Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases
Identifieur interne : 000281 ( Ncbi/Merge ); précédent : 000280; suivant : 000282Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases
Auteurs : Joël Mossong [Luxembourg (pays)] ; Niel Hens [Belgique] ; Mark Jit [Royaume-Uni] ; Philippe Beutels [Belgique] ; Kari Auranen [Finlande] ; Rafael Mikolajczyk [Allemagne] ; Marco Massari [Italie] ; Stefania Salmaso [Italie] ; Gianpaolo Scalia Tomba [Italie] ; Jacco Wallinga [Pays-Bas] ; Janneke Heijne [Pays-Bas] ; Malgorzata Sadkowska-Todys [Pologne] ; Magdalena Rosinska [Pologne] ; W. John Edmunds [Royaume-Uni]Source :
- PLoS Medicine [ 1549-1277 ] ; 2008.
Abstract
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.
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.
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.
Url:
DOI: 10.1371/journal.pmed.0050074
PubMed: 18366252
PubMed Central: 2270306
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<author><name sortKey="Sadkowska Todys, Malgorzata" sort="Sadkowska Todys, Malgorzata" uniqKey="Sadkowska Todys M" first="Malgorzata" last="Sadkowska-Todys">Malgorzata Sadkowska-Todys</name>
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<wicri:regionArea> National Institute of Hygiene, Warsaw</wicri:regionArea>
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<author><name sortKey="Rosinska, Magdalena" sort="Rosinska, Magdalena" uniqKey="Rosinska M" first="Magdalena" last="Rosinska">Magdalena Rosinska</name>
<affiliation wicri:level="1"><nlm:aff id="aff11"> National Institute of Hygiene, Warsaw, Poland</nlm:aff>
<country xml:lang="fr">Pologne</country>
<wicri:regionArea> National Institute of Hygiene, Warsaw</wicri:regionArea>
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<front><div type="abstract" xml:lang="en"><sec id="st1"><title>Background</title>
<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>
</sec>
<sec id="st2"><title>Methods and Findings</title>
<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>
</sec>
<sec id="st3"><title>Conclusions</title>
<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>
</sec>
</div>
</front>
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<article-id pub-id-type="doi">10.1371/journal.pmed.0050074</article-id>
<article-id pub-id-type="publisher-id">07-PLME-RA-1231R2</article-id>
<article-id pub-id-type="sici">plme-05-03-18</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject>
</subj-group>
<subj-group subj-group-type="Discipline"><subject>Infectious Diseases</subject>
<subject>Mathematics</subject>
<subject>Public Health and Epidemiology</subject>
</subj-group>
<subj-group subj-group-type="System Taxonomy"><subject>Infectious Diseases</subject>
<subject>Epidemiology</subject>
<subject>Health Policy</subject>
<subject>Public Health</subject>
<subject>Statistics</subject>
</subj-group>
</article-categories>
<title-group><article-title>Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases</article-title>
<alt-title alt-title-type="running-head">Social Contacts and Mixing Patterns</alt-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Mossong</surname>
<given-names>Joël</given-names>
</name>
<xref ref-type="aff" rid="aff1">1</xref>
<xref ref-type="aff" rid="aff2">2</xref>
<xref ref-type="corresp" rid="cor1">*</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Hens</surname>
<given-names>Niel</given-names>
</name>
<xref ref-type="aff" rid="aff3">3</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Jit</surname>
<given-names>Mark</given-names>
</name>
<xref ref-type="aff" rid="aff4">4</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Beutels</surname>
<given-names>Philippe</given-names>
</name>
<xref ref-type="aff" rid="aff5">5</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Auranen</surname>
<given-names>Kari</given-names>
</name>
<xref ref-type="aff" rid="aff6">6</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Mikolajczyk</surname>
<given-names>Rafael</given-names>
</name>
<xref ref-type="aff" rid="aff7">7</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Massari</surname>
<given-names>Marco</given-names>
</name>
<xref ref-type="aff" rid="aff8">8</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Salmaso</surname>
<given-names>Stefania</given-names>
</name>
<xref ref-type="aff" rid="aff8">8</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Tomba</surname>
<given-names>Gianpaolo Scalia</given-names>
</name>
<xref ref-type="aff" rid="aff9">9</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Wallinga</surname>
<given-names>Jacco</given-names>
</name>
<xref ref-type="aff" rid="aff10">10</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Heijne</surname>
