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

arXiv: https://arxiv.org/abs/1409.7017 · HTML: https://mlsanity.com/paper/1409.7017/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2014.

## Built on by (most cited first, 17 of 17)

- [Inference of hyperedges and overlapping communities in hypergraphs](https://mlsanity.com/paper/2204.05646/) (2022)
- [De Bruijn goes Neural: Causality-Aware Graph Neural Networks for Time Series Data on