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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
Cited by
Sorted by citations
Items 401–500 of 1,003
Ewing, A., Lee, E. C., Viboud, C. G., & Bansal, S. (2016) · The Journal of Infectious Diseases · doi:10.1093/infdis/jiw642 · Cited by 66
The role of schools and school-aged children in SARS-CoV-2 transmission
Flasche, S., & Edmunds, W. J. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30927-0 · Cited by 64
Self-citationPeer, V., Schwartz, N., & Green, M. S. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2022.997025 · Cited by 66
“To the best of our knowledge, in children and especially in children of pubertal age, there are no data to suggest that girls have more frequent exposure to infectious TB cases compared to boys of the same age (57).”
BackgroundBehavioral Epidemiology of Infectious Diseases: An Overview
Bauch, C. T., d’Onofrio, A., & Manfredi, P. (2012) · Springer eBooks · doi:10.1007/978-1-4614-5474-8_1 · Cited by 72
Cowling, B. J., Chan, K., Feng, S., Chan, E. L. Y., Lo, J., Peiris, M. J. S., & Chiu, S. S. S. (2014) · Vaccine · doi:10.1016/j.vaccine.2014.07.084 · Cited by 66
Opatowski, L., Fraser, C., Griffin, J. T., de Silva, E., Kerkhove, M. D. V., Lyons, E., Cauchemez, S., & Ferguson, N. M. (2011) · PLoS Pathogens · doi:10.1371/journal.ppat.1002225 · Cited by 66
Cryptic connections illuminate pathogen transmission within community networks
Hoyt, J. R., Langwig, K. E., White, J. P., Kaarakka, H. M., Redell, J. A., Kurta, A., DePue, J. E., Scullon, W. H., Parise, K. L., Foster, J. T., et al. (2018) · Nature · doi:10.1038/s41586-018-0720-z · Cited by 72
Next Generation Technology for Epidemic Prevention and Control: Data-Driven Contact Tracking
Chen, H., Yang, B. X., Pei, H., & Liu, J. (2018) · IEEE Access · doi:10.1109/access.2018.2882915 · Cited by 65
Tomori, D. V., Rübsamen, N., Berger, T., Scholz, S. M., Walde, J., Wittenberg, I., Lange, B., Kuhlmann, A., Horn, J., Mikolajczyk, R., et al. (2021) · BMC Medicine · doi:10.1186/s12916-021-02139-6 · Cited by 72
“Further details about POLYMOD can be found elsewhere [12].”
BackgroundMethodInfluentialSelf-citationPescosolido, B. A., & Smith, E. B. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296 · Cited by 117
Tigoi, C., Gatakaa, H. W., Karani, A., Mugo, D., Kungu, S., Wanjiru, E., Jomo, J., Musyimi, R., Ojal, J., Glass, N. E., et al. (2012) · Clinical Infectious Diseases · doi:10.1093/cid/cis371 · Cited by 65
Quantifying the role of weather on seasonal influenza
Roussel, M., Pontier, D., Cohen, J., Lina, B., & Fouchet, D. (2016) · BMC Public Health · doi:10.1186/s12889-016-3114-x · Cited by 65
Althouse, B. M., Hammitt, L. L., Grant, L. R., Wagner, B. G., Reid, R., Larzelere‐Hinton, F., Weatherholtz, R., Klugman, K. P., Rodgers, G. L., O’Brien, K. L., et al. (2017) · Epidemiology and Infection · doi:10.1017/s095026881700125x · Cited by 65
Borgonovi, F., Andrieu, E., & Subramanian, S. V. (2021) · Social Science & Medicine · doi:10.1016/j.socscimed.2021.113948 · Cited by 66
Eriksen, J., Davidkin, I., Kafatos, G., Andrews, N. J., Bárbara, C., Cohen, D., Duks, A., Griškevičius, A., Johansen, K., Bartha, K., et al. (2012) · Epidemiology and Infection · doi:10.1017/s0950268812001136 · Cited by 65
Self-citationHouse, T., Baguelin, M., van Hoek, A. J., White, P. J., Sadique, Z., Eames, K. T. D., Read, J. M., Hens, N., Melegaro, A., Edmunds, W. J., et al. (2011) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2010.2688 · Cited by 64
Self-citationIncorporating social contact data in spatio-temporal models for infectious disease spread
Meyer, S., & Held, L. (2016) · Biostatistics · doi:10.1093/biostatistics/kxw051 · Cited by 62
Gabutti, G., Azzari, C., Bonanni, P., Prato, R., Tozzi, A. E., Zanetti, A. R., & Zuccotti, G. (2014) · Human Vaccines & Immunotherapeutics · doi:10.4161/hv.34364 · Cited by 64
Mandated and Voluntary Social Distancing during the COVID-19 Epidemic
Gupta, S., Simon, K. I., & Wing, C. (2020) · Brookings Papers on Economic Activity · doi:10.1353/eca.2020.0011 · Cited by 64