<given-names>Janneke</given-names>
</name>
<xref ref-type="aff" rid="aff10">10</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Sadkowska-Todys</surname>
<given-names>Malgorzata</given-names>
</name>
<xref ref-type="aff" rid="aff11">11</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Rosinska</surname>
<given-names>Magdalena</given-names>
</name>
<xref ref-type="aff" rid="aff11">11</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Edmunds</surname>
<given-names>W. John</given-names>
</name>
<xref ref-type="aff" rid="aff4">4</xref>
</contrib>
</contrib-group>
<aff id="aff1"><label>1</label>
Microbiology Unit, Laboratoire National de Santé, Luxembourg, Luxembourg</aff>
<aff id="aff2"><label>2</label>
Centre de Recherche Public Santé, Luxembourg, Luxembourg</aff>
<aff id="aff3"><label>3</label>
Center for Statistics, Hasselt University, Diepenbeek, Belgium</aff>
<aff id="aff4"><label>4</label>
Modelling and Economics Unit, Health Protection Agency Centre for Infections, London, United Kingdom</aff>
<aff id="aff5"><label>5</label>
Unit Health Economic and Modeling Infectious Diseases, Center for the Evaluation of Vaccination, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium</aff>
<aff id="aff6"><label>6</label>
Department of Vaccines, National Public Health Institute KTL, Helsinki, Finland</aff>
<aff id="aff7"><label>7</label>
School of Public Health, University of Bielefeld, Bielefeld, Germany</aff>
<aff id="aff8"><label>8</label>
Istituto Superiore di Sanità, Rome, Italy</aff>
<aff id="aff9"><label>9</label>
Department of Mathematics, University of Rome Tor Vergata, Rome, Italy</aff>
<aff id="aff10"><label>10</label>
Centre for Infectious Disease Control Netherlands, National Institute for Public Health and the Environment, Bilthoven, The Netherlands</aff>
<aff id="aff11"><label>11</label>
National Institute of Hygiene, Warsaw, Poland</aff>
<contrib-group><contrib contrib-type="editor"><name><surname>Riley</surname>
<given-names>Steven</given-names>
</name>
<role>Academic Editor</role>
<xref ref-type="aff" rid="edit1"></xref>
</contrib>
</contrib-group>
<aff id="edit1">Hong Kong University, Hong Kong</aff>
<author-notes><corresp id="cor1">* To whom correspondence should be addressed. E-mail: <email>joel.mossong@lns.etat.lu</email>
</corresp>
</author-notes>
<pub-date pub-type="ppub"><month>3</month>
<year>2008</year>
</pub-date>
<pub-date pub-type="epub"><day>25</day>
<month>3</month>
<year>2008</year>
</pub-date>
<volume>5</volume>
<issue>3</issue>
<elocation-id>e74</elocation-id>
<history><date date-type="received"><day>8</day>
<month>8</month>
<year>2007</year>
</date>
<date date-type="accepted"><day>15</day>
<month>2</month>
<year>2008</year>
</date>
</history>
<copyright-statement><bold>Copyright</bold>
: © 2008 Mossong et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</copyright-statement>
<copyright-year>2008</copyright-year>
<related-article xlink:href="10.1371/journal.pmed.0050071" related-article-type="companion" xlink:title="Editorial" vol="5" page="e71" id="RA1" ext-link-type="doi"><article-title>It's the Network, Stupid: Why Everything in Medicine Is Connected</article-title>
</related-article>
<abstract><sec id="st1"><title>Background</title>
<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>
</sec>
<sec id="st2"><title>Methods and Findings</title>
<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>
</sec>
<sec id="st3"><title>Conclusions</title>
<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>
</sec>
</abstract>
<abstract abstract-type="toc"><p>Surveying 7,290 participants in eight European countries, Joël Mossong and colleagues determine patterns of person-to-person contact relevant to controlling pathogens spread by respiratory or close-contact routes.</p>
</abstract>
<abstract abstract-type="editor"><title>Editors' Summary</title>
<sec id="sb1a"><title>Background</title>
<p>To understand and predict the impact of infectious disease, researchers often develop mathematical models. These computer simulations of hypothetical scenarios help policymakers and others to anticipate possible patterns and consequences of the emergence of diseases, and to develop interventions to curb disease spread. Whether to prepare for an outbreak of infectious disease or to control an existing outbreak, models can help researchers and policy makers decide how to intervene. For example, they may decide to develop or stockpile vaccines or antibiotics, fund vaccination or screening programs, or mount health promotion campaigns to help citizens minimize their exposure to the infectious agent (e.g., handwashing, travel restrictions, or school closures).</p>
