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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Younger individuals tend to have more contacts than older age groups.
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Generalized Additive Models: An Introduction with R
Wood, S. N. (2017) · doi:10.1201/9781315370279 · Cited by 13,852 · see citing works
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 2,195 · cited 2× · see citing works
“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 2,091 · cited 1× · see citing works
“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 · see citing works
Mathematical Epidemiology of Infectious Diseases: Model Building, Analysis and Interpretation
Diekmann, O., & Heesterbeek, H. (2000) · Socio-Environmental Systems Modeling · Cited by 1,863 · see citing works
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 1,467 · cited 1× · see citing works
“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 1,208 · cited 1× · see citing works
“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 1,158 · cited 1× · see citing works
“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× · see citing works
“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× · see citing works
“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].”
MethodPublic health interventions and epidemic intensity during the 1918 influenza pandemic
Hatchett, R. J., Mecher, C. E., & Lipsitch, M. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0610941104 · Cited by 778 · cited 1× · see citing works
“Both can be achieved by nonpharmaceutical interventions such as school closure, travel restrictions, and contact tracing, or by health-care interventions such as vaccination and use of antiviral or antibiotic agents [2, [10] [11] [12] [13] .”
BackgroundHilbe, J. M. (2007) · Cambridge University Press eBooks · doi:10.1017/cbo9780511811852 · Cited by 756 · see citing works
Mathematical Epidemiology of Infectious Diseases
Diekmann, O. (1996) · Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands · Cited by 722 · see citing works
Wallinga, J., Teunis, P. F. M., & Kretzschmar, M. (2006) · American Journal of Epidemiology · doi:10.1093/aje/kwj317 · Cited by 702 · cited 2× · see citing works
“Contact rates between individuals are often critical determinants of model outcomes [14].”
BackgroundResultSelf-citationRecent Developments in Network Measurement
Marsden, P. V. (2005) · Cambridge University Press eBooks · doi:10.1017/cbo9780511811395.002 · Cited by 651 · see citing works
Sexual mixing patterns and sex-differentials in teenage exposure to HIV infection in rural Zimbabwe
Gregson, S., Nyamukapa, C. A., Garnett, G. P., Mason, P. R., Zhuwau, T., Caraël, M., Chandiwana, S. K., & Anderson, R. M. (2002) · The Lancet · doi:10.1016/s0140-6736(02)08780-9 · Cited by 582 · see citing works
Fenton, K. A., Korovessis, C., Johnson, A. M., McCadden, A., McManus, S., Wellings, K., Mercer, C. H., Carder, C., Copas, A. J., Nanchahal, K., et al. (2001) · The Lancet · doi:10.1016/s0140-6736(01)06886-6 · Cited by 428 · cited 1× · see citing works
“In comparison to HIV and sexually transmitted diseases [15] [16] [17] and drug/needle sharing networks [18] , where a number of large-scale empirical studies have been conducted on contact patterns, relatively little effort has been devoted to infections spread by respiratory droplets or close contact.”
BackgroundDye, C., & Gay, N. J. (2003) · Science · doi:10.1126/science.1086925 · Cited by 278 · see citing works
Edmunds, W. J., O'Callaghan, C. J., & Nokes, D. J. (1997) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.1997.0131 · Cited by 277 · cited 1× · see citing works
“A small number of studies have attempted to directly quantify such contact patterns, but they were conducted in small or nonrepresentative populations [14, [21] [22] [23] [24] [25] .”
BackgroundSelf-citationReducing the Impact of the Next Influenza Pandemic Using Household-Based Public Health Interventions
Wu, J. T. K., Riley, S., Fraser, C., & Leung, G. M. (2006) · PLoS Medicine · doi:10.1371/journal.pmed.0030361 · Cited by 256 · cited 1× · see citing works
“Both can be achieved by nonpharmaceutical interventions such as school closure, travel restrictions, and contact tracing, or by health-care interventions such as vaccination and use of antiviral or antibiotic agents [2, [10] [11] [12] [13] .”
BackgroundFarrington, P., Kanaan, M. N., & Gay, N. (2001) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/1467-9876.00233 · Cited by 186 · cited 1× · see citing works
“], where a number of large-scale empirical studies have been conducted on contact patterns, relatively little effort has been devoted to infections spread by respiratory droplets or close contact. Instead, the contact structure for these infections has been assumed to follow a predetermined pattern governed by a small…”
BackgroundReal-time Estimates in Early Detection of SARS
Cauchemez, S., Boëlle, P., Donnelly, C. A., Ferguson, N. M., Thomas, G., Leung, G. M., Hedley, A. J., Anderson, R. M., & Valleron, A. (2006) · Emerging infectious diseases · doi:10.3201/eid1201.050593 · Cited by 169 · cited 1× · see citing works
“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.”
