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Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases
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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.
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“These younger individuals tend to have more contacts than older age groups 33,34 .”
From Spread of a SARS-CoV-2 variant through Europe in the summer of 2020 (2021) · cites this paper as [33]
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Cites
Generalized Additive Models: An Introduction with R
Wood, S. N. (2017) · doi:10.1201/9781315370279 · Cited by 13,852
Strategies for mitigating an influenza pandemic
Ferguson, N. M., Cummings, D. A. T., Fraser, C., Cajka, J. C., Cooley, P. C., & Burke, D. S. (2006) · Nature · doi:10.1038/nature04795 · Cited by 2,203 · cited 2×
“For instance, household size, class size, transport statistics, and workplace size distribution have been used in recent models to define the contact structure [2, 3, 33, 34] .”
BackgroundMethodModelling disease outbreaks in realistic urban social networks
Eubank, S., Güçlü, H., Kumar, V. S. A., MARATHE, M. V., Srinivasan, A., Toroczkai, Z., & Wang, N. (2004) · Nature · doi:10.1038/nature02541 · Cited by 2,098 · cited 1×
“For instance, household size, class size, transport statistics, and workplace size distribution have been used in recent models to define the contact structure [2, 3, 33, 34] .”
MethodHilbe, J. M. (2007) · Cambridge University Press eBooks · doi:10.1017/cbo9780511811852.007 · Cited by 1,998
Mathematical Epidemiology of Infectious Diseases: Model Building, Analysis and Interpretation
Diekmann, O., & Heesterbeek, H. (2000) · Socio-Environmental Systems Modeling · Cited by 1,833
Transmission Dynamics and Control of Severe Acute Respiratory Syndrome
Lipsitch, M., Cohen, T., Cooper, B. S., Robins, J. M., Ma, S., James, L., Gopalakrishna, G., Chew, S. K., Tan, C. C., Samore, M. H., et al. (2003) · Science · doi:10.1126/science.1086616 · Cited by 1,485 · cited 1×
“Preparing for outbreaks of directly transmitted pathogens such as pandemic influenza [1] [2] [3] and SARS [4] [5] [6] [7] [8] [9] , and controlling endemic diseases such as tuberculosis and meningococcal diseases, are major public health priorities.”
BackgroundWallinga, J. (2004) · American Journal of Epidemiology · doi:10.1093/aje/kwh255 · Cited by 1,214 · cited 1×
“Preparing for outbreaks of directly transmitted pathogens such as pandemic influenza [1] [2] [3] and SARS [4] [5] [6] [7] [8] [9] , and controlling endemic diseases such as tuberculosis and meningococcal diseases, are major public health priorities.”
BackgroundSelf-citationContaining Pandemic Influenza at the Source
Longini, I. M., Nizam, A., Xu, S., Ungchusak, K., Hanshaoworakul, W., Cummings, D. A. T., & Halloran, M. E. (2005) · Science · doi:10.1126/science.1115717 · Cited by 1,177 · cited 1×
“Preparing for outbreaks of directly transmitted pathogens such as pandemic influenza [1] [2] [3] and SARS [4] [5] [6] [7] [8] [9] , and controlling endemic diseases such as tuberculosis and meningococcal diseases, are major public health priorities.”
BackgroundRiley, S., Fraser, C., Donnelly, C. A., Ghani, A. C., Abu-Raddad, L. J., Hedley, A. J., Leung, G. M., Ho, L., Lam, T.-H., Thach, T., et al. (2003) · Science · doi:10.1126/science.1086478 · Cited by 1,152 · cited 7×
“This similarity implies that the results may well be applicable to other European countries, and that the initial phase of spread of newly emerging infections in susceptible populations, such as SARS was in 2003, is likely to be very similar across Europe and in countries with similar social structures.”
BackgroundInfluentialMitigation strategies for pandemic influenza in the United States
Germann, T. C., Kadau, K., Longini, I. M., & Macken, C. A. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601266103 · Cited by 1,047 · cited 1×
“For instance, household size, class size, transport statistics, and workplace size distribution have been used in recent models to define the contact structure [2,3,33,34].”