<p>Respiratory infections, including the common cold, flu, and pneumonia, are some of the most prevalent infections in the world. Much work has gone into modeling how many people would be affected by respiratory diseases under various conditions and what can be done to limit the consequences.</p>
</sec>
<sec id="sb1b"><title>Why Was This Study Done?</title>
<p>Mathematical models have tended to use contact rates (the number of other people that a person encounters per day) as one of their main elements in predicting the outcomes of epidemics. In the past, contact rates were not based on direct observations, but were assumed to follow a certain pattern and calibrated against other indirect data sources such as serological or case notification data. This study aimed to estimate contact rates directly by asking people who they have met during the course of one day. This allowed the researchers to study in more detail different <italic>patterns</italic>
of contacts, such as those between different groups of people (such as age groups) and in different social settings. This is particularly important for respiratory diseases, which are spread through the air and by close contact with an infected individual or surface.</p>
</sec>
<sec id="sb1c"><title>What Did the Researchers Do and Find?</title>
<p>The researchers wanted to examine the social contacts that people have in order to better understand how respiratory infections might spread. They recruited 7,290 people from eight European countries (Belgium, Germany, Finland, Great Britain, Italy, Luxembourg, The Netherlands, and Poland) to participate in their study. They asked the participants to fill out a diary that documented their physical and nonphysical contacts for a single day. Physical contacts included interactions such as a kiss or a handshake. Nonphysical contacts were situations such as a two-way conversation without skin-to-skin contact. Participants detailed the location and duration of each contact. Diaries also contained basic demographic information about the participant and the contact.</p>
<p>They found that these 7,290 participants had 97,904 contacts during the study, which averaged to 13.4 contacts per day per person. There was a great deal of diversity among the contacts, which challenges the idea that contact rates alone provide a complete picture of transmission dynamics. The researchers identified varied types of contacts, duration of contacts, and mixing patterns. For example, children had more contacts than adults, and those living in larger households had more contacts. Weekdays resulted in more daily contacts than Sundays. More intense contacts (of longer duration or more frequent) tended to be physical. Approximately 70% of contacts made on a daily basis lasted longer than an hour, whereas three-quarters of contacts with people who were not previously known lasted less than 15 minutes. While mixing patterns were very similar across the eight countries, people of the same age tended to mix with each other.</p>
<p>Analyzing these contact patterns and applying mathematical and statistical techniques, the researchers created a model of the initial phase of a hypothetical respiratory infection epidemic. This model suggests that 5- to 19-year-olds will suffer the highest burden of respiratory infection during an initial spread. The high incidence of infection among school-aged children in the model results from these children having a large number of contacts compared to other groups and tending to make contacts within their own age group.</p>
</sec>
<sec id="sb1d"><title>What Do These Findings Mean?</title>
<p>This work provides insight about contacts that can be supplemental to traditional measurements such as contact rates, which are usually generated from household or workplace size and transportation statistics. Incorporating contact patterns into the model allowed for a deeper understanding of the transmission patterns of a hypothetical respiratory epidemic among a susceptible population. Understanding the patterning of social contacts—between and within groups, and in different social settings—shows how diverse contacts and mixing between individuals really are. Physical exposure to an infectious agent, the authors conclude, is best modeled by taking into account the social network of close contacts and its patterning.</p>
</sec>
<sec id="sb1e"><title>Additional Information.</title>
<p>Please access these Web sites via the online version of this summary at doi:<ext-link ext-link-type="doi" xlink:href="10.1371/journal.pmed.0050074.">10.1371/journal.pmed.0050074.</ext-link>
