Skip to main content
    • Aa
    • Aa
  • Get access
    Check if you have access via personal or institutional login
  • Cited by 37
  • Cited by
    This article has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Edwards, Christina Hansen Tomba, Gianpaolo Scalia and de Blasio, Birgitte Freiesleben 2016. Influenza in workplaces: transmission, workers’ adherence to sick leave advice and European sick leave recommendations. The European Journal of Public Health, Vol. 26, Issue. 3, p. 478.

    Gao, Xiaolei Wei, Jianjian Cowling, Benjamin J. and Li, Yuguo 2016. Potential impact of a ventilation intervention for influenza in the context of a dense indoor contact network in Hong Kong. Science of The Total Environment, Vol. 569-570, p. 373.

    Kiti, Moses C Tizzoni, Michele Kinyanjui, Timothy M Koech, Dorothy C Munywoki, Patrick K Meriac, Milosch Cappa, Luca Panisson, André Barrat, Alain Cattuto, Ciro and Nokes, D James 2016. Quantifying social contacts in a household setting of rural Kenya using wearable proximity sensors. EPJ Data Science, Vol. 5, Issue. 1,

    McCreesh, Nicky Looker, Clare Dodd, Peter J. Plumb, Ian D. Shanaube, Kwame Muyoyeta, Monde Godfrey-Faussett, Peter Corbett, Elizabeth L. Ayles, Helen and White, Richard G. 2016. Comparison of indoor contact time data in Zambia and Western Cape, South Africa suggests targeting of interventions to reduce Mycobacterium tuberculosis transmission should be informed by local data. BMC Infectious Diseases, Vol. 16, Issue. 1,

    Smieszek, Timo Castell, Stefanie Barrat, Alain Cattuto, Ciro White, Peter J. and Krause, Gérard 2016. Contact diaries versus wearable proximity sensors in measuring contact patterns at a conference: method comparison and participants’ attitudes. BMC Infectious Diseases, Vol. 16, Issue. 1,

    Dodd, Peter J. Looker, Clare Plumb, Ian D. Bond, Virginia Schaap, Ab Shanaube, Kwame Muyoyeta, Monde Vynnycky, Emilia Godfrey-Faussett, Peter Corbett, Elizabeth L. Beyers, Nulda Ayles, Helen and White, Richard G. 2015. Age- and Sex-Specific Social Contact Patterns and Incidence ofMycobacterium tuberculosisInfection. American Journal of Epidemiology, p. kwv160.

    Duan, Wei Fan, Zongchen Zhang, Peng Guo, Gang and Qiu, Xiaogang 2015. Mathematical and computational approaches to epidemic modeling: a comprehensive review. Frontiers of Computer Science, Vol. 9, Issue. 5, p. 806.

    Stein, Mart L. van der Heijden, Peter G. M. Buskens, Vincent van Steenbergen, Jim E. Bengtsson, Linus Koppeschaar, Carl E. Thorson, Anna and Kretzschmar, Mirjam E. E. 2015. Tracking social contact networks with online respondent-driven detection: who recruits whom?. BMC Infectious Diseases, Vol. 15, Issue. 1,

    Barrat, A. Cattuto, C. Tozzi, A.E. Vanhems, P. and Voirin, N. 2014. Measuring contact patterns with wearable sensors: methods, data characteristics and applications to data-driven simulations of infectious diseases. Clinical Microbiology and Infection, Vol. 20, Issue. 1, p. 10.

    Mao, Liang 2014. Modeling triple-diffusions of infectious diseases, information, and preventive behaviors through a metropolitan social network—An agent-based simulation. Applied Geography, Vol. 50, p. 31.

    Smieszek, Timo Barclay, Victoria C Seeni, Indulaxmi Rainey, Jeanette J Gao, Hongjiang Uzicanin, Amra and Salathé, Marcel 2014. How should social mixing be measured: comparing web-based survey and sensor-based methods. BMC Infectious Diseases, Vol. 14, Issue. 1,

    Ceddia, M.G. Bardsley, N.O. Goodwin, R. Holloway, G.J. Nocella, G. and Stasi, A. 2013. A complex system perspective on the emergence and spread of infectious diseases: Integrating economic and ecological aspects. Ecological Economics, Vol. 90, p. 124.

