Hostname: page-component-77c89778f8-vsgnj Total loading time: 0 Render date: 2024-07-18T08:17:37.400Z Has data issue: false hasContentIssue false

Data on face-to-face contacts in an office building suggest a low-cost vaccination strategy based on community linkers

Published online by Cambridge University Press:  16 March 2015

Aix Marseille Université, Université de Toulon, CNRS, CPT, UMR 7332, 13288 Marseille, France (e-mail:
Aix Marseille Université, Université de Toulon, CNRS, CPT, UMR 7332, 13288 Marseille, France (e-mail:
Aix Marseille Université, Université de Toulon, CNRS, CPT, UMR 7332, 13288 Marseille, France (e-mail:
Data Science Laboratory, ISI Foundation, Torino, Italy
Département des maladies infectieuses, Institut de veille sanitaire, Saint-Maurice, France
Aix Marseille Université, Université de Toulon, CNRS, CPT, UMR 7332, 13288 Marseille, France and Data Science Laboratory, ISI Foundation, Torino, Italy


Empirical data on contacts between individuals in social contexts play an important role in providing information for models describing human behavior and how epidemics spread in populations. Here, we analyze data on face-to-face contacts collected in an office building. The statistical properties of contacts are similar to other social situations, but important differences are observed in the contact network structure. In particular, the contact network is strongly shaped by the organization of the offices in departments, which has consequences in the design of accurate agent-based models of epidemic spread. We consider the contact network as a potential substrate for infectious disease spread and show that its sparsity tends to prevent outbreaks of rapidly spreading epidemics. Moreover, we define three typical behaviors according to the fraction f of links each individual shares outside its own department: residents, wanderers, and linkers. Linkers (f ~ 50%) act as bridges in the network and have large betweenness centralities. Thus, a vaccination strategy targeting linkers efficiently prevents large outbreaks. As such a behavior may be spotted a priori in the offices' organization or from surveys, without the full knowledge of the time-resolved contact network, this result may help the design of efficient, low-cost vaccination or social-distancing strategies.

Research Article
Copyright © Cambridge University Press 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)


