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Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases
Open accessAbstract
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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Gao, X., Wei, J., Lei, H., Xu, P., Cowling, B. J., & Li, Y. (2016) · PLoS ONE · doi:10.1371/journal.pone.0162481 · Cited by 55
Measles and the canonical path to elimination
Graham, M., Winter, A. K., Ferrari, M. J., Grenfell, B. T., Moss, W. J., Azman, A. S., Metcalf, C. J. E., & Lessler, J. (2019) · Science · doi:10.1126/science.aau6299 · Cited by 53
Bracher, J., & Held, L. (2020) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2020.07.002 · Cited by 4
Fitting to the UK COVID-19 outbreak, short-term forecasts and estimating the reproductive number
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A mathematical model of the indirect effects of rotavirus vaccination
Effelterre, T. P. V., Soriano‐Gabarró, M., Debrus, S., Newbern, E. C., & GRAY, J. (2009) · Epidemiology and Infection · doi:10.1017/s0950268809991245 · Cited by 53
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Gastañaduy, P. A., Funk, S., Lopman, B. A., Rota, P. A., Gambhir, M., Grenfell, B. T., & Paul, P. (2019) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/jamapediatrics.2019.4357 · Cited by 52
Implications of the COVID-19 Lockdown on Dengue Transmission in Malaysia
Ong, S., Ahmad, H., & Ngesom, A. M. M. (2021) · Infectious Disease Reports · doi:10.3390/idr13010016 · Cited by 52
Measuring social networks in British primary schools through scientific engagement
Conlan, A. J. K., Eames, K. T. D., Gage, J. A., von Kirchbach, J. C., Ross, J. V., Saenz, R. A., & Gog, J. R. (2010) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2010.1807 · Cited by 56
Fumanelli, L., Ajelli, M., Merler, S., Ferguson, N. M., & Cauchemez, S. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004681 · Cited by 51
Arpino, B., Bordone, V., & Pasqualini, M. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2008581117 · Cited by 52
Coletti, P., Libin, P., Petrof, O., Willem, L., Abrams, S., Herzog, S. A., Faes, C., Wambua, J., Kuylen, E., COVID-19 team the SIMID, et al. (2021) · BMC Infectious Diseases · doi:10.1186/s12879-021-06092-w · Cited by 56
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Giangreco, G. (2020) · Journal of Medical Virology · doi:10.1002/jmv.25894 · Cited by 51
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Zhou, S., Zhou, S., Zheng, Z., & Lu, J. (2021) · GeoHealth · doi:10.1029/2021gh000427 · Cited by 48
“, 2014) or different social groups in those places (Mossong et al., 2008).”
BackgroundChenciner, L., Annerstedt, K. S., Pescarini, J. M., & Wingfield, T. (2021) · The Lancet Global Health · doi:10.1016/s2214-109x(21)00300-4 · Cited by 51
Nyiro, J. U., Munywoki, P. K., Kamau, E., Agoti, C. N., Gichuki, A. M., Etyang, T., Otieno, G. P., & Nokes, D. J. (2018) · Wellcome Open Research · doi:10.12688/wellcomeopenres.14662.1 · Cited by 51
A prospect on the use of antiviral drugs to control local outbreaks of COVID-19
Torneri, A., Libin, P., Vanderlocht, J., Vandamme, A., Neyts, J. H., & Hens, N. (2020) · BMC Medicine · doi:10.1186/s12916-020-01636-4 · Cited by 52
Self-citationEffects of latency and age structure on the dynamics and containment of COVID-19