BackgroundSocial contacts of school children and the transmission of respiratory-spread pathogens
Mikolajczyk, R., Akmatov, M. K., RASTIN, S., & Kretzschmar, M. (2007) · Epidemiology and Infection · doi:10.1017/s0950268807009181 · Cited by 157 · cited 1× · see citing works
“A small number of studies have attempted to directly quantify such contact patterns, but they were conducted in small or nonrepresentative populations [14, [21] [22] [23] [24] [25] .”
BackgroundSelf-citationSexual Mixing Patterns of Patients Attending Sexually Transmitted Diseases Clinics
Garnett, G. P., Hughes, J. P., Anderson, R. M., Stoner, B. P., ARAL, S. O., Whittington, W. L. H., Handsfield, H. H., & Holmes, K. K. (1996) · Sexually Transmitted Diseases · doi:10.1097/00007435-199605000-00015 · Cited by 140 · cited 1× · see citing works
“In comparison to HIV and sexually transmitted diseases [15] [16] [17] and drug/needle sharing networks [18] , where a number of large-scale empirical studies have been conducted on contact patterns, relatively little effort has been devoted to infections spread by respiratory droplets or close contact.”
BackgroundA Comparative Analysis of Influenza Vaccination Programs
Bansal, S., Pourbohloul, B., & Ancel, L. W. (2006) · PLoS Medicine · doi:10.1371/journal.pmed.0030387 · Cited by 140 · cited 1× · see citing works
“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].”
MethodMixing patterns and the spread of close-contact infectious diseases
Edmunds, W. J., Kafatos, G., Wallinga, J., & Mossong, J. (2006) · Emerging Themes in Epidemiology · doi:10.1186/1742-7622-3-10 · Cited by 128 · cited 1× · see citing works
“A small number of studies have attempted to directly quantify such contact patterns, but they were conducted in small or nonrepresentative populations [14, [21] [22] [23] [24] [25] .”
BackgroundSelf-citationBeutels, P., Shkedy, Z., Aerts, M., & Damme, P. V. (2006) · Epidemiology and Infection · doi:10.1017/s0950268806006418 · Cited by 124 · cited 1× · see citing works
“There may be other at-risk events that our methodology does not capture, such as being in a confined space or in close physical proximity with other individuals and not talking to them [ 23 ].”
BackgroundSelf-citationCarrat, F., Luong, J., Lao, H., Sallé, A.-V., Lajaunie, C., & Wackernagel, H. (2006) · BMC Medicine · doi:10.1186/1741-7015-4-26 · Cited by 124 · see citing works
Vynnycky, E., & Edmunds, W. J. (2007) · Epidemiology and Infection · doi:10.1017/s0950268807008369 · Cited by 113 · cited 1× · see citing works
“This pattern is likely to be the main reason why children and teenagers are and have been an important conduit for the initial spread of closecontact infections in general and for influenza in particular [11,14] and our preliminary modelling work confirms this.”
ResultSelf-citationEntry screening for severe acute respiratory syndrome (SARS) or influenza: policy evaluation
Pitman, R., Cooper, B. S., Trotter, C. L., Gay, N. J., & Edmunds, W. J. (2005) · BMJ · doi:10.1136/bmj.38573.696100.3a · Cited by 90 · cited 1× · see citing works
“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-citationFu, Y.-C. (2007) · Field Methods · doi:10.1177/1525822x06298590 · Cited by 84 · cited 1× · see citing works
“Despite the limitations of self-reported egocentric data [36] , contact diaries can provide extensive details regarding contact structures and have been used successfully for social network analysis [37] .”
BackgroundNew probabilistic interest measures for association rules
Hahsler, M., & Hornik, K. (2007) · Intelligent Data Analysis · doi:10.3233/ida-2007-11502 · Cited by 68 · cited 1× · see citing works
“For a more extensive overview of applying association rules on contact data see [29].”
BackgroundMikolajczyk, R., & Kretzschmar, M. (2007) · Social Networks · doi:10.1016/j.socnet.2007.09.002 · Cited by 64 · see citing works
Self-citationEvaluation of Interviewing Techniques to Enhance Recall of Sexual and Drug Injection Partners
Brewer, D. D., & Garrett, S. B. (2001) · Sexually Transmitted Diseases · doi:10.1097/00007435-200111000-00010 · Cited by 61 · cited 1× · see citing works
“In comparison to HIV and sexually transmitted diseases [15–17] and drug/needle sharing networks [18], where a number of large-scale empirical studies have been conducted on contact patterns, relatively little effort has been devoted to infections spread by respiratory droplets or close contact.”
BackgroundContact Surface Models for Infectious Diseases
Farrington, P., & Whitaker, H. (2005) · Journal of the American Statistical Association · doi:10.1198/016214504000001754 · Cited by 32 · cited 1× · see citing works
“Instead, the contact structure for these infections has been assumed to follow a predetermined pattern governed by a small number of parameters that are then estimated using seroepidemiological data [19,20].”