.</p>
<list list-type="bullet"><list-item><p>Wikipedia has <ext-link ext-link-type="uri" xlink:href="http://en.wikipedia.org/wiki/Mathematical_modelling_in_epidemiology">technical discussions on the assumptions used in mathematical models of epidemiology</ext-link>
(note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)</p>
</list-item>
<list-item><p>Plans for pandemic influenza are explained for the <ext-link ext-link-type="uri" xlink:href="http://www.influenza.gc.ca/index_e.html">Government of Canada</ext-link>
, the <ext-link ext-link-type="uri" xlink:href="http://www.hpa.org.uk/infections/topics_az/influenza/pandemic/guidelines.htm">United Kingdom's Health Protection Agency</ext-link>
, and the <ext-link ext-link-type="uri" xlink:href="http://pandemicflu.gov/">United States Department of Health and Human Services</ext-link>
</p>
</list-item>
</list>
</sec>
</abstract>
<counts><page-count count="1"></page-count>
</counts>
<custom-meta-wrap><custom-meta><meta-name>citation</meta-name>
<meta-value>Mossong J, Hens N, Jit M, Beutels P, Auranen K, et al. (2008) Social contacts and mixing patterns relevant to the spread of infectious diseases. PLoS Med 5(3) e74. doi:<ext-link ext-link-type="doi" xlink:href="10.1371/journal.pmed.0050074">10.1371/journal.pmed.0050074</ext-link>
</meta-value>
</custom-meta>
</custom-meta-wrap>
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</front>
</pmc>
<affiliations><list><country><li>Allemagne</li>
<li>Belgique</li>
<li>Finlande</li>
<li>Italie</li>
<li>Luxembourg (pays)</li>
<li>Pays-Bas</li>
<li>Pologne</li>
<li>Royaume-Uni</li>
</country>
<region><li>Angleterre</li>
<li>Grand Londres</li>
<li>Latium</li>
<li>Province d'Anvers</li>
<li>Région flamande</li>
</region>
<settlement><li>Anvers</li>
<li>Londres</li>
<li>Rome</li>
</settlement>
<orgName><li>Université d'Anvers</li>
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<tree><country name="Luxembourg (pays)"><noRegion><name sortKey="Mossong, Joel" sort="Mossong, Joel" uniqKey="Mossong J" first="Joël" last="Mossong">Joël Mossong</name>
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<name sortKey="Mossong, Joel" sort="Mossong, Joel" uniqKey="Mossong J" first="Joël" last="Mossong">Joël Mossong</name>
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<country name="Belgique"><noRegion><name sortKey="Hens, Niel" sort="Hens, Niel" uniqKey="Hens N" first="Niel" last="Hens">Niel Hens</name>
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<name sortKey="Beutels, Philippe" sort="Beutels, Philippe" uniqKey="Beutels P" first="Philippe" last="Beutels">Philippe Beutels</name>
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<country name="Royaume-Uni"><region name="Angleterre"><name sortKey="Jit, Mark" sort="Jit, Mark" uniqKey="Jit M" first="Mark" last="Jit">Mark Jit</name>
</region>
<name sortKey="Edmunds, W John" sort="Edmunds, W John" uniqKey="Edmunds W" first="W. John" last="Edmunds">W. John Edmunds</name>
</country>
<country name="Finlande"><noRegion><name sortKey="Auranen, Kari" sort="Auranen, Kari" uniqKey="Auranen K" first="Kari" last="Auranen">Kari Auranen</name>
</noRegion>
</country>
<country name="Allemagne"><noRegion><name sortKey="Mikolajczyk, Rafael" sort="Mikolajczyk, Rafael" uniqKey="Mikolajczyk R" first="Rafael" last="Mikolajczyk">Rafael Mikolajczyk</name>
</noRegion>
</country>
<country name="Italie"><region name="Latium"><name sortKey="Massari, Marco" sort="Massari, Marco" uniqKey="Massari M" first="Marco" last="Massari">Marco Massari</name>
</region>
<name sortKey="Salmaso, Stefania" sort="Salmaso, Stefania" uniqKey="Salmaso S" first="Stefania" last="Salmaso">Stefania Salmaso</name>
<name sortKey="Tomba, Gianpaolo Scalia" sort="Tomba, Gianpaolo Scalia" uniqKey="Tomba G" first="Gianpaolo Scalia" last="Tomba">Gianpaolo Scalia Tomba</name>
</country>
<country name="Pays-Bas"><noRegion><name sortKey="Wallinga, Jacco" sort="Wallinga, Jacco" uniqKey="Wallinga J" first="Jacco" last="Wallinga">Jacco Wallinga</name>
</noRegion>
<name sortKey="Heijne, Janneke" sort="Heijne, Janneke" uniqKey="Heijne J" first="Janneke" last="Heijne">Janneke Heijne</name>
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<country name="Pologne"><noRegion><name sortKey="Sadkowska Todys, Malgorzata" sort="Sadkowska Todys, Malgorzata" uniqKey="Sadkowska Todys M" first="Malgorzata" last="Sadkowska-Todys">Malgorzata Sadkowska-Todys</name>
</noRegion>
<name sortKey="Rosinska, Magdalena" sort="Rosinska, Magdalena" uniqKey="Rosinska M" first="Magdalena" last="Rosinska">Magdalena Rosinska</name>
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