Ajelli, M., Gonçalves, B., Balcan, D., Colizza, V., Hu, H., Ramasco, J., . . . Vespignani, A. (2010). Comparing large-scale computational approaches to epidemic modeling: Agent-based versus structured metapopulation models. BMC Infectious Diseases, 10 (1), 190.CrossRefGoogle ScholarPubMed
Ajelli, M., Poletti, P., Melegaro, A., & Merler, S. (2014). The role of different social contexts in shaping influenza transmission during the 2009 pandemic. Scientific Reports, 4, 7218.Google Scholar
Barabàsi, A.-L. (2005). The origin of bursts and heavy tails in human dynamics. Nature, 435, 207.CrossRefGoogle ScholarPubMed
Barrat, A., Cattuto, C., Colizza, V., Gesualdo, F., Isella, L., Pandolfi, E., . . . Broeck, W. (2013). Empirical temporal networks of face-to-face human interactions. The European Physical Journal Special Topics, 222 (6), 12951309.CrossRefGoogle Scholar
Barrat, A., Cattuto, C., Tozzi, A. E., Vanhems, P., & Voirin, N. (2014). Measuring contact patterns with wearable sensors: Methods, data characteristics and applications to data-driven simulations of infectious diseases. Clinical Microbiology and Infections, 20 (1), 1016.Google Scholar
Blower, S., & Go, M.-H. (2011). The importance of including dynamic social networks when modeling epidemics of airborne infections: Does increasing complexity increase accuracy? BMC Medicine, 9 (1), 88.CrossRefGoogle ScholarPubMed
Brown, C., Efstratiou, C., Leontiadis, I., Quercia, D., & Mascolo, C. (2014b). Tracking serendipitous interactions: How individual cultures shape the office. In Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing. CSCW '14. New York, NY, USA: ACM, pp. 10721081.Google Scholar
Brown, C., Efstratiou, C., Leontiadis, I., Quercia, D., Mascolo, C., Scott, J., & Key, P. (2014a). The architecture of innovation: Tracking face-to-face interactions with ubicomp technologies. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. UbiComp'14. New York, NY, USA: ACM, pp. 811822.Google Scholar
Castellano, C. & Pastor-Satorras, R. (2012). Competing activation mechanisms in epidemics on networks. Scientific Reports, 2, 371.Google Scholar
Cattuto, C., Van den Broeck, W., Barrat, A., Colizza, V., Pinton, J.-F., & Vespignani, A. (2010). Dynamics of person-to-person interactions from distributed RFID sensor networks. PLoS ONE, 5 (7), e11596.CrossRefGoogle ScholarPubMed
Chowell, G., & Viboud, C. (2013). A practical method to target individuals for outbreak detection and control. BMC Medicine, 11 (1), 36.CrossRefGoogle ScholarPubMed
Christley, R. M., Pinchbeck, G. L., Bowers, R. G., Clancy, D., French, N. P., Bennett, R., & Turner, J. (2005). Infection in social networks: Using network analysis to identify high-risk individuals. American Journal of Epidemiology, 162 (10), 10241031.Google Scholar
Dall'Asta, L., Barrat, A., Barthélemy, M., & Vespignani, A. (2006). Vulnerability of weighted networks. Journal of Statistical Mechanics: Theory and Experiment, 2006 (04), P04006.Google Scholar
Davey, V. J., Glass, R.t J., Min, H. J., Beyeler, W. E., & Glass, L. M. (2008). Effective, robust design of community mitigation for pandemic influenza: A systematic examination of proposed US guidance. PLoS ONE, 3 (7).Google Scholar
Eagle, N., Pentland, A. (Sandy), & Lazer, D. (2009). Inferring friendship network structure by using mobile phone data. Proceedings of the National Academy of Sciences, 106 (36), 1527415278.CrossRefGoogle ScholarPubMed
Fournet, J., & Barrat, A. (2014). Contact patterns among high school students. PLoS ONE, 9 (9), e107878.Google Scholar
Gauvin, L., Panisson, A., & Cattuto, C. (2014). Detecting the community structure and activity patterns of temporal networks: A non-negative tensor factorization approach. Plos One, 9 (1), e86028.Google Scholar
Gauvin, L., Panisson, A., Cattuto, C., & Barrat, A. (2013). Activity clocks: Spreading dynamics on temporal networks of human contact. Scientific Reports, 3, 3099.Google Scholar
Gemmetto, V., Barrat, A., & Cattuto, C. (2014). Mitigation of infectious disease at school: Targeted class closure vs school closure. BMC Infectious Diseases, 14 (1), 695.Google Scholar
Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78, 13601380.Google Scholar
Hébert-Dufresne, L., Allard, A., Young, J.-G., & Dubé, L. J. (2013). Global efficiency of local immunization on complex networks. Scientific Reports, 3, 2171.Google Scholar
Holme, P., Kim, B. J., Yoon, C. N., & Han, S. K. (2002). Attack vulnerability of complex networks. Physical Review E, 65 (May), 056109.Google Scholar
Holme, P. & Saramäki, J. (2012). Temporal networks. Physics Reports, 519 (3), 97125.CrossRefGoogle Scholar
Isella, L., Romano, M., Barrat, A., Cattuto, C., Colizza, V., Van den Broeck, W., . . . Tozzi, A. E. (2011a). Close encounters in a pediatric ward: Measuring face-to-face proximity and mixing patterns with wearable sensors. PLoS ONE, 6 (2), e17144.Google Scholar
Isella, L., Stehlé, J., Barrat, A., Cattuto, C., Pinton, J.-F., & Van den Broeck, W. (2011b). What's in a crowd? Analysis of face-to-face behavioral networks. Journal of Theoretical Biology, 271 (1), 166180.Google Scholar
Karsai, M., Kivela, M., Pan, R. K., Kaski, K., Kertesz, J., Barabási, A. L., & Saramäki, J. (2011). Small but slow world: How network topology and burstiness slow down spreading. Physical Review E, 83 (2), 025102.CrossRefGoogle ScholarPubMed