BackgroundFry, J. (1958) · BMJ · doi:10.1136/bmj.1.5065.259 · Cited by 32 · see citing works
Estimating the impact of vaccination using age–time-dependent incidence rates of hepatitis B
Hens, N., Aerts, M., Shkedy, Z., Kimani, P., KOJOUHOROVA, M., Damme, P. V., & Beutels, P. (2007) · Epidemiology and Infection · doi:10.1017/s0950268807008692 · Cited by 14 · cited 1× · see citing works
“Contact surface smoothing was performed by applying a negative binomial model on the aggregated number of contacts (both physical and nonphysical) over 5 y age bands for both responders and contacts using a tensor product spline as a smooth interaction term [30, 31] .”
MethodSelf-citation
Flagged first, then by citations
1 of the 38 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England
Davies, N. G., Abbott, S., Barnard, R. C., Jarvis, C. I., Kucharski, A. J., Munday, J. D., Pearson, C. A. B., Russell, T. W., Tully, D. C., Washburne, A. D., et al. (2021) · Science · doi:10.1126/science.abg3055 · Cited by 2,633 · see citing works
“105) 43 (40-46) 72 (67-78) UK sequence separate-slopes binomial GLMM (1|LTLA/OBS) + REGION * DATE London VOC vs.”
BackgroundInfluentialSelf-citationPrem, K., Liu, Y., Russell, T., Kucharski, A. J., Eggo, R. M., Davies, N. G. M., Flasche, S., Clifford, S., Pearson, C. A. B., Munday, J. D., et al. (2020) · The Lancet Public Health · doi:10.1016/s2468-2667(20)30073-6 · Cited by 2,087 · see citing works
Self-citationAge-dependent effects in the transmission and control of COVID-19 epidemics
Davies, N. G., Klepac, P., Liu, Y., Prem, K., Jit, M., CMMID COVID-19 working group, Pearson, C. A. B., Quilty, B. J., Kucharski, A. J., Gibbs, H. P., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0962-9 · Cited by 1,721 · see citing works
Self-citation"Herd Immunity": A Rough Guide
Fine, P. E., Eames, K. T. D., & Heymann, D. (2011) · Clinical Infectious Diseases · doi:10.1093/cid/cir007 · Cited by 1,256 · see citing works
The spread of awareness and its impact on epidemic outbreaks
Funk, S., Gilad, E., Watkins, C., & Jansen, V. A. A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0810762106 · Cited by 1,134 · see citing works
Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures
Gatto, M., Bertuzzo, E., Mari, L., Miccoli, S., Carraro, L., Casagrandi, R., & Rinaldo, A. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2004978117 · Cited by 1,022 · see citing works
Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China
Zhang, J., Litvinova, M., Liang, Y., Wang, Y., Wang, W., Zhao, S., Wu, Q., Merler, S., Viboud, C., Vespignani, A., et al. (2020) · Science · doi:10.1126/science.abb8001 · Cited by 1,107 · see citing works
Walker, P. G. T., Whittaker, C., Watson, O. J., Baguelin, M., Winskill, P., Hamlet, A., Djafaara, B. A., Cucunubá, Z., Olivera Mesa, D., Green, W., et al. (2020) · Science · doi:10.1126/science.abc0035 · Cited by 993 · see citing works
Davies, N. G., Kucharski, A. J., Eggo, R. M., Gimma, A., Edmunds, W. J., Jombart, T., O'Reilly, K., Endo, A., Hellewell, J., Nightingale, E. S., et al. (2020) · The Lancet Public Health · doi:10.1016/s2468-2667(20)30133-x · Cited by 848 · see citing works
Self-citationStatistical physics of vaccination
Wang, Z., Bauch, C. T., Bhattacharyya, S., d’Onofrio, A., Manfredi, P., Perc, M., Perra, N., Salathé, M., & Zhao, D. (2016) · Physics Reports · doi:10.1016/j.physrep.2016.10.006 · Cited by 954 · see citing works
Transmissibility and transmission of respiratory viruses
Leung, N. H. L. (2021) · Nature Reviews Microbiology · doi:10.1038/s41579-021-00535-6 · Cited by 890 · see citing works
Projecting social contact matrices in 152 countries using contact surveys and demographic data
Prem, K., Cook, A. R., & Jit, M. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005697 · Cited by 889 · see citing works
Self-citationHigh-Resolution Measurements of Face-to-Face Contact Patterns in a Primary School
Stehlé, J., Voirin, N., Barrat, A., Cattuto, C., Isella, L., Pinton, J.-F., Quaggiotto, M., den Broeck, W. V., Régis, C., Lina, B., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0023176 · Cited by 834 · see citing works
