\r1549-1277 (Linking)", "scopus"=>"2-s2.0-41549103774", "doi"=>"10.1371/journal.pmed.0050074", "issn"=>"15491277"}, "id"=>"8cfc14a0-1bf2-3030-832a-86107c0989da", "abstract"=>"BACKGROUND Mathematical modelling of infectious diseases transmitted by the respiratory or close-contact route (e.g., pandemic influenza) is increasingly being used to determine the impact of possible interventions. Although mixing patterns are known to be crucial determinants for model outcome, researchers often rely on a priori contact assumptions with little or no empirical basis. We conducted a population-based prospective survey of mixing patterns in eight European countries using a common paper-diary methodology. METHODS AND FINDINGS 7,290 participants recorded characteristics of 97,904 contacts with different individuals during one day, including age, sex, location, duration, frequency, and occurrence of physical contact. We found that mixing patterns and contact characteristics were remarkably similar across different European countries. Contact patterns were highly assortative with age: schoolchildren and young adults in particular tended to mix with people of the same age. Contacts lasting at least one hour or occurring on a daily basis mostly involved physical contact, while short duration and infrequent contacts tended to be nonphysical. Contacts at home, school, or leisure were more likely to be physical than contacts at the workplace or while travelling. Preliminary modelling indicates that 5- to 19-year-olds are expected to suffer the highest incidence during the initial epidemic phase of an emerging infection transmitted through social contacts measured here when the population is completely susceptible. CONCLUSIONS To our knowledge, our study provides the first large-scale quantitative approach to contact patterns relevant for infections transmitted by the respiratory or close-contact route, and the results should lead to improved parameterisation of mathematical models used to design control strategies.", "link"=>"http://www.mendeley.com/research/social-contacts-mixing-patterns-relevant-spread-infectious-diseases", "reader_count"=>578, "reader_count_by_academic_status"=>{"Unspecified"=>16, "Professor > Associate Professor"=>35, "Librarian"=>2, "Researcher"=>158, "Student > Doctoral Student"=>30, "Student > Ph. D. 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Student"=>141, "Student > Postgraduate"=>25, "Student > Master"=>77, "Other"=>18, "Student > Bachelor"=>33, "Lecturer"=>12, "Lecturer > Senior Lecturer"=>9, "Professor"=>21}, "reader_count_by_subject_area"=>{"Unspecified"=>51, "Agricultural and Biological Sciences"=>133, "Business, Management and Accounting"=>8, "Veterinary Science and Veterinary Medicine"=>4, "Chemistry"=>3, "Computer Science"=>42, "Decision Sciences"=>2, "Earth and Planetary Sciences"=>2, "Economics, Econometrics and Finance"=>4, "Engineering"=>8, "Environmental Science"=>10, "Nursing and Health Professions"=>4, "Biochemistry, Genetics and Molecular Biology"=>12, "Materials Science"=>1, "Mathematics"=>81, "Medicine and Dentistry"=>144, "Neuroscience"=>1, "Physics and Astronomy"=>18, "Psychology"=>5, "Social Sciences"=>33, "Immunology and Microbiology"=>11}, "reader_count_by_subdiscipline"=>{"Materials Science"=>{"Materials Science"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>144}, "Social Sciences"=>{"Social Sciences"=>33}, "Decision Sciences"=>{"Decision Sciences"=>2}, "Physics and Astronomy"=>{"Physics and Astronomy"=>18}, "Psychology"=>{"Psychology"=>5}, "Mathematics"=>{"Mathematics"=>81}, "Unspecified"=>{"Unspecified"=>51}, "Environmental Science"=>{"Environmental Science"=>10}, "Engineering"=>{"Engineering"=>8}, "Chemistry"=>{"Chemistry"=>3}, "Neuroscience"=>{"Neuroscience"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>2}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>4}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>11}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>133}, "Computer Science"=>{"Computer Science"=>42}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>8}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>4}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>12}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>4}}, "reader_count_by_country"=>{"Puerto Rico"=>1, "United States"=>26, "Japan"=>1, "Philippines"=>1, "United