Blyuss, K. B., & Kyrychko, Y. N. (2021) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2021.110587 · Cited by 54
Sociodemographic determinants of intraurban variations in COVID-19 incidence: the case of Barcelona
López‐Gay, A., Spijker, J., Cole, H. V. S., Marques, A. G., Mas, M. T., Anguelovski, I., Marí-Dell’Olmo, M., Cabrerizo, J. A. M., Álamo-Junquera, D., López‐Gallego, F., et al. (2021) · Journal of Epidemiology & Community Health · doi:10.1136/jech-2020-216325 · Cited by 57
Goldstein, E., & Lipsitch, M. (2020) · Eurosurveillance · doi:10.2807/1560-7917.es.2020.25.17.2000596 · Cited by 50
Staniszewska, S., Hill, E., Grant, R., Grove, P., Porter, J., Shiri, T., Tulip, S., Whitehurst, J., Wright, C., Datta, S., et al. (2021) · Patient · doi:10.1007/s40271-020-00476-x · Cited by 39
Simulating devil facial tumour disease outbreaks across empirically derived contact networks
Hamede, R., Bashford, J., Jones, M. E., & McCallum, H. I. (2012) · Journal of Applied Ecology · doi:10.1111/j.1365-2664.2011.02103.x · Cited by 52
A review of typhoid fever transmission dynamic models and economic evaluations of vaccination
Watson, C., & Edmunds, W. J. (2015) · Vaccine · doi:10.1016/j.vaccine.2015.04.013 · Cited by 53
Self-citationNichol, K. L., Tummers, K., Hoyer‐Leitzel, A., Marsh, J., Moynihan, M., & McKelvey, S. (2010) · PLoS ONE · doi:10.1371/journal.pone.0009548 · Cited by 49
Accounting for behavioral responses during a flu epidemic using home television viewing
Springborn, M., Chowell, G., MacLachlan, M., & Fenichel, E. P. (2015) · BMC Infectious Diseases · doi:10.1186/s12879-014-0691-0 · Cited by 50
Dorigatti, I., Cauchemez, S., Pugliese, A., & Ferguson, N. M. (2011) · Epidemics · doi:10.1016/j.epidem.2011.11.001 · Cited by 51
Estimating within-school contact networks to understand influenza transmission
Potter, G. E., Handcock, M. S., Longini, I. M., & Halloran, M. E. (2012) · The Annals of Applied Statistics · doi:10.1214/11-aoas505 · Cited by 50
Nishiura, H., Hoye, B. J., Klaassen, M., Bauer, S., & Heesterbeek, H. (2009) · Epidemics · doi:10.1016/j.epidem.2009.04.002 · Cited by 50
Rukke, B. A., Birkemoe, T., Soleng, A., Lindstedt, H. H., & Ottesen, P. (2011) · Parasitology · doi:10.1017/s0031182011001004 · Cited by 50
Feng, Z., Hill, A. N., Smith, P. J., & Glasser, J. W. (2015) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2015.09.006 · Cited by 50
The use of health geography modeling to understand early dispersion of COVID-19 in São Paulo, Brazil
Fortaleza, C. M. C. B., Guimarães, R. B., Catão, R. D. C., Ferreira, C. P., de Almeida, G. B., VILCHES, T. N., & Pugliesi, E. A. (2021) · PLoS ONE · doi:10.1371/journal.pone.0245051 · Cited by 49
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Shoukat, A., VILCHES, T. N., Moghadas, S. M., Sah, P., Schneider, E. C., Shaff, J., Zucker, J. R., Quinn, C., Chokshi, D. A., & Galvani, A. P. (2021) · The Lancet Regional Health - Americas · doi:10.1016/j.lana.2021.100085 · Cited by 51
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Molla, J., Sekkak, I., Ortiz, A. M., Moyles, I. R., & Nasri, B. (2023) · One Health · doi:10.1016/j.onehlt.2023.100540 · Cited by 49
“Three studies reported regions of interest in Europe (Belgium, Austria, and the United Kingdom), and nine studies reported no geographical region of interest.”