Method
Research works printed but not linked: 1
- 2007, Comparison of paper diary vs computer assisted telephone interview for collecting social contact data relevant to the spread of airborne infectious diseases., Eur J Public Health, 17, 189
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Kim, J. H., Yoo, H.-S., Lee, J.-S., Lee, E. G., Park, H.-K., Sung, Y. H., Kim, S., Kim, H. S., Shin, S. Y., & Lee, J. (2010) · Journal of Korean Medical Science · doi:10.3346/jkms.2010.25.7.1109 · Cited by 30
Horn, J., Karch, A., Damm, O., Kretzschmar, M., Siedler, A., Ultsch, B., Weidemann, F., Wichmann, O., Hengel, H., Greiner, W., et al. (2016) · Human Vaccines & Immunotherapeutics · doi:10.1080/21645515.2015.1135279 · Cited by 30
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White, L. F., Moser, C., Thompson, R. N., & Pagano, M. (2020) · American Journal of Epidemiology · doi:10.1093/aje/kwaa211 · Cited by 30
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Reichert, T., Chowell, G., & McCullers, J. A. (2012) · BMC Medicine · doi:10.1186/1741-7015-10-162 · Cited by 30
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Chowell, G., Nishiura, H., & Viboud, C. G. (2012) · BMC Medicine · doi:10.1186/1741-7015-10-159 · Cited by 30
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Feng, Z., Hill, A. N., Curns, A. T., & Glasser, J. W. (2016) · Mathematical Biosciences · doi:10.1016/j.mbs.2016.09.013 · Cited by 32
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Manna, A., Koltai, J., & Karsai, M. (2024) · Nature Communications · doi:10.1038/s41467-024-48332-y · Cited by 34
“To identify these dimensions, we model the expected number of contacts of respondent i using a negative binomial regression 18,51 as de fi ned in Eq.”
Leveraging social networks for understanding the evolution of epidemics
Martín, G., Marinescu, M.-C., Singh, D. E., & Carretero, J. (2011) · BMC Systems Biology · doi:10.1186/1752-0509-5-s3-s14 · Cited by 29
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Riolo, M. A., & Rohani, P. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1415573112 · Cited by 29
Nagy, L., Heikkinen, T., Sackeyfio, A., & Pitman, R. (2016) · PharmacoEconomics · doi:10.1007/s40273-016-0430-z · Cited by 29
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Fine, P., Mulholland, K., Scott, J. A. G., & Edmunds, W. J. (2017) · Elsevier eBooks · doi:10.1016/b978-0-323-35761-6.00077-8 · Cited by 31
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Anderson, R. M., Hollingsworth, T. D., & Nokes, D. J. (2009) · Oxford University Press eBooks · doi:10.1093/med/9780199218707.003.0041 · Cited by 30
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Jackson, C., Mangtani, P., Fine, P., & Vynnycky, E. (2014) · PLoS ONE · doi:10.1371/journal.pone.0099762 · Cited by 29
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Hao, L., Glasser, J. W., Su, Q., Ma, C., Feng, Z., Yin, Z., Goodson, J. L., Wen, N., Fan, C., Yang, H., et al. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyz058 · Cited by 29
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Lee, T., Kwon, H., & Lee, J. (2021) · PLoS ONE · doi:10.1371/journal.pone.0249262 · Cited by 31
Barry, C.-J. S., Liu, J., Richmond, R. C., Rutter, M. K., Lawlor, D. A., Dudbridge, F., & Bowden, J. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009703 · Cited by 29
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Nuraini, N., Sukandar, K. K., Hadisoemarto, P. F., Susanto, H., Hasan, A., & Sumarti, N. (2021) · Infectious Disease Modelling · doi:10.1016/j.idm.2021.09.002 · Cited by 31
Endo, A., Uchida, M., Hayashi, N., Liu, Y., Atkins, K. E., Kucharski, A. J., & Funk, S. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2112605118 · Cited by 32
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Bosetti, P., Huynh, B.-T., Abdou, A. Y., Sanchez, M., Eisenhauer, C., Courtejoie, N., Accardo, J., Salje, H., Guillemot, D., Moslonka‐Lefebvre, M., et al. (2021) · Eurosurveillance · doi:10.2807/1560-7917.es.2021.26.48.2001636 · Cited by 34
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BackgroundHerd immunity and epidemic size in networks with vaccination homophily
Hiraoka, T., Rizi, A. K., Kivelä, M., & Saramäki, J. (2022) · Physical review. E · doi:10.1103/physreve.105.l052301 · Cited by 29
“In human society, vaccination homophily can emerge due to the presence of confounding factors, such as age [5], geography [25,26], socioeconomic status [27], and personal and religious beliefs [28], that influence both the likelihood of interaction between L052301-4 individuals and the likelihood of…”
BackgroundGlover, A., Heathcote, J., & Krueger, D. (2022) · Journal of Economic Dynamics and Control · doi:10.1016/j.jedc.2022.104306 · Cited by 27
“6 Mossong et al. (2008) argue that in normal 4Empirically, we associate this sector with food away from home, transportation services, apparel, new vehicles and gasoline, with the idea that these are sectors with a social interaction component of consumption.”