Kingdom"=>13, "Kenya"=>3, "Portugal"=>1, "Switzerland"=>2, "Spain"=>1, "India"=>2, "Canada"=>3, "Austria"=>2, "Vietnam"=>2, "Brazil"=>3, "Mexico"=>2, "Italy"=>2, "Israel"=>1, "France"=>6, "Nigeria"=>2, "Australia"=>7, "Chile"=>1, "Germany"=>3}, "group_count"=>40}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/460703", "https://ndownloader.figshare.com/files/460729", "https://ndownloader.figshare.com/files/460810", "https://ndownloader.figshare.com/files/460859", "https://ndownloader.figshare.com/files/460918", "https://ndownloader.figshare.com/files/460970", "https://ndownloader.figshare.com/files/461089", "https://ndownloader.figshare.com/files/461130", "https://ndownloader.figshare.com/files/461215", "https://ndownloader.figshare.com/files/461328"], "description"=>"<div><h3>Background</h3><p>Mathematical modelling of infectious diseases transmitted by the respiratory or close-contact route (e.g., pandemic influenza) is increasingly being used to determine the impact of possible interventions. Although mixing patterns are known to be crucial determinants for model outcome, researchers often rely on a priori contact assumptions with little or no empirical basis. We conducted a population-based prospective survey of mixing patterns in eight European countries using a common paper-diary methodology.</p> <h3>Methods and Findings</h3><p>7,290 participants recorded characteristics of 97,904 contacts with different individuals during one day, including age, sex, location, duration, frequency, and occurrence of physical contact. We found that mixing patterns and contact characteristics were remarkably similar across different European countries. Contact patterns were highly assortative with age: schoolchildren and young adults in particular tended to mix with people of the same age. Contacts lasting at least one hour or occurring on a daily basis mostly involved physical contact, while short duration and infrequent contacts tended to be nonphysical. Contacts at home, school, or leisure were more likely to be physical than contacts at the workplace or while travelling. Preliminary modelling indicates that 5- to 19-year-olds are expected to suffer the highest incidence during the initial epidemic phase of an emerging infection transmitted through social contacts measured here when the population is completely susceptible.</p> <h3>Conclusions</h3><p>To our knowledge, our study provides the first large-scale quantitative approach to contact patterns relevant for infections transmitted by the respiratory or close-contact route, and the results should lead to improved parameterisation of mathematical models used to design control strategies.</p> </div>", "links"=>[], "tags"=>["contacts", "mixing", "patterns", "infectious", "diseases"], "article_id"=>150785, "categories"=>["Mathematics", "Cancer", "Biotechnology"], "users"=>["Joël Mossong", "Niel Hens", "Mark Jit", "Philippe Beutels", "Kari Auranen", "Rafael Mikolajczyk", "Marco Massari", "Stefania Salmaso", "Gianpaolo Scalia Tomba", "Jacco Wallinga", "Janneke Heijne", "Malgorzata Sadkowska-Todys", "Magdalena Rosinska", "W. John Edmunds"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.0050074.sg001", "https://dx.doi.org/10.1371/journal.pmed.0050074.sg002", "https://dx.doi.org/10.1371/journal.pmed.0050074.sg003", "https://dx.doi.org/10.1371/journal.pmed.0050074.sg004", "https://dx.doi.org/10.1371/journal.pmed.0050074.st001", "https://dx.doi.org/10.1371/journal.pmed.0050074.st002", "https://dx.doi.org/10.1371/journal.pmed.0050074.st003", "https://dx.doi.org/10.1371/journal.pmed.0050074.st004", "https://dx.doi.org/10.1371/journal.pmed.0050074.st005", "https://dx.doi.org/10.1371/journal.pmed.0050074.sd001"], "stats"=>{"downloads"=>68, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Social_Contacts_and_Mixing_Patterns_Relevant_to_the_Spread_of_Infectious_Diseases/150785", "title"=>"Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2008-03-25 00:13:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/934939"], "description"=>"<p>Sampling weights were used for each country. “Other” refers to contacts made at locations other than home, work, school, travel, or leisure. “Multiple” refers to the fact that the person was contacted during the day in multiple locations, not just a single location.