Quantifying the impact of physical distance measures on the transmission of COVID-19 in the UK
CMMID COVID-19 working group, Jarvis, C. I., Van Zandvoort, K., Gimma, A., Prem, K., Klepac, P., Rubin, G. J., & Edmunds, W. J. (2020) · BMC Medicine · doi:10.1186/s12916-020-01597-8 · Cited by 720 · see citing works
Self-citationAleta, A., Martín-Corral, D., y Piontti, A. P., Ajelli, M., Litvinova, M., Chinazzi, M., Dean, N. E., Halloran, M. E., Longini, I. M., Merler, S., et al. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-0931-9 · Cited by 810 · see citing works
Dynamics of Person-to-Person Interactions from Distributed RFID Sensor Networks
Cattuto, C., den Broeck, W. V., Barrat, A., Vittoria Colizza, A., Pinton, J.-F., & Vespignani, A. (2010) · PLoS ONE · doi:10.1371/journal.pone.0011596 · Cited by 823 · see citing works
Viner, R., Russell, S., Saulle, R., Croker, H. A., Stansfield, C., Packer, J., Nicholls, D., Goddings, A., Bonell, C. P., Hudson, L. D., et al. (2022) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/jamapediatrics.2021.5840 · Cited by 798 · see citing works
Miller, E., Höschler, K., Hardelid, P., Stanford, E., Andrews, N. J., & Zambon, M. C. F. A. (2010) · The Lancet · doi:10.1016/s0140-6736(09)62126-7 · Cited by 708 · see citing works
Impact of delays on effectiveness of contact tracing strategies for COVID-19: a modelling study
Kretzschmar, M. E., Rozhnova, G., Bootsma, M. C. J., van Boven, M., van de Wijgert, J. H. H. M., & Bonten, M. J. M. (2020) · The Lancet Public Health · doi:10.1016/s2468-2667(20)30157-2 · Cited by 697 · see citing works
Covasim: An agent-based model of COVID-19 dynamics and interventions
Kerr, C. C., Stuart, R. M., Mistry, D., Abeysuriya, R., Rosenfeld, K., Hart, G. R., Núñez, R. C., Cohen, J. A., Selvaraj, P., Hagedorn, B., et al. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009149 · Cited by 676 · see citing works
The Impact of Vaccination on Coronavirus Disease 2019 (COVID-19) Outbreaks in the United States
Moghadas, S. M., VILCHES, T. N., Zhang, K., Wells, C. R., Shoukat, A., Singer, B. H., Ancel, L. W., Neuzil, K. M., Langley, J. M., Fitzpatrick, M. C., et al. (2021) · Clinical Infectious Diseases · doi:10.1093/cid/ciab079 · Cited by 674 · see citing works
Meningococcal carriage by age: a systematic review and meta-analysis
Christensen, H., May, M., Bowen, L., Hickman, M., & Trotter, C. L. (2010) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(10)70251-6 · Cited by 650 · see citing works
Jusup, M., Holme, P., Kanazawa, K., Takayasu, M., Romić, I., Wang, Z., Geček, S., Lipić, T., Boris Podobnik, A., Wang, L., et al. (2022) · Physics Reports · doi:10.1016/j.physrep.2021.10.005 · Cited by 662 · see citing works
Islam, N., Sharp, S. J., Chowell, G., Shabnam, S., Kawachi, I., Lacey, B., Massaro, J. M., D’Agostino, R. B., & White, M. (2020) · BMJ · doi:10.1136/bmj.m2743 · Cited by 601 · see citing works
Biggerstaff, M., Cauchemez, S., Reed, C., Gambhir, M., & Finelli, L. (2014) · BMC Infectious Diseases · doi:10.1186/1471-2334-14-480 · Cited by 609 · see citing works
Global circulation patterns of seasonal influenza viruses vary with antigenic drift
Bedford, T., Riley, S., Barr, I., Broor, S., Chadha, M., Cox, N. J., Daniels, R. S., Gunasekaran, C. P., Hurt, A. C., Kelso, A., et al. (2015) · Nature · doi:10.1038/nature14460 · Cited by 603 · see citing works
Mastrandrea, R., Fournet, J., & Barrat, A. (2015) · PLoS ONE · doi:10.1371/journal.pone.0136497 · Cited by 590 · see citing works
Dynamics and Control of Diseases in Networks with Community Structure
Salathé, M., & Jones, J. H. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000736 · Cited by 586 · see citing works
Coupled disease–behavior dynamics on complex networks: A review
Wang, Z., Andrews, M. A., Wu, Z.-X., Wang, L., & Bauch, C. T. (2015) · Physics of Life Reviews · doi:10.1016/j.plrev.2015.07.006 · Cited by 551 · see citing works
Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England
Davies, N. G., Abbott, S., Barnard, R. C., Jarvis, C. I., Kucharski, A. J., Munday, J. D., Pearson, C. A. B., Russell, T. W., Tully, D. C., Washburne, A. D., et al. (2020) · doi:10.1101/2020.12.24.20248822 · Cited by 2,557 · see citing works
Self-citationSocial Network Sensors for Early Detection of Contagious Outbreaks
Christakis, N. A., & Fowler, J. H. (2010) · PLoS ONE · doi:10.1371/journal.pone.0012948 · Cited by 535 · see citing works
Modelling transmission and control of the COVID-19 pandemic in Australia
Chang, S. L., Harding, N., Zachreson, C., Cliff, O. M., & Prokopenko, M. (2020) · RePEc: Research Papers in Economics · Cited by 523 · see citing works
Global Influenza Seasonality: Reconciling Patterns across Temperate and Tropical Regions
Tamerius, J. D., Nelson, M. I., Zhou, S., Viboud, C. G., Miller, M. A., & Alonso, W. J. (2010) · Environmental Health Perspectives · doi:10.1289/ehp.1002383 · Cited by 519 · see citing works
Epidemiology and transmission dynamics of COVID-19 in two Indian states
Laxminarayan, R., Wahl, B., Dudala, S. R., Gopal, K., Mohan B, C., Neelima, S., Jawahar Reddy, K. S., Radhakrishnan, J., & Lewnard, J. A. (2020) · Science · doi:10.1126/science.abd7672 · Cited by 485 · see citing works
Salathé, M., Bengtsson, L., Bodnar, T. J., Brewer, D. D., Brownstein, J. S., Buckee, C., Campbell, E. M., Cattuto, C., Khandelwal, S., Mabry, P. L., et al. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002616 · Cited by 500 · see citing works
Potent protection against H5N1 and H7N9 influenza via childhood hemagglutinin imprinting
Gostic, K. M., Ambrose, M. R., Worobey, M., & Lloyd‐Smith, J. O. (2016) · Science · doi:10.1126/science.aag1322 · Cited by 464 · see citing works
Disease and healthcare burden of COVID-19 in the United States
Miller, I. F., Becker, A. D., Grenfell, B. T., & Metcalf, C. J. E. (2020) · Nature Medicine · doi:10.1038/s41591-020-0952-y · Cited by 460 · see citing works
Spread of a SARS-CoV-2 variant through Europe in the summer of 2020
Hodcroft, E. B., Zuber, M., Nadeau, S., Vaughan, T. G., Crawford, K. H. D., Althaus, C. L., Reichmuth, M. L., Bowen, J. E., Walls, A. C., Corti, D., et al. (2021) · Nature · doi:10.1038/s41586-021-03677-y · Cited by 425 · see citing works
Networks and the Epidemiology of Infectious Disease
Danon, L., Ford, A. P., House, T., Jewell, C., Keeling, M. J., Roberts, G. O., Ross, J. V., & Vernon, M. C. (2011) · Interdisciplinary Perspectives on Infectious Diseases · doi:10.48550/arxiv.1011.5950 · Cited by 457 · see citing works
The implications of silent transmission for the control of COVID-19 outbreaks
Moghadas, S. M., Fitzpatrick, M. C., Sah, P., Pandey, A., Shoukat, A., Singer, B. H., & Galvani, A. P. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2008373117 · Cited by 448 · see citing works
Optimizing Influenza Vaccine Distribution
Medlock, J., & Galvani, A. P. (2009) · Science · doi:10.1126/science.1175570 · Cited by 438 · see citing works
Wrong but Useful — What Covid-19 Epidemiologic Models Can and Cannot Tell Us
Holmdahl, I. E., & Buckee, C. O. (2020) · New England Journal of Medicine · doi:10.1056/nejmp2016822 · Cited by 433 · see citing works
How simulation modelling can help reduce the impact of COVID-19
Currie, C. S. M., Fowler, J., Kotiadis, K., Monks, T., Onggo, B. S., Robertson, D. A., & Tako, A. A. (2020) · Journal of Simulation · doi:10.1080/17477778.2020.1751570 · Cited by 425 · see citing works
Transmission heterogeneities, kinetics, and controllability of SARS-CoV-2
Sun, K., Wang, W., Gao, L., Wang, Y., Luo, K., Ren, L., Zhan, Z., Chen, X., Zhao, S., Huang, Y., et al. (2020) · Science · doi:10.1126/science.abe2424 · Cited by 404 · see citing works
Modelling transmission and control of the COVID-19 pandemic in Australia
Chang, S. L., Harding, N., Zachreson, C., Cliff, O. M., & Prokopenko, M. (2020) · Nature Communications · doi:10.1038/s41467-020-19393-6 · Cited by 569 · see citing works
Mathematical epidemiology: Past, present, and future