Optimal H1N1 vaccination strategies based on self-interest versus group interest
Shim, E., Meyers, L. A., & Galvani, A. P. (2011) · BMC Public Health · doi:10.1186/1471-2458-11-s1-s4 · Cited by 64
A large-scale immuno-epidemiological simulation of influenza A epidemics
Lukens, S., DePasse, J. V., Rosenfeld, R., Ghedin, E., Mochan, E., Brown, S. T., Grefenstette, J. J., Burke, D. S., Swigon, D., & Clermont, G. (2014) · BMC Public Health · doi:10.1186/1471-2458-14-1019 · Cited by 63
Viner, R., Waddington, C. S., Mytton, O. T., Booy, R., Cruz, J., Ward, J., Ladhani, S. N., Panovska‐Griffiths, J., Bonell, C. P., & Meléndez‐Torres, G. J. (2021) · Journal of Infection · doi:10.1016/j.jinf.2021.12.026 · Cited by 64
“of transmission.[69] Our findings may also reflect the successful operation of NPI mitigations within 370”
Gavenčiak, T., Monrad, J. T., Leech, G., Sharma, M., Mindermann, S., Brauner, J., Bhatt, S., & Kulveit, J. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010435 · Cited by 63
Geard, N. L., Glass, K., McCaw, J. M., McBryde, E. S., Korb, K. B., Keeling, M. J., & McVernon, J. (2015) · Epidemics · doi:10.1016/j.epidem.2015.08.002 · Cited by 63
Compensating for population sampling in simulations of epidemic spread on temporal contact networks
Génois, M., Vestergaard, C. L., Cattuto, C., & Barrat, A. (2015) · Nature Communications · doi:10.1038/ncomms9860 · Cited by 63
Modelling the impact of varicella vaccination on varicella and zoster
Karhunen, M., Leino, T. M., Salo, H., Davidkin, I., Kilpi, T. M., & Auranen, K. J. (2009) · Epidemiology and Infection · doi:10.1017/s0950268809990768 · Cited by 63
Self-citationSexually transmitted infections in polygamous mating systems
Ashby, B., & Gupta, S. (2013) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2012.0048 · Cited by 63
Edwards, C. H., Tomba, G. S., & de Blasio, B. F. (2016) · European Journal of Public Health · doi:10.1093/eurpub/ckw031 · Cited by 63
Self-citationThe evolution of antibiotic resistance in a structured host population
Blanquart, F., Lehtinen, S., Lipsitch, M., & Fraser, C. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2018.0040 · Cited by 63
Jackson, C., Vynnycky, E., & Mangtani, P. (2009) · American Journal of Epidemiology · doi:10.1093/aje/kwp394 · Cited by 63
The impact of social distancing on COVID19 spread: State of Georgia case study
Keskinocak, P., Oruc, B. E., Baxter, A., Asplund, J., & Serban, N. (2020) · PLoS ONE · doi:10.1371/journal.pone.0239798 · Cited by 62
Game-theoretic modeling of collective decision making during epidemics
Ye, M., Zino, L., Rizzo, A., & Cao, M. (2021) · Physical review. E · doi:10.1103/physreve.104.024314 · Cited by 66
Glatman‐Freedman, A., Portelli, I., Jacobs, S. K., Mathew, J. I., Slutzman, J. E., Goldfrank, L. R., & Smith, S. W. (2012) · PLoS ONE · doi:10.1371/journal.pone.0050228 · Cited by 62
Nurses’ Contacts and Potential for Infectious Disease Transmission
Bernard, H., Fischer, R., Mikolajczyk, R., Kretzschmar, M., & Wildner, M. (2009) · Emerging infectious diseases · doi:10.3201/eid1509.081475 · Cited by 62
Self-citationMandated and Voluntary Social Distancing During The COVID-19 Epidemic: A Review
Gupta, S., Simon, K. I., & Wing, C. (2020) · National Bureau of Economic Research · doi:10.3386/w28139 · Cited by 69
Harpaz, R. (2019) · Expert Review of Vaccines · doi:10.1080/14760584.2019.1646129 · Cited by 61
Lopman, B. A., Liu, C. Y., Guillou, A. L., Handel, A., Lash, T. L., Isakov, A. P., & Jenness, S. M. (2021) · Scientific Reports · doi:10.1038/s41598-021-85252-z · Cited by 60
Association between tuberculosis in men and social network structure in Kampala, Uganda
Miller, P. B., Zalwango, S. K., Galiwango, R. M., Kakaire, R., Sekandi, J. N., Steinbaum, L., Drake, J. M., Whalen, C. C., & Kiwanuka, N. (2021) · BMC Infectious Diseases · doi:10.1186/s12879-021-06475-z · Cited by 61
“Based on known preferential mixing patterns [29, 34], we also expected to find preferential mixing patterns by sex and age.”