BackgroundRukke, B. A., Soleng, A., Lindstedt, H. H., Ottesen, P., & Birkemoe, T. (2014) · Parasitology Research · doi:10.1007/s00436-014-3833-9 · Cited by 28
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Shi, B., Liu, J., Zhou, X., & Yang, G.-J. (2014) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0002682 · Cited by 27
Bartenfeld, M. T., Peacock, G., & Griese, S. E. (2014) · Biosecurity and Bioterrorism Biodefense Strategy Practice and Science · doi:10.1089/bsp.2014.0036 · Cited by 28
Tuite, A. R., Greer, A. L., Keninck, S. D., & Fisman, D. N. (2020) · Annals of Internal Medicine · doi:10.7326/m20-2945 · Cited by 27
Becker, A. D., Birger, R. B., Teillant, A., Gastañaduy, P. A., Wallace, G. S., & Grenfell, B. T. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1604976113 · Cited by 29
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Rushton, S. P., Sanderson, R., Reid, W. D. K., Shirley, M. D. F., Harris, J. P., Hunter, P. R., & O’Brien, S. J. (2019) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2018.0267 · Cited by 27
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Zhang, H., Yang, Z., Pawelek, K. A., & Liu, S. (2019) · Applied Mathematics and Computation · doi:10.1016/j.amc.2019.124956 · Cited by 27
Sharifi, H. P., Jahani, Y., Mirzazadeh, A., Gohari, M. A., Nakhaeizadeh, M., Shokoohi, M., Eybpoosh, S., Tohidinik, H. R., Mostafavi, E., Khalili, D., et al. (2020) · International Journal of Health Policy and Management · doi:10.34172/ijhpm.2020.134 · Cited by 27
Yang, Q., Xiao, X., Gu, X., Dong, L., Cao, T., Mou, J., Huang, C., Chen, L., & Liu, J. (2021) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2021.02.027 · Cited by 29
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BackgroundMcBryde, E. S., Meehan, M. T., Caldwell, J. M., Adekunle, A. I., Ogunlade, S. T., Kuddus, M. A., Ragonnet, R. R., Jayasundara, P., Trauer, J. M., & Cope, R. C. (2021) · The Medical Journal of Australia · doi:10.5694/mja2.51263 · Cited by 27
“Values for Vs and Vm are derived from data on earlier variants (13).”
MethodChoi, Y. H., & Miller, E. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-045380 · Cited by 24
Lappe, B. L., Wikswo, M. E., Kambhampati, A. K., Mirza, S. A., Tate, J. E., Kraay, A. N. M., & Lopman, B. A. (2023) · BMC Infectious Diseases · doi:10.1186/s12879-023-08224-w · Cited by 27
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Balocco, C., & Lió, P. (2010) · Energy and Buildings · doi:10.1016/j.enbuild.2010.09.020 · Cited by 27
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Weidemann, F., Remschmidt, C., Buda, S., Buchholz, U., Ultsch, B., & Wichmann, O. (2017) · BMC Infectious Diseases · doi:10.1186/s12879-017-2344-6 · Cited by 27
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High-resolution epidemic simulation using within-host infection and contact data
Nguyễn, V. K., Mikolajczyk, R., & Hernandez-Vargas, E. A. (2018) · BMC Public Health · doi:10.1186/s12889-018-5709-x · Cited by 28
Self-citationReal-world diffusion dynamics based on point process approaches: a review
Kim, M., Paini, D. R., & Jurdak, R. (2018) · Artificial Intelligence Review · doi:10.1007/s10462-018-9656-9 · Cited by 27
Zhang, X., Angelis, D. D., White, P. J., Charlett, A., Pebody, R. G., & McCauley, J. W. (2012) · Epidemics · doi:10.1016/j.epidem.2012.10.003 · Cited by 27
Anderson, N., Bonauto, D. K., Fan, Z. J., & Spector, J. T. (2012) · PLoS ONE · doi:10.1371/journal.pone.0048806 · Cited by 26
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McGirr, A. A., Tuite, A. R., & Fisman, D. N. (2013) · PLoS ONE · doi:10.1371/journal.pone.0083850 · Cited by 26