Brauer, F. (2017) · Infectious Disease Modelling · doi:10.1016/j.idm.2017.02.001 · Cited by 408 · see citing works
Vanhems, P., Barrat, A., Cattuto, C., Pinton, J.-F., Khanafer, N. L., Régis, C., Kim, B.-A., Comte, B., & Voirin, N. (2013) · PLoS ONE · doi:10.1371/journal.pone.0073970 · Cited by 407 · see citing works
Efficacy of contact tracing for the containment of the 2019 novel coronavirus (COVID-19)
Keeling, M. J., Hollingsworth, T. D., & Read, J. M. (2020) · Journal of Epidemiology & Community Health · doi:10.1136/jech-2020-214051 · Cited by 396 · see citing works
Tuite, A. R., Fisman, D. N., & Greer, A. L. (2020) · Canadian Medical Association Journal · doi:10.1503/cmaj.200476 · Cited by 407 · see citing works
Behavioural change models for infectious disease transmission: a systematic review (2010–2015)
Verelst, F., Willem, L., & Beutels, P. (2016) · Journal of The Royal Society Interface · doi:10.1098/rsif.2016.0820 · Cited by 386 · see citing works
Self-citationStehlé, J., Voirin, N., Barrat, A., Cattuto, C., Vittoria Colizza, A., Isella, L., Régis, C., Pinton, J.-F., Khanafer, N. L., den Broeck, W. V., et al. (2011) · BMC Medicine · doi:10.1186/1741-7015-9-87 · Cited by 371 · see citing works
The emergence of SARS-CoV-2 in Europe and North America
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) · Science · doi:10.1126/science.abc8169 · Cited by 359 · see citing works
FluTE, a Publicly Available Stochastic Influenza Epidemic Simulation Model
Chao, D. L., Halloran, M. E., Obenchain, V., & Longini, I. M. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000656 · Cited by 346 · see citing works
Age groups that sustain resurging COVID-19 epidemics in the United States
Monod, M., Blenkinsop, A., Xi, X., Hebert, D. J., Bershan, S., Tietze, S., Baguelin, M., Bradley, V. C., Chen, Y., Coupland, H., et al. (2021) · Science · doi:10.1126/science.abe8372 · Cited by 326 · see citing works
Individual variation in susceptibility or exposure to SARS-CoV-2 lowers the herd immunity threshold
Gomes, M. G. M., Ferreira, M. U., Corder, R. M., King, J. G., Souto-Maior, C., Penha‐Gonçalves, C., Gonçalves, G., Chikina, M. D., Pegden, W., & Aguas, R. J. (2022) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2022.111063 · Cited by 328 · see citing works
Braithwaite, I., Callender, T., Bullock, M., & Aldridge, R. W. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30184-9 · Cited by 318 · see citing works
Emergence and spread of a SARS-CoV-2 variant through Europe in the summer of 2020Preprint
Hodcroft, E. B., Zuber, M., Nadeau, S., Vaughan, T. G., Crawford, K. H. D., Althaus, C. L., Reichmuth, M. L., Bowen, J. E., Walls, A. C., Corti, D., et al. (2020) · medRxiv · doi:10.1101/2020.10.25.20219063 · Cited by 326 · see citing works
Pepe, E., Bajardi, P., Gauvin, L., Privitera, F., Lake, B., Cattuto, C., & Tizzoni, M. (2020) · Scientific Data · doi:10.1038/s41597-020-00575-2 · Cited by 318 · see citing works
Teslya, A., Pham, T. M., Godijk, N. G., Kretzschmar, M., Bootsma, M., & Rozhnova, G. (2020) · PLoS Medicine · doi:10.1371/journal.pmed.1003166 · Cited by 309 · see citing works
Contact Patterns among High School Students
Fournet, J., & Barrat, A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0107878 · Cited by 306 · see citing works
Baguelin, M., Flasche, S., Camacho, A., Demiris, N., Miller, E., & Edmunds, W. J. (2013) · PLoS Medicine · doi:10.1371/journal.pmed.1001527 · Cited by 294 · see citing works
Self-citationInferring high-resolution human mixing patterns for disease modeling
Mistry, D., Litvinova, M., y Piontti, A. P., Chinazzi, M., Fumanelli, L., Gomes, M. F. D. C., Haque, S. A., Liu, Q.-H., Mu, K., Xiong, X., et al. (2021) · Nature Communications · doi:10.1038/s41467-020-20544-y · Cited by 281 · see citing works
Edge-based compartmental modelling for infectious disease spread
Miller, J. C., Slim, A. C., & Volz, E. (2011) · Journal of The Royal Society Interface · doi:10.1098/rsif.2011.0403 · Cited by 280 · see citing works
Can co-location be used as a proxy for face-to-face contacts?