BackgroundChoi, Y. H., Jit, M., Gay, N. J., Andrews, N. J., Waight, P. A., Melegaro, A., George, R. C., & Miller, E. (2011) · PLoS ONE · doi:10.1371/journal.pone.0026190 · Cited by 61
Self-citationGoeyvaerts, N., Willem, L., Kerckhove, K. V., Vandendijck, Y., Hanquet, G., Beutels, P., & Hens, N. (2015) · Epidemics · doi:10.1016/j.epidem.2015.04.002 · Cited by 61
Self-citationGuzzetta, G., Poletti, P., Ajelli, M., Trentini, F., Marziano, V., Cereda, D., Tirani, M., Diurno, G., Bodina, A., Barone, A. A., et al. (2020) · Eurosurveillance · doi:10.2807/1560-7917.es.2020.25.12.2000293 · Cited by 60
Modelling insights into the COVID-19 pandemic
Meehan, M. T., Rojas, D. P., Adekunle, A. I., Adegboye, O. A., Caldwell, J. M., Turek, E. M., Williams, B. M., Marais, B. J., Trauer, J. M., & McBryde, E. S. (2020) · Paediatric Respiratory Reviews · doi:10.1016/j.prrv.2020.06.014 · Cited by 62
Shut and re-open: the role of schools in the spread of COVID-19 in Europe
Stage, H. B., Shingleton, J., Ghosh, S., Scarabel, F., Pellis, L., & Finnie, T. (2021) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2020.0277 · Cited by 66
How the individual human mobility spatio-temporally shapes the disease transmission dynamics
Changruenngam, S., Bicout, D., & Modchang, C. (2020) · Scientific Reports · doi:10.1038/s41598-020-68230-9 · Cited by 65
The impact of mask-wearing and shelter-in-place on COVID-19 outbreaks in the United States
Zhang, K., VILCHES, T. N., Tariq, M., Galvani, A. P., & Moghadas, S. M. (2020) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2020.10.002 · Cited by 62
Fava, E. D., Cimentada, J., Perrotta, D., Grow, A., Rampazzo, F., Gil‐Clavel, S., & Zagheni, E. (2021) · BMJ Open · doi:10.1136/bmjopen-2021-050651 · Cited by 64
Real-time pandemic surveillance using hospital admissions and mobility data
Fox, S. J., Lachmann, M., Tec, M., Pasco, R. F., Woody, S., Du, Z., Wang, X., Ingle, T. A., Javan, E. M., Dahan, M., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2111870119 · Cited by 60
“The simplest models assumed that populations are entirely homogeneous and well mixed, others incorporated age-specific contact patterns from diary-based surveys (85) or inferred from epidemiological data (86), and still others assumed complex networks of interactions based on sociological data sources (87, 88).”
BackgroundWong, K. L., Gimma, A., Coletti, P., CoMix Europe Working Group, Paolotti, D., Tizzani, M., Cattuto, C., Schmidt, A., Gredinger, G., Stumpfl, S., et al. (2023) · BMC Infectious Diseases · doi:10.1186/s12879-023-08214-y · Cited by 60
“In the UK, for instance, at its highest recorded level, the mean number of contacts in 2021 only returned to approximately 50% of that observed in pre-pandemic times (as measured in the POLYMOD contact survey study in 2005 to 2006 [11]) [12].”
