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

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  • GÉNOIS, MATHIEU
  • VESTERGAARD, CHRISTIAN L.
  • FOURNET, JULIE
  • PANISSON, ANDRÉ
  • BONMARIN, ISABELLE
  • BARRAT, ALAIN

Abstract

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.

Suggested Citation

  • Génois, Mathieu & Vestergaard, Christian L. & Fournet, Julie & Panisson, André & Bonmarin, Isabelle & Barrat, Alain, 2015. "Data on face-to-face contacts in an office building suggest a low-cost vaccination strategy based on community linkers," Network Science, Cambridge University Press, vol. 3(3), pages 326-347, September.
  • Handle: RePEc:cup:netsci:v:3:y:2015:i:03:p:326-347_00
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    Cited by:

    1. Li, Mingwu & Dankowicz, Harry, 2019. "Impact of temporal network structures on the speed of consensus formation in opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1355-1370.
    2. Peng, Hao & Qian, Cheng & Zhao, Dandan & Zhong, Ming & Han, Jianmin & Zhou, Tao & Wang, Wei, 2024. "Message-passing approach to higher-order percolation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 634(C).
    3. Yin, Ran-Ran & Guo, Qiang & Yang, Jian-Nan & Liu, Jian-Guo, 2018. "Inter-layer similarity-based eigenvector centrality measures for temporal networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 165-173.
    4. Gail E. Potter & Nicole Bohme Carnegie & Jonathan D. Sugimoto & Aldiouma Diallo & John C. Victor & Kathleen M. Neuzil & M. Elizabeth Halloran, 2022. "Using social contact data to improve the overall effect estimate of a cluster‐randomized influenza vaccination program in Senegal," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(1), pages 70-90, January.
    5. Tao, Li & Kong, Shengzhou & He, Langzhou & Zhang, Fan & Li, Xianghua & Jia, Tao & Han, Zhen, 2022. "A sequential-path tree-based centrality for identifying influential spreaders in temporal networks," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).

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