</p>", "links"=>[], "tags"=>["contacts"], "article_id"=>605370, "categories"=>["Biotechnology", "Mathematics", "Infectious Diseases"], "users"=>["Joël Mossong", "Niel Hens", "Mark Jit", "Philippe Beutels", "Kari Auranen", "Rafael Mikolajczyk", "Marco Massari", "Stefania Salmaso", "Gianpaolo Scalia Tomba", "Jacco Wallinga", "Janneke Heijne", "Malgorzata Sadkowska-Todys", "Magdalena Rosinska", "W. John Edmunds"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.0050074.g002", "stats"=>{"downloads"=>1, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_Distribution_by_Location_and_by_Country_of_A_All_Reported_Contacts_and_B_Physical_Contacts_Only_/605370", "title"=>"The Distribution by Location and by Country of (A) All Reported Contacts and (B) Physical Contacts Only", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-03-25 01:29:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/935291"], "description"=>"<p>For each country, we monitored incidence five generations of infection after the introduction of a single infected individual in the 65–70 age group; the incidence is normalized such that height of all bars sums to one for each country. (A) Results for all reported contacts; (B) for physical contacts only.</p>", "links"=>[], "tags"=>["incidence", "susceptible", "groups", "contacts", "observed"], "article_id"=>605719, "categories"=>["Biotechnology", "Mathematics", "Infectious Diseases"], "users"=>["Joël Mossong", "Niel Hens", "Mark Jit", "Philippe Beutels", "Kari Auranen", "Rafael Mikolajczyk", "Marco Massari", "Stefania Salmaso", "Gianpaolo Scalia Tomba", "Jacco Wallinga", "Janneke Heijne", "Malgorzata Sadkowska-Todys", "Magdalena Rosinska", "W. John Edmunds"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.0050074.g004", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relative_Incidence_of_a_New_Emerging_Infection_in_a_Completely_Susceptible_Population_When_the_Infection_Is_Spread_between_and_within_Age_Groups_by_the_Contacts_as_Observed_in_Figure_3_/605719", "title"=>"Relative Incidence of a New Emerging Infection in a Completely Susceptible Population, When the Infection Is Spread between and within Age Groups by the Contacts as Observed in Figure 3", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-03-25 01:35:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/934822"], "description"=>"<p>Graphs show data by (A) duration, (B) location, and (C) frequency of contact; the correlation between duration and frequency of contact is shown in (D). All correlations are highly significant (<i>p</i> < 0.001, <i>χ</i><sup>2</sup>-test). The figures are based on pooled contact data from all eight countries and weighted according to sampling weights as explained in the Methods (based on household size and age).</p>", "links"=>[], "tags"=>["contacts"], "article_id"=>605257, "categories"=>["Biotechnology", "Mathematics", "Infectious Diseases"], "users"=>["Joël Mossong", "Niel Hens", "Mark Jit", "Philippe Beutels", "Kari Auranen", "Rafael Mikolajczyk", "Marco Massari", "Stefania Salmaso", "Gianpaolo Scalia Tomba", "Jacco Wallinga", "Janneke Heijne", "Malgorzata Sadkowska-Todys", "Magdalena Rosinska", "W. John Edmunds"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.0050074.g001", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_Mean_Proportion_of_Contacts_That_Involved_Physical_Contact_by_Duration_Frequency_and_Location_of_Contact_in_All_Countries_/605257", "title"=>"The Mean Proportion of Contacts That Involved Physical Contact, by Duration, Frequency, and Location of Contact in All Countries", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-03-25 01:27:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/935144"], "description"=>"<p>White indicates high contact rates, green intermediate contact rates, and blue low contact rates, relative to the country-specific contact intensity. Fitting is based on a tensor-product spline to contact matrix data using a negative binomial distribution to account for overdispersion.</p>", "links"=>[], "tags"=>["matrices", "contacts", "weighted", "sampling"], "article_id"=>605575, "categories"=>["Biotechnology", "Mathematics", "Infectious Diseases"], "users"=>["Joël Mossong", "Niel Hens", "Mark Jit", "Philippe Beutels", "Kari Auranen", "Rafael Mikolajczyk", "Marco Massari", "Stefania Salmaso", "Gianpaolo Scalia Tomba", "Jacco Wallinga", "Janneke Heijne", "Malgorzata Sadkowska-Todys", "Magdalena Rosinska", "W. John Edmunds"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.0050074.g003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Smoothed_Contact_Matrices_for_Each_Country_Based_on_A_All_Reported_Contacts_and_B_Physical_Contacts_Weighted_by_Sampling_Weights_/605575", "title"=>"Smoothed Contact Matrices for Each Country Based on (A) All Reported Contacts and (B) Physical Contacts Weighted by Sampling Weights", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-03-25 01:32:55"}

PMC Usage Stats | Further Information

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Relative Metric

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