BackgroundMethodResultInfluentialSelf-citationKinyanjui, T., House, T., Kiti, M. C., Cane, P. A., Nokes, D. J., & Medley, G. F. (2015) · PLoS ONE · doi:10.1371/journal.pone.0138018 · Cited by 60
The statistics of epidemic transitions
Drake, J. M., Brett, T. S., Chen, S., Epureanu, B. I., Ferrari, M. J., Marty, É., Miller, P. B., O’Dea, E. B., O’Regan, S. M., PARK, A. W., et al. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006917 · Cited by 65
Lourenço, J., Obolski, U., Swarthout, T. D., Gori, A., Bar‐Zeev, N., Everett, D., Kamng’ona, A. W., Mwalukomo, T. S., Mataya, A. A., Mwansambo, C., et al. (2019) · BMC Medicine · doi:10.1186/s12916-019-1450-2 · Cited by 56
Lokuge, K., Banks, E., Davis, S. I., Roberts, L., Street, T., O’Donovan, D., Caleo, G., & Glass, K. (2021) · BMC Medicine · doi:10.1186/s12916-021-01934-5 · Cited by 58
Resolving the impact of waiting time distributions on the persistence of measles
Conlan, A. J. K., Rohani, P., Lloyd, A. L., Keeling, M. J., & Grenfell, B. T. (2009) · Journal of The Royal Society Interface · doi:10.1098/rsif.2009.0284 · Cited by 59
Capturing norovirus transmission
de Graaf, M., Villabruna, N., & Koopmans, M. (2017) · Current Opinion in Virology · doi:10.1016/j.coviro.2016.11.008 · Cited by 59
Impact of vaccination and non-pharmaceutical interventions on SARS-CoV-2 dynamics in Switzerland
Shattock, A. J., Rutte, E. A. L., Dünner, R. P., Sen, S., Kelly, S. L., Chitnis, N., & Penny, M. A. (2021) · Epidemics · doi:10.1016/j.epidem.2021.100535 · Cited by 64
Impact of rotavirus vaccination on epidemiological dynamics in England and Wales
Atkins, K. E., Shim, E., Pitzer, V. E., & Galvani, A. P. (2011) · Vaccine · doi:10.1016/j.vaccine.2011.11.064 · Cited by 59
Artificial Intelligence Applications in Tracking Health Behaviors During Disease Epidemics
Ganasegeran, K., & Abdulrahman, S. A. (2019) · Learning and analytics in intelligent systems · doi:10.1007/978-3-030-35139-7_7 · Cited by 62
Modelling scenarios of the epidemic of COVID-19 in Canada
Ogden, N. H., Fazil, A., Arino, J., Berthiaume, P., Fisman, D. N., Greer, A. L., Ludwig, A., Ng, V., Tuite, A. R., Turgeon, P., et al. (2020) · Canada Communicable Disease Report · doi:10.14745/ccdr.v46i06a08 · Cited by 60
Seasonal patterns in time series of pertussis
de Greeff, S. C., Dekkers, A. L. M., Teunis, P. F. M., Rahamat-Langendoen, J. C., Mooi, F. R., & de Melker, H. E. (2009) · Epidemiology and Infection · doi:10.1017/s0950268809002489 · Cited by 59
Production Networks and Epidemic Spreading: How to Restart the UK Economy?Preprint
Pichler, A., Pangallo, M., del Rio-Chanona, R. M., Lafond, F., & Farmer, J. D. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3606984 · Cited by 59
A decision-support framework to optimize border control for global outbreak mitigation
Zlojutro, A., Rey, D., & Gardner, L. (2019) · Scientific Reports · doi:10.1038/s41598-019-38665-w · Cited by 58
Horton, K. C., Hoey, A. L., Béraud, G., Corbett, E. L., & White, R. G. (2020) · Emerging infectious diseases · doi:10.3201/eid2605.190574 · Cited by 58
Dowdy, D. W., & Behr, M. A. (2022) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(22)00153-0 · Cited by 58
New strategies for control of respiratory syncytial virus infection
Nokes, D. J., & Cane, P. A. (2008) · Current Opinion in Infectious Diseases · doi:10.1097/qco.0b013e3283184245 · Cited by 58
Bhattacharyya, S. K., & Bauch, C. T. (2010) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2010.09.005 · Cited by 58