Eichner, M., Schwehm, M., Hain, J., Uphoff, H., Salzberger, B., Knuf, M., & Schmidt‐Ott, R. (2014) · BMC Infectious Diseases · doi:10.1186/1471-2334-14-365 · Cited by 50
Spontaneous social distancing in response to a simulated epidemic: a virtual experiment
Kleczkowski, A., Maharaj, S., Rasmussen, S. A., Williams, L., & Cairns, N. (2015) · BMC Public Health · doi:10.1186/s12889-015-2336-7 · Cited by 50
Duval, A., Obadia, T., Martinet, L., Boëlle, P., Eric Fleury, B., Guillemot, D., Opatowski, L., Temime, L., I-Bird study group, Alvarez, A. S., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-20008-w · Cited by 49
Stocks, T., Britton, T., & Höhle, M. (2018) · Biostatistics · doi:10.1093/biostatistics/kxy057 · Cited by 49
Bicher, M., Rippinger, C., Urach, C., Brunmeir, D., Siebert, U., & Popper, N. (2021) · Medical Decision Making · doi:10.1177/0272989x211013306 · Cited by 54
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Fisman, D. N., Greer, A. L., Brankston, G., Hillmer, M. P., O’Brien, S. F., Drews, S. J., & Tuite, A. R. (2021) · Annals of Internal Medicine · doi:10.7326/m20-7003 · Cited by 47
Guzzetta, G., Poletti, P., Fava, E. D., Ajelli, M., Scalia‐Tomba, G., Merler, S., & Manfredi, P. (2013) · American Journal of Epidemiology · doi:10.1093/aje/kws370 · Cited by 48
Mehmood, K., Bao, Y., Petropoulos, G. P., Abbas, R., Abrar, M. M., Ullah, S., Mustafa, A., Soban, A., Saud, S., Ahmad, M., et al. (2021) · Chemosphere · doi:10.1016/j.chemosphere.2021.129809 · Cited by 48
Network models to evaluate vaccine strategies towards herd immunity in COVID-19
Tetteh, J. N. A., Nguyễn, V. K., & Hernandez-Vargas, E. A. (2021) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2021.110894 · Cited by 51
“Some of these social contact networks include existing survey data social networks such as from the POLYMOD study in Mossong et al. (2008), age-related social networks in Kenya (Kiti et al., 2014) and social contact networks in Great Britain (Danon et al., 2013).”
BackgroundNurhonen, M., Cheng, A. C., & Auranen, K. J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0056079 · Cited by 48
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Brett, T. S., Ajelli, M., Liu, Q.-H., Krauland, M. G., Grefenstette, J. J., van Panhuis, W. G., Vespignani, A., Drake, J. M., & Rohani, P. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007679 · Cited by 51
Evolutionary implications of SARS-CoV-2 vaccination for the future design of vaccination strategies
Rouzine, I. M., & Rozhnova, G. (2023) · Communications Medicine · doi:10.1038/s43856-023-00320-x · Cited by 49
“In particular, children typically have the largest number of contacts in the population due to their interaction in schools 23,24 .”
InfluentialEpidemic prediction and control in clustered populations
House, T., & Keeling, M. J. (2010) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2010.12.009 · Cited by 47
Can vaccine legacy explain the British pertussis resurgence?
Riolo, M. A., King, A. A., & Rohani, P. (2013) · Vaccine · doi:10.1016/j.vaccine.2013.09.020 · Cited by 47
Factors associated with social contacts in four communities during the 2007–2008 influenza season
Destefano, F. R., Haber, M., Currivan, D. B., Farris, T., Burrus, B., Stone-Wiggins, B., McCalla, A., GULED, H., SHIH, H., Edelson, P. J., et al. (2010) · Epidemiology and Infection · doi:10.1017/s095026881000230x · Cited by 47
Inferring Influenza Infection Attack Rate from Seroprevalence Data
Wu, J. T. K., Leung, K. S. M., Perera, R. A. P. M., Chu, D. K. W., Lee, C. K., Hung, I. F., Lin, C. K., Lo, S., Lau, Y., Leung, G. M., et al. (2014) · PLoS Pathogens · doi:10.1371/journal.ppat.1004054 · Cited by 47
Model hierarchies in edge-based compartmental modeling for infectious disease spread
Miller, J. C., & Volz, E. (2012) · Journal of Mathematical Biology · doi:10.1007/s00285-012-0572-3 · Cited by 47