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Duncan, A. J., Gunn, G. J., Lewis, F. I., Umstätter, C., & Humphry, R. W. (2012) · Epidemics · doi:10.1016/j.epidem.2012.04.003 · Cited by 26
Marangi, L., Mirinavičiūtė, G., Flem, E., Tomba, G. S., Guzzetta, G., de Blasio, B. F., & Manfredi, P. (2017) · PLoS ONE · doi:10.1371/journal.pone.0176845 · Cited by 26
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Douglas, J. V., Bianco, S., Edlund, S. B., Engelhardt, T., Filter, M., Günther, T., Hu, K., Nixon, E. J., Sevilla, N. L., Swaid, A., et al. (2019) · Health Security · doi:10.1089/hs.2019.0018 · Cited by 26
Huang, Y., Cai, X., Zhang, B., Zhu, G., Liu, T., Guo, P., Xiao, J., Li, X., Zeng, W., Hu, J., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-63383-z · Cited by 26
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Ulytė, A., Radtke, T., Abela, I. A., Haile, S. R., Braun, J., Jung, R., Berger, C., Trkola, A., Fehr, J. S., Puhan, M. A., et al. (2020) · International Journal of Public Health · doi:10.1007/s00038-020-01495-z · Cited by 29
Su, Q., Feng, Z., Hao, L., Ma, C., Hagan, J. E., Grant, G. B., Wen, N., Fan, C., Yang, H., Rodewald, L. E., et al. (2021) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30475-8 · Cited by 26
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Metadata
- Authors
- Mossong, J., Hens, N., Jit, M., Beutels, P., Auranen, K., Mikolajczyk, R., Massari, M., Salmaso, S., Tomba, G. S., Wallinga, J., Heijne, J., Sadkowska-Todys, M., Rosinska, M., & Edmunds, W. J.
- Affiliations
- Laboratoire National de Santé (LU)
- Hasselt University (BE)
- Health Protection Agency (GB)
- University of Antwerp (BE)
- Bielefeld University (DE)
- Istituto Superiore di Sanità (IT)
- University of Rome Tor Vergata (IT)
- National Institute for Public Health and the Environment (NL)
- Corresponding author
- Mossong, Joël
- ORCID
- Venue
- PLoS Medicine
- Publisher
- Public Library of Science (PLoS)
- Published
- 2008-03-25
- Volume
- 5
- Issue
- 3
- Pages
- e74
- ISSN
- 1549-1277 (ISSN-L)
- 1549-1676
- PMC
- PMC2270306
- OpenAlex
- W2159301256
- Cited by
- 2,900
- References
- 38
- Citations by year
- 2026: 143 · 2025: 141 · 2024: 176 · 2023: 167 · 2022: 300 · 2021: 508 · 2020: 350 · 2019: 107 · 2018: 126 · 2017: 96 · 2016: 108 · 2015: 102 · 2014: 119 · 2013: 99 · 2012: 98
- FWCI
- 64.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, other-oa)
- PubMed (publishedVersion)
- LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
- Figshare (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Cineca Institutional Research Information System (Tor Vergata University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Document Server@UHasselt (UHasselt) (submittedVersion, other-oa)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion, other-oa)
- Rivm Repository (Netherlands National Institute for Public Health and the Environment) (submittedVersion)
- Funders
- European Commission (SSP22-CT-2004-502084, 502084)
- Topics
- Health Sciences › Medicine › Epidemiology › Influenza Virus Research Studies
- Physical Sciences › Mathematics › Applied Mathematics › Mathematical and Theoretical Epidemiology and Ecology Models
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Keywords
- social mixing, contact patterns, infectious disease transmission, respiratory transmission, close contact transmission, physical contact, pandemic influenza, elementary school students, young adults
- MeSH terms
- Adolescent, Adult, Aged, Child, Child, Preschool, Cross-Sectional Studies, Europe, Female, Humans, Infant, Male, Middle Aged, Models, Theoretical, Respiratory Tract Infections, Social Support
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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