Génois, M., & Barrat, A. (2018) · EPJ Data Science · doi:10.1140/epjds/s13688-018-0140-1 · Cited by 275 · see citing works
Prem, K., van Zandvoort, K., Klepac, P., Eggo, R. M., Davies, N. G. M., Cook, A. R., & Jit, M. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009098 · Cited by 253 · see citing works
Self-citationThe scaling of contact rates with population density for the infectious disease models
Hu, H., Nigmatulina, K. R., & Eckhoff, P. A. (2013) · Mathematical Biosciences · doi:10.1016/j.mbs.2013.04.013 · Cited by 252 · see citing works
Isella, L., Romano, M., Barrat, A., Cattuto, C., Vittoria Colizza, A., den Broeck, W. V., Gesualdo, F., Pandolfi, E., Ravà, L., Rizzo, C., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0017144 · Cited by 259 · see citing works
Hinch, R., Probert, W. J. M., Nurtay, A., Kendall, M., Wymant, C., Hall, M. D., Lythgoe, K., Cruz, A. B., Zhao, L., Stewart, A., et al. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009146 · Cited by 245 · see citing works
The molecular epidemiology of multiple zoonotic origins of SARS-CoV-2
Pekar, J. E., Magee, A. F., Parker, E., Moshiri, N., Izhikevich, K., Havens, J. L., Gangavarapu, K., Serrano, L. M. M., Crits‐Christoph, A., Matteson, N. L., et al. (2022) · Science · doi:10.1126/science.abp8337 · Cited by 248 · see citing works
Children are unlikely to be the main drivers of the COVID‐19 pandemic – A systematic review
Ludvigsson, J. F. (2020) · Acta Paediatrica · doi:10.1111/apa.15371 · Cited by 244 · see citing works
Evidence for Antigenic Seniority in Influenza A (H3N2) Antibody Responses in Southern China
Lessler, J., Riley, S., Read, J. M., Wang, S., Zhu, H., Smith, G. J. D., Guan, Y., Jiang, C. Q., & Cummings, D. A. T. (2012) · PLoS Pathogens · doi:10.1371/journal.ppat.1002802 · Cited by 243 · see citing works
Hoang, T. V., Coletti, P., Melegaro, A., Wallinga, J., Grijalva, C. G., Edmunds, J. W., Beutels, P., & Hens, N. (2019) · Epidemiology · doi:10.1097/ede.0000000000001047 · Cited by 243 · see citing works
Self-citationNine challenges in incorporating the dynamics of behaviour in infectious diseases models
Funk, S., Bansal, S., Bauch, C. T., Eames, K. T. D., Edmunds, W. J., Galvani, A. P., & Klepac, P. (2014) · Epidemics · doi:10.1016/j.epidem.2014.09.005 · Cited by 236 · see citing works
Self-citationMitigation of infectious disease at school: targeted class closure vs school closure
Gemmetto, V., Barrat, A., & Cattuto, C. (2014) · BMC Infectious Diseases · doi:10.1186/s12879-014-0695-9 · Cited by 244 · see citing works
Measurability of the epidemic reproduction number in data-driven contact networks
Liu, Q.-H., Ajelli, M., Aleta, A., Merler, S., Moreno, Y., & Vespignani, A. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1811115115 · Cited by 242 · see citing works
Pertussis Disease Burden in the Household: How to Protect Young Infants
de Greeff, S. C., Mooi, F. R., Westerhof, A., Verbakel, J., Peeters, M. F., Heuvelman, C. J., Notermans, D. W., Elvers, L. H., Schellekens, J. F. P., & de Melker, H. E. (2010) · Clinical Infectious Diseases · doi:10.1086/652281 · Cited by 238 · see citing works
How Many People Do You Know?: Efficiently Estimating Personal Network Size
McCormick, T. H., Salganik, M., & Zheng, T. (2010) · Journal of the American Statistical Association · doi:10.1198/jasa.2009.ap08518 · Cited by 228 · see citing works
Contact Network Structure Explains the Changing Epidemiology of Pertussis
Rohani, P., Zhong, X. Y., & King, A. A. (2010) · Science · doi:10.1126/science.1194134 · Cited by 234 · see citing works
School Closure and Mitigation of Pandemic (H1N1) 2009, Hong Kong
Wu, J. T. K., Cowling, B. J., Lau, E. H. Y., Ip, D., Ho, L., Tsang, T., Chuang, S. K., Leung, P.-Y., Lo, S., Liu, S., et al. (2010) · Emerging infectious diseases · doi:10.3201/eid1603.091216 · Cited by 233 · see citing works
Gupta, S., Nguyen, T. D., Lozano-Rojas, F. A., Raman, S., Lee, B., Bento, A. I., Simon, K. I., & Wing, C. (2020) · National Bureau of Economic Research · doi:10.3386/w27027 · Cited by 229 · see citing works
The dynamic nature of contact networks in infectious disease epidemiology
Bansal, S., Read, J. M., Pourbohloul, B., & Ancel, L. W. (2010) · Journal of Biological Dynamics · doi:10.1080/17513758.2010.503376 · Cited by 225 · see citing works
Génois, M., Vestergaard, C. L., Fournet, J., Panisson, A., BONMARIN, I., & Barrat, A. (2015) · Network Science · doi:10.1017/nws.2015.10 · Cited by 225 · see citing works
Pitman, R., Fisman, D. N., Zaric, G. S., Postma, M. J., Kretzschmar, M., Edmunds, J., & Brisson, M. (2012) · Value in Health · doi:10.1016/j.jval.2012.06.011 · Cited by 223 · see citing works
Artificial Intelligence for infectious disease Big Data Analytics
Wong, Z. S.-Y., Zhou, J., & Zhang, Q. (2018) · Infection Disease & Health · doi:10.1016/j.idh.2018.10.002 · Cited by 223 · see citing works
Dynamic prioritization of COVID-19 vaccines when social distancing is limited for essential workers
Buckner, J. H., Chowell, G., & Springborn, M. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2025786118 · Cited by 216 · see citing works
Hens, N., Ayele, G. M., Goeyvaerts, N., Aerts, M., Mossong, J., Edmunds, J. W., & Beutels, P. (2009) · BMC Infectious Diseases · doi:10.1186/1471-2334-9-187 · Cited by 215 · see citing works
Self-citationEffects of Heterogeneous and Clustered Contact Patterns on Infectious Disease Dynamics
Volz, E., Miller, J. C., Galvani, A. P., & Ancel, L. W. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002042 · Cited by 212 · see citing works
Social Networks and Health: New Developments in Diffusion, Online and Offline
Zhang, J., & Centola, D. M. (2019) · Annual Review of Sociology · doi:10.1146/annurev-soc-073117-041421 · Cited by 208 · see citing works
Ke, R., Martinez, P. P., Smith, R. L., Gibson, L. L., Mirza, A., Conte, M., Gallagher, N., Luo, C. H., Jarrett, J., Zhou, R., et al. (2022) · Nature Microbiology · doi:10.1038/s41564-022-01105-z · Cited by 212 · see citing works
Fumanelli, L., Ajelli, M., Manfredi, P., Vespignani, A., & Merler, S. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002673 · Cited by 208 · see citing works
Duration of Immunity to Norovirus Gastroenteritis
Simmons, K., Gambhir, M., León, J. S., & Lopman, B. A. (2013) · Emerging infectious diseases · doi:10.3201/eid1908.130472 · Cited by 207 · see citing works
Willem, L., Verelst, F., Bilcke, J., Hens, N., & Beutels, P. (2017) · BMC Infectious Diseases · doi:10.1186/s12879-017-2699-8 · Cited by 204 · see citing works
Self-citationBéraud, G., Kazmercziak, S., Beutels, P., Lévy-Bruhl, D., Lenne, X., Mielcarek, N., Yazdanpanah, Y., Boëlle, P., Hens, N., & Dervaux, B. (2015) · PLoS ONE · doi:10.1371/journal.pone.0133203 · Cited by 200 · see citing works
Self-citationVynnycky, E., Adams, E. J., Cutts, F. T., Reef, S. E., Návar, A. M., Simons, E. A., Yoshida, L., Brown, D. W., Jackson, C., Strebel, P. M., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0149160 · Cited by 200 · see citing works
Measured Dynamic Social Contact Patterns Explain the Spread of H1N1v Influenza
Eames, K. T. D., Tilston‐Lunel, N. L., Brooks‐Pollock, E., & Edmunds, W. J. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002425 · Cited by 197 · see citing works
Self-citationLopes, P. C., Block, P., & König, B. (2016) · Scientific Reports · doi:10.1038/srep31790 · Cited by 196 · see citing works
Modelling optimal vaccination strategy for SARS-CoV-2 in the UK
Moore, S., Hill, E. M., Dyson, L., Tildesley, M. J., & Keeling, M. J. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008849 · Cited by 193 · see citing works
Insights from unifying modern approximations to infections on networks
House, T., & Keeling, M. J. (2010) · Journal of The Royal Society Interface · doi:10.1098/rsif.2010.0179 · Cited by 195 · see citing works
Using contact networks to explore mechanisms of parasite transmission in wildlife
White, L. A., Forester, J. D., & Craft, M. E. (2015) · Biological reviews/Biological reviews of the Cambridge Philosophical Society · doi:10.1111/brv.12236 · Cited by 191 · see citing works
Mathematical Models for COVID-19 Pandemic: A Comparative Analysis
Adiga, A., Dubhashi, D., Lewis, B. L., MARATHE, M. V., Venkatramanan, S., & Vullikanti, A. K. S. (2020) · Journal of the Indian Institute of Science · doi:10.1007/s41745-020-00200-6 · Cited by 203 · see citing works
- 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
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › 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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