Influenza epidemic spread simulation for Poland — a large scale, individual based model study
Rakowski, F., Gruziel-Słomka, M., Bieniasz-Krzywiec, Ł., & Radomski, J. P. (2010) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2010.04.029 · Cited by 47
Hodgson, D., Baguelin, M., van Leeuwen, E., Panovska‐Griffiths, J., Ramsay, M. E., Pebody, R. G., & Atkins, K. E. (2017) · The Lancet Public Health · doi:10.1016/s2468-2667(16)30044-5 · Cited by 46
Koike, F., & Morimoto, N. (2018) · Global Ecology and Biogeography · doi:10.1111/geb.12754 · Cited by 46
Age could be driving variable SARS-CoV-2 epidemic trajectories worldwide
Ayoub, H. H., Chemaitelly, H. S., Seedat, S., Mumtaz, G. R., Makhoul, M., & Abu-Raddad, L. J. (2020) · PLoS ONE · doi:10.1371/journal.pone.0237959 · Cited by 45
Wambua, J., Loedy, N., Jarvis, C. I., Wong, K. L., Faes, C., Grah, R., Prasse, B., Sandmann, F., Niehus, R., Johnson, H., et al. (2023) · BMC Public Health · doi:10.1186/s12889-023-16252-z · Cited by 49
“These mean predicted number of contacts in the period between December 2020 and September 2021 are relatively much lower compared to the average number of contacts reported in the prepandemic period in a multi-country social contact survey (POLYMOD) conducted in 8 European countries [42].”
Self-citationStein, M. L., van Steenbergen, J., Chanyasanha, C., Tipayamongkholgul, M., Buskens, V., van der Heijden, P. G. M., Sabaiwan, W., Bengtsson, L., Lu, X. B., Thorson, A. E., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0085256 · Cited by 44
Simulating school closure policies for cost effective pandemic decision making
Araz, Ö. M., Damien, P., Paltiel, A. D., Burke, S., van de Geijn, B., Galvani, A. P., & Ancel, L. W. (2012) · BMC Public Health · doi:10.1186/1471-2458-12-449 · Cited by 46
Middelkoop, K., Bekker, L., Morrow, C. D., Lee, N., & Wood, R. (2014) · BMC Infectious Diseases · doi:10.1186/1471-2334-14-221 · Cited by 46
Head lice predictors and infestation dynamics among primary school children in Norway
Birkemoe, T., Lindstedt, H. H., Ottesen, P., Soleng, A., Næss, Ø., & Rukke, B. A. (2015) · Family Practice · doi:10.1093/fampra/cmv081 · Cited by 46
Cao, E. D., Zagheni, E., Manfredi, P., & Melegaro, A. (2014) · Biostatistics · doi:10.1093/biostatistics/kxu008 · Cited by 46
Cost-Effectiveness of Pertussis Vaccination During Pregnancy in the United States
Atkins, K. E., Fitzpatrick, M. C., Galvani, A. P., & Townsend, J. P. (2016) · American Journal of Epidemiology · doi:10.1093/aje/kwv347 · Cited by 47
Quantifying heterogeneous contact patterns in Japan: a social contact survey
Munasinghe, L., Asai, Y., & Nishiura, H. (2019) · Theoretical Biology and Medical Modelling · doi:10.1186/s12976-019-0102-8 · Cited by 46
Pellis, L., Cauchemez, S., Ferguson, N. M., & Fraser, C. (2020) · Nature Communications · doi:10.1038/s41467-019-14229-4 · Cited by 49
Flasche, S., Lipsitch, M., Ojal, J., & Pinsent, A. (2020) · BMC Medicine · doi:10.1186/s12916-020-01601-1 · Cited by 41
Modeling strict age-targeted mitigation strategies for COVID-19
Chikina, M. D., & Pegden, W. (2020) · PLoS ONE · doi:10.1371/journal.pone.0236237 · Cited by 47
Imitation Dynamics in Population Games on Community Networks
Como, G., Fagnani, F., & Zino, L. (2020) · IEEE Transactions on Control of Network Systems · doi:10.1109/tcns.2020.3032873 · Cited by 46
Yin, L., Zhang, H., Li, Y., Liu, K., Chen, T., Luo, W. P., Lai, S., Li, Y., Tang, X., Li, N., et al. (2021) · Journal of The Royal Society Interface · doi:10.1098/rsif.2021.0112 · Cited by 49
“The close contacts represented regular encounters with a small group of acquaintances in households, workplaces, and schools and daycares, while the casual contacts represented random encounters with strangers at various public places, such as shops and restaurants [36,37].”
