mlsanityData to 7 Oct 2026

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

arXiv 1409.7017 ↗First posted 24 Sep 2014physics.soc-ph cs.SIcited by 17

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Abstract (arXiv)

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.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Sep 2014.

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Citing papers per year: 2021: 2; 2022: 3; 2023: 3; 2024: 3; 2025: 6.

17 papers.

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  1. 2022Inference of hyperedges and overlapping communities in hypergraphscited by 13
  2. 2022De Bruijn goes Neural: Causality-Aware Graph Neural Networks for Time Series Data on Dynamic Graphscited by 10
  3. 2023Structure and inference in hypergraphs with node attributescited by 7
  4. 2023Continuous-time Graph Representation with Sequential Survival Processcited by 4
  5. 2024Gaussian Embedding of Temporal Networkscited by 3
  6. 2025Broad Spectrum Structure Discovery in Large-Scale Higher-Order Networkscited by 3
  7. 2021Predicting Influential Higher-Order Patterns in Temporal Network Datacited by 1
  8. 2022Systematic assessment of the quality of fit of the stochastic block model for empirical networkscited by 1
  9. 2024Inference and Visualization of Community Structure in Attributed Hypergraphs Using Mixed-Membership Stochastic Block Modelscited by 1
  10. 2024Inference of Sequential Patterns for Neural Message Passing in Temporal Graphscited by 1
  11. 2021Overcoming Bias in Community Detection Evaluationcited by 0
  12. 2023Bayesian Detection of Mesoscale Structures in Pathway Data on Graphscited by 0
5 more
  1. 2025Unifying Structural Proximity and Equivalence for Enhanced Dynamic Network Embeddingcited by 0
  2. 2025Community and hyperedge inference in multiple hypergraphscited by 0
  3. 2025Improving Graph Embeddings in Machine Learning Using Knowledge Completion with Validation in a Case Study on COVID-19 Spreadcited by 0
  4. 2025Learning Multi-Order Block Structure in Higher-Order Networkscited by 0
  5. 2025Graph Neural Networks for Source Detection: A Review and Benchmark Studycited by 0