Household members do not contact each other at random: implications for infectious disease modelling
Goeyvaerts, N., Santermans, E., Potter, G. E., Torneri, A., Kerckhove, K. V., Willem, L., Aerts, M., Beutels, P., & Hens, N. (2018) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2018.2201 · Cited by 58
Self-citationUnraveling the seasonal epidemiology of pneumococcus
de Cellès, M. D., Arduin, H., Lévy-Bruhl, D., Georges, S., Souty, C., Guillemot, D., Watier, L., & Opatowski, L. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1812388116 · Cited by 52
Age and generational patterns of overdose death risk from opioids and other drugs
Jalal, H. J., Buchanich, J. M., Sinclair, D. R., Roberts, M. S., & Burke, D. S. (2020) · Nature Medicine · doi:10.1038/s41591-020-0855-y · Cited by 61
Contact tracing – Old models and new challenges
Müller, J., & Kretzschmar, M. (2020) · Infectious Disease Modelling · doi:10.1016/j.idm.2020.12.005 · Cited by 56
Jiang, L., Ng, I. H. L., Hou, Y., Li, D., Tan, L. W. L., Ho, H. J. A., & Chen, M. I.-C. (2017) · Journal of Hospital Infection · doi:10.1016/j.jhin.2017.10.020 · Cited by 56
van Hoek, A. J., Andrews, N. J., Campbell, H. J., Amirthalingam, G., Edmunds, W. J., & Miller, E. (2013) · PLoS ONE · doi:10.1371/journal.pone.0076180 · Cited by 57
Self-citationA Sign of Superspreading in Tuberculosis
Ypma, R. J. F., Altes, H. K., van Soolingen, D., Wallinga, J., & van Ballegooijen, W. M. (2013) · Epidemiology · doi:10.1097/ede.0b013e3182878e19 · Cited by 59
Self-citationEstimating the clinical impact of introducing paediatric influenza vaccination in England and Wales
Pitman, R., White, L. J., & Sculpher, M. (2011) · Vaccine · doi:10.1016/j.vaccine.2011.11.106 · Cited by 57
Distribution of vaccine/antivirals and the ‘least spread line’ in a stratified population
Goldstein, E., Apolloni, A., Lewis, B., Miller, J. C., Macauley, M., Eubank, S., Lipsitch, M., & Wallinga, J. (2009) · Journal of The Royal Society Interface · doi:10.1098/rsif.2009.0393 · Cited by 56
Self-citationStatistical inference to advance network models in epidemiology
Welch, D., Bansal, S., & Hunter, D. R. (2011) · Epidemics · doi:10.1016/j.epidem.2011.01.002 · Cited by 54
Prevalence of antibodies against influenza A and B viruses in children in Germany, 2008 to 2010
Sauerbrei, A., Langenhan, T., Brandstädt, A., Schmidt‐Ott, R., Krumbholz, A., Girschick, H. J., Huppertz, H., Kaiser, P. K., Liese, J. G., Streng, A., et al. (2014) · Eurosurveillance · doi:10.2807/1560-7917.es2014.19.5.20687 · Cited by 57
Hyon, R., Youm, Y., Kim, J., Chey, J., Kwak, S., & Parkinson, C. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2013606117 · Cited by 57
The emergence of SARS-CoV-2 in Europe and the USPreprint
Worobey, M., Pekar, J. E., Larsen, B. B., Nelson, M. I., Hill, V., Joy, J. B., Rambaut, A., Suchard, M. A., Wertheim, J. O., & Lemey, P. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.21.109322 · Cited by 60
Schimit, P. H. T. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105832 · Cited by 56
Epidemic Spreading in Metapopulation Networks Coupled With Awareness Propagation
Gao, S., Dai, X., Wang, L., Perra, N., & Wang, Z. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2022.3198732 · Cited by 56
“To account for the age-related heterogeneity in human contact patterns [5], recent mathematical models have extended the compartmental models to incorporate age-stratified contact rates [6], [7], [8], [9].”