BackgroundTesting Fractional doses of COVID-19 Vaccines
Więcek, W., Ahuja, A., Chaudhuri, E., Kremer, M. R., Gomes, A. S., Snyder, C. M., Tabarrok, A. T., & Tan, B. J. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2116932119 · Cited by 43
“Previously published data were used for this work (51, 52).”
Pandemic fatigue impedes mitigation of COVID-19 in Hong Kong
Du, Z., Wang, L., Shan, S., Lam, D., Tsang, T. K., Xiao, J., Gao, H., Yang, B., Ali, S. T., Pei, S., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2213313119 · Cited by 48
Kuchler, T., Russel, D., & Stroebel, J. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3587255 · Cited by 52
Epidemiological drivers of transmissibility and severity of SARS-CoV-2 in England
Perez-Guzman, P. N., Knock, E., Imai, N., Rawson, T., Santoni, C. N., Alcada, J., Whittles, L. K., Thekke Kanapram, D., Sonabend, R., Gaythorpe, K. A. M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-39661-5 · Cited by 42
“To model heterogenous contact rates among age groups, we use the POLYMOD contact matrix for England 48 .”
Structural HolesCapstone, Cautions, and Enthusiasms
Burt, R. S. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296.029 · Cited by 46
Ximenes, R. A. D. A., Martelli, C. M. T., Amaku, M., Sartori, A. M. C., de Soárez, P. C., Novaes, H. M. D., Pereira, L. M. M. B., Moreira, R. C., Figueiredo, G. M., Azevedo, R. S., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0094622 · Cited by 45
Networks of influence and infection: parental choices and childhood disease
Eames, K. T. D. (2009) · Journal of The Royal Society Interface · doi:10.1098/rsif.2009.0085 · Cited by 45
McCaw, J. M., Forbes, K. M., Nathan, P. M., Pattison, P. E., Robins, G. L., Nolan, T. M., & McVernon, J. (2010) · BMC Infectious Diseases · doi:10.1186/1471-2334-10-166 · Cited by 45
Assessing the role of contact tracing in a suspected H7N2 influenza A outbreak in humans in Wales
Eames, K. T. D., Webb, C. R., Thomas, K., Smith, J., Salmon, R. L., & Temple, J. M. F. (2010) · BMC Infectious Diseases · doi:10.1186/1471-2334-10-141 · Cited by 45
Rose, M. A., Damm, O., Greiner, W., Knuf, M., Wutzler, P., Liese, J. G., Krüger, H., Wahn, U., Schaberg, T., Schwehm, M., et al. (2014) · BMC Infectious Diseases · doi:10.1186/1471-2334-14-40 · Cited by 45
Success Is Something to Sneeze At: Influenza Mortality in Cities that Participate in the Super Bowl
Stoecker, C., Sanders, N., & Barreca, A. (2016) · American Journal of Health Economics · doi:10.1162/ajhe_a_00036 · Cited by 45
Gagnon, A., Acosta, E., Hallman, S., Bourbeau, R. R., Dillon, L., Ouellette, N., Earn, D. J. D., Herring, D. A., Inwood, K. E., Madrenas, J., et al. (2018) · mBio · doi:10.1128/mbio.02091-17 · Cited by 45
de Waroux, O. L. P., Flasche, S., Kucharski, A. J., Langendorf, C., Ndazima, D., Mwanga‐Amumpaire, J., Grais, R. F., Cohuet, S., & Edmunds, W. J. (2018) · Epidemics · doi:10.1016/j.epidem.2018.05.008 · Cited by 45
Self-citationFactors favouring the evolution of multidrug resistance in bacteria
Jacopin, E., Lehtinen, S., Débarre, F., & Blanquart, F. (2020) · Journal of The Royal Society Interface · doi:10.1098/rsif.2020.0105 · Cited by 50
Socioeconomic status and cardiovascular health in the COVID-19 pandemic