Kitsak, M., Gallos, L. K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H. E., & Makse, H. A. (2010). Identification of influential spreaders in complex networks. Nature Physics, 6 (11), 888893.Google Scholar
Lee, S., Rocha, L. E. C., Liljeros, F., & Holme, P. (2012). Exploiting temporal network structures of human interaction to effectively immunize populations. PLoS ONE, 7 (5), e36439.Google Scholar
Machens, A., Gesualdo, F., Rizzo, C., Tozzi, A., Barrat, A., & Cattuto, C. (2013). An infectious disease model on empirical networks of human contact: Bridging the gap between dynamic network data and contact matrices. BMC Infectious Diseases, 13 (1), 185.Google Scholar
Maslov, S., & Sneppen, K. (2002). Specificity and stability in topology of protein networks. Science, 296 (5569), 910913.Google Scholar
Merler, S., & Ajelli, M. (2010). The role of population heterogeneity and human mobility in the spread of pandemic influenza. Proceedings of the Royal Society B-Biological Sciences, 277 (1681), 557565.Google Scholar
Mossong, J., Hens, N., Jit, M., Beutels, P., Auranen, K., Mikolajczyk, R., . . . Edmunds, W. J. (2008). Social contacts and mixing patterns relevant to the spread of infectious diseases. PLoS Medicine, 5 (3), e74.CrossRefGoogle Scholar
Onnela, J.-P., Saramäki, J., Hyvönen, J., Szabó, G., de Menezes, M. A., Kaski, K., . . . Kertész, J. (2007). Analysis of a large-scale weighted network of one-to-one human communication. New Journal of Physics, 9 (6), 179.Google Scholar
Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32 (3), 245251.Google Scholar
Pastor-Satorras, R., & Vespignani, A. (2002). Immunization of complex networks. Physical Review E, 65 (Feb), 036104.Google Scholar
Penn, A., Desyllas, J., & Vaughan, L. (1999). The space of innovation: Interaction and communication in the work environment. Environment and Planning B: Planning and Design, 26 (2), 193218.CrossRefGoogle Scholar
Pfitzner, R., Scholtes, I., Garas, A., Tessone, C. J., & Schweitzer, F. (2013). Betweenness preference: Quantifying correlations in the topological dynamics of temporal networks. Physical Review Letters, 110 (May), 198701.Google Scholar
Read, J. M., Edmunds, W. J., Riley, S., Lessler, J., & Cummings, D. A. T. (2012). Close encounters of the infectious kind: Methods to measure social mixing behaviour. Epidemiology and Infection, 140 (12), 21172130.Google Scholar
Sailer, K. & McCulloh, I. (2012). Social networks and spatial configuration–-how office layouts drive social interaction. Social Networks, 34 (1), 4758.Google Scholar
Salathé, M., & Jones, J. H. (2010). Dynamics and control of diseases in networks with community structure. PLoS Computational Biology, 6 (4), e1000736.Google Scholar
Salathé, M., Kazandjieva, M., Lee, J. Woo, L., Philip, F., Marcus, W., & Jones, J. H. (2010). A high-resolution human contact network for infectious disease transmission. Proceedings of the National Academy of Sciences, 107 (51), 2202022025.Google Scholar
Scholtes, I., Wider, N., Pfitzner, R., Garas, A., Tessone, C. J., & Schweitzer, F. (2014). Causality-driven slow-down and speed-up of diffusion in non-Markovian temporal networks. Nature Communications, 5, 5024.CrossRefGoogle ScholarPubMed
Smieszek, T., Fiebig, L., & Scholz, R. (2009). Models of epidemics: When contact repetition and clustering should be included. Theoretical Biology and Medical Modelling, 6 (1), 11.Google Scholar
Smieszek, T., & Salathé, M. (2013). A low-cost method to assess the epidemiological importance of individuals in controlling infectious disease outbreaks. BMC Medicine, 11 (1), 35.Google Scholar
Starnini, M., Machens, A., Cattuto, C., Barrat, A., & Pastor-Satorras, R. (2013). Immunization strategies for epidemic processes in time-varying contact networks. Journal of Theoretical Biology, 337 (0), 89100.Google Scholar
Stehlé, J., Voirin, N., Barrat, A., Cattuto, C., Isella, L., Pinton, J.-F., . . . Vanhems, P. (2011a). High-resolution measurements of face-to-face contact patterns in a primary school. PLoS ONE, 6 (8), e23176.CrossRefGoogle Scholar
Stehlé, J., Voirin, N., Barrat, A., Cattuto, C., Colizza, V., Isella, L., . . . & Vanhems, P. (2011b). Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees. BMC Medicine, 9 (1), 87.CrossRefGoogle ScholarPubMed
Stopczynski, A., Sekara, V., Sapiezynski, P., Cuttone, A., Madsen, M. M., Larsen, J. E., & Lehmann, S. (2014). Measuring large-scale social networks with high resolution. PLoS ONE, 9 (4), e95978.CrossRefGoogle ScholarPubMed
Vanhems, P., Barrat, A., Cattuto, C., Pinton, J.-F., Khanafer, N., Régis, C., . . . Voirin, N. (2013). Estimating potential infection transmission routes in hospital wards using wearable proximity sensors. PLoS ONE, 8 (9), e73970.CrossRefGoogle ScholarPubMed
Vu, L., Nahrstedt, K., Retika, S., & Gupta, I. (2010). Joint Bluetooth/Wifi scanning framework for characterizing and leveraging people movement in University campus. In MSWIM 2010: Proceedings of the 13th ACM International Conference on Modeling, Analysis, and Simulation of Wireless and Mobile Systems. 1515 Broadway, New York, NY 10036-9998 USA: ASSOC Computing Machinery, for ACM SIGSIM, pp. 257265.Google Scholar
Wasserman, S., & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Zhang, Y., Wang, L., Zhang, Y.-Q., & Li, X. (2012). Towards a temporal network analysis of interactive WiFi users. Europhysics Letters, 98 (6), 68002.Google Scholar
Supplementary material: PDF

Génois supplementary material S1

Génois supplementary material S1

Download Génois supplementary material S1(PDF)
PDF 2.8 MB