MethodSocial contacts and the locations in which they occur as risk factors for influenza infection
Kwok, K. O., Cowling, B. J., Wei, V. W. I., Wu, K. M., Read, J. M., Lessler, J., Cummings, D. A. T., Peiris, M. J. S., & Riley, S. (2014) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2014.0709 · Cited by 56
Smieszek, T., Balmer, M., Hattendorf, J., Axhausen, K. W., Zinsstag, J., & Scholz, R. W. (2011) · BMC Infectious Diseases · doi:10.1186/1471-2334-11-115 · Cited by 56
Ferrari, M. J., Grenfell, B. T., & Strebel, P. M. (2013) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2012.0141 · Cited by 56
Potential impact of a maternal vaccine for RSV: A mathematical modelling study
Hogan, A. B., Campbell, P. T., Blyth, C. C., Lim, F. J., Fathima, P., Davis, S. I., Moore, H. C., & Glass, K. (2017) · Vaccine · doi:10.1016/j.vaccine.2017.09.043 · Cited by 56
Prevalence and characteristics of acute respiratory virus infections in pediatric cancer patients
Soudani, N., Caniza, M. A., Assaf‐Casals, A., Shaker, R., Lteif, M., Su, Y., Tang, L., Akel, I. S., Muwakkit, S. A., Chmaisse, A., et al. (2019) · Journal of Medical Virology · doi:10.1002/jmv.25432 · Cited by 55
Metcalf, C. J. E., Tate, A. T., & Graham, A. L. (2017) · Nature Ecology & Evolution · doi:10.1038/s41559-017-0315-3 · Cited by 54
Kretzschmar, M., & Mikolajczyk, R. (2009) · PLoS ONE · doi:10.1371/journal.pone.0005931 · Cited by 55
Self-citationChoi, Y. H., Jit, M., Flasche, S., Gay, N. J., & Miller, E. (2012) · PLoS ONE · doi:10.1371/journal.pone.0039927 · Cited by 55
Self-citationOptimizing the impact of low-efficacy influenza vaccines
Sah, P., Medlock, J., Fitzpatrick, M. C., Singer, B. H., & Galvani, A. P. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1802479115 · Cited by 54
Choi, Y. H., Andrews, N. J., & Miller, E. (2019) · PLoS Medicine · doi:10.1371/journal.pmed.1002845 · Cited by 55
Yang, W., Shaff, J., & Shaman, J. L. (2021) · Journal of The Royal Society Interface · doi:10.1098/rsif.2020.0822 · Cited by 58
Retrospective public health impact of a quadrivalent influenza vaccine in the United States
Crépey, P., de Boer, P. T., Postma, M. J., & Pitman, R. (2015) · Influenza and Other Respiratory Viruses · doi:10.1111/irv.12318 · Cited by 54
The importance of dog population contact network structures in rabies transmission
Laager, M., Mbilo, C., Madaye, E., Naminou, A., Léchenne, M. S., Tschopp, A., Naïssengar, S., Smieszek, T., Zinsstag, J., & Chitnis, N. (2018) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0006680 · Cited by 54
Zhang, J., Litvinova, M., Liang, Y., Wang, Y., Wang, W., Zhao, S., Wu, Q., Merler, S., Viboud, C. G., Vespignani, A., et al. (2020) · medRxiv · doi:10.1101/2020.03.19.20039107 · Cited by 54
The impact of demographic changes on the epidemiology of herpes zoster: Spain as a case study
Marziano, V., Poletti, P., Guzzetta, G., Ajelli, M., Manfredi, P., & Merler, S. (2015) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2014.2509 · Cited by 53
Graffigna, G., Barello, S., Savarese, M., Palamenghi, L., Castellini, G., Bonanomi, A. G., & Lozza, E. (2020) · PLoS ONE · doi:10.1371/journal.pone.0238613 · Cited by 53
The effect of COVID-19 vaccination in Italy and perspectives for living with the virus
Marziano, V., Guzzetta, G., Mammone, A., Riccardo, F., Poletti, P., Trentini, F., Manica, M., Siddu, A., Bella, A., Stefanelli, P., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-27532-w · Cited by 55
“Mixing patterns are assumed to be heterogeneous across ages according to an age-specific social contact matrix estimated prior to the COVID-19 pandemic [3].”
BackgroundInfluentialModeling for COVID-19 college reopening decisions: Cornell, a case study
Frazier, P. I., Cashore, J. M., Duan, N., Henderson, S. G., Janmohamed, A., Liu, B., Shmoys, D. B., Wan, J., & Zhang, Y. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2112532119 · Cited by 53
“This could be dangerous because these young people may be socially cohesive, and, during normal times, young people have elevated rates of social contact (5, 45).”
Distinguishing Simple and Complex Contagion Processes on Networks
Cencetti, G., Contreras, D. A., Mancastroppa, M., & Barrat, A. (2023) · Physical Review Letters · doi:10.1103/physrevlett.130.247401 · Cited by 56
Opatowski, L., Mandel, J., Varon, E., Boëlle, P., Temime, L., & Guillemot, D. (2010) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.00331-09 · Cited by 53
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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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.
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