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Modeling the impact of racial and ethnic disparities on COVID-19 epidemic dynamics
Ma, K. C., Menkir, T. F., Kissler, S. M., Grad, Y. H., & Lipsitch, M. (2021) · eLife · doi:10.7554/elife.66601 · Cited by 45
Empirical evidence on the efficiency of backward contact tracing in COVID-19
Raymenants, J., Geenen, C., Thibaut, J., Nelissen, K., Gorissen, S., & André, E. (2022) · Nature Communications · doi:10.1038/s41467-022-32531-6 · Cited by 44
Keeling, M. J., & White, P. J. (2010) · Journal of The Royal Society Interface · doi:10.1098/rsif.2010.0474 · Cited by 45
State-level tracking of COVID-19 in the United StatesPreprint
Unwin, H. J. T., Mishra, S., Bradley, V. C., Gandy, A., Mellan, T. A., Coupland, H., Ish-Horowicz, J. S., Vollmer, M., Whittaker, C., Filippi, S. L., et al. (2020) · medRxiv · doi:10.1101/2020.07.13.20152355 · Cited by 45
Social contact patterns and control strategies for influenza in the elderly
Towers, S., & Feng, Z. (2012) · Mathematical Biosciences · doi:10.1016/j.mbs.2012.07.007 · Cited by 43
Simulating preventative testing of SARS-CoV-2 in schools: policy implications
Asgary, A., Cojocaru, M. G., Najafabadi, M. M., & Wu, J. (2021) · BMC Public Health · doi:10.1186/s12889-020-10153-1 · Cited by 44
Duan, W., Cao, Z., Wang, Y., Zhu, B., Zeng, D. D., Wang, F., Qiu, X., Song, H., & Wang, Y. (2013) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2013.2256855 · Cited by 44
Wu, D. B., Chaiyakunapruk, N., Chong, H. Y., & Beutels, P. (2015) · Vaccine · doi:10.1016/j.vaccine.2015.01.081 · Cited by 44
Self-citationAmato, M. S., Moore, C. F., Magzamen, S. L., Imm, P., Havlena, J. A., Anderson, H. A., & Kanarek, M. S. (2012) · Annals of Epidemiology · doi:10.1016/j.annepidem.2012.07.004 · Cited by 48
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van Boven, M., van de Kassteele, J., Korndewal, M. J., van Dorp, C. H., Kretzschmar, M., van der Klis, F. R. M., de Melker, H. E., Vossen, A. C. T. M., & van Baarle, D. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005719 · Cited by 44
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Ozella, L., Gesualdo, F., Tizzoni, M., Rizzo, C., Pandolfi, E., Campagna, I., Tozzi, A. E., & Cattuto, C. (2018) · PLoS ONE · doi:10.1371/journal.pone.0198733 · Cited by 44
Lee, L., Zhou, J., Frise, R., Goldhill, D. H., Koszalka, P., Mifsud, E. J., Baba, K., Noda, T., Ando, Y., Sato, K., et al. (2020) · PLoS Pathogens · doi:10.1371/journal.ppat.1008395 · Cited by 44
Epidemiological Measures in the Context of the COVID-19 Pandemic
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Miikkulainen, R. P., Francon, O., Meyerson, E., Qiu, X., Sargent, D., Canzani, E., & Hodjat, B. (2021) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2021.3063217 · Cited by 47
Sypsa, V., Roussos, S., Paraskevis, D. N., Lytras, T., Tsiodras, S., & Hatzakis, A. E. (2021) · Emerging infectious diseases · doi:10.3201/eid2702.203412 · Cited by 43
Health inequities in influenza transmission and surveillance
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Manzira, C. K., Charly, A., & Caulfield, B. M. (2022) · Sustainable Cities and Society · doi:10.1016/j.scs.2022.103770 · Cited by 44
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
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