    Danon, L. Read, J. M. House, T. A. Vernon, M. C. and Keeling, M. J. 2013. Social encounter networks: characterizing Great Britain. Proceedings of the Royal Society B: Biological Sciences, Vol. 280, Issue. 1765, p. 20131037.

    Machens, Anna Gesualdo, Francesco Rizzo, Caterina Tozzi, Alberto E Barrat, Alain and Cattuto, Ciro 2013. An infectious disease model on empirical networks of human contact: bridging the gap between dynamic network data and contact matrices. BMC Infectious Diseases, Vol. 13, Issue. 1,

    Rattana, Prapanporn Blyuss, Konstantin B. Eames, Ken T. D. and Kiss, Istvan Z. 2013. A Class of Pairwise Models for Epidemic Dynamics on Weighted Networks. Bulletin of Mathematical Biology, Vol. 75, Issue. 3, p. 466.

    SMIESZEK, T. BURRI, E. U. SCHERZINGER, R. and SCHOLZ, R. W. 2012. Collecting close-contact social mixing data with contact diaries: reporting errors and biases. Epidemiology and Infection, Vol. 140, Issue. 04, p. 744.

    Blower, Sally and Go, Myong-Hyun 2011. The importance of including dynamic social networks when modeling epidemics of airborne infections: does increasing complexity increase accuracy?. BMC Medicine, Vol. 9, Issue. 1,

    Johnstone-Robertson, S. P. Mark, D. Morrow, C. Middelkoop, K. Chiswell, M. Aquino, L. D. H. Bekker, L.-G. and Wood, R. 2011. Social Mixing Patterns Within a South African Township Community: Implications for Respiratory Disease Transmission and Control. American Journal of Epidemiology, Vol. 174, Issue. 11, p. 1246.

    Kimura, Yoshinari Saito, Reiko Tsujimoto, Yoshiki Ono, Yasuhiko Nakaya, Tomoki Shobugawa, Yugo Sasaki, Asami Oguma, Taeko and Suzuki, Hiroshi 2011. Geodemographics profiling of influenza A and B virus infections in community neighborhoods in Japan. BMC Infectious Diseases, Vol. 11, Issue. 1,

    Melegaro, Alessia Jit, Mark Gay, Nigel Zagheni, Emilio and Edmunds, W. John 2011. What types of contacts are important for the spread of infections? Using contact survey data to explore European mixing patterns. Epidemics, Vol. 3, Issue. 3-4, p. 143.


Social mixing patterns for transmission models of close contact infections: exploring self-evaluation and diary-based data collection through a web-based interface

  • P. BEUTELS (a1) (a2), Z. SHKEDY (a3), M. AERTS (a3) and P. VAN DAMME (a1)
  • DOI:
  • Published online: 01 May 2006

Although mixing patterns are crucial in dynamic transmission models of close contact infections, they are largely estimated by intuition. Using a convenience sample (n=73), we tested self-evaluation and prospective diary surveys with a web-based interface, in order to obtain social contact data. The number of recorded contacts was significantly (P<0·01) greater on workdays (18·1) vs. weekend days (12·3) for conversations, and vice versa for touching (5·4 and 7·2 respectively). Mixing was highly assortative with age for both (adults contacting other adults vs. 0- to 5-year-olds, odds ratio 8·9–10·8). Respondents shared a closed environment significantly more often with >20 other adults than with >20 children. The difference in number of contacts per day was non-significant between self-evaluation and diary (P=0·619 for conversations, P=0·125 for touching). We conclude that self-evaluation could yield similar results to diary surveys for general or very recent mixing information. More detailed data could be collected by diary, at little effort to respondents.

Corresponding author
Center for the Evaluation of Vaccination, Epidemiology and Social Medicine, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium. (Email:
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Epidemiology & Infection
  • ISSN: 0950-2688
  • EISSN: 1469-4409
  • URL: /core/journals/epidemiology-and-infection
Please enter your name
Please enter a valid email address
Who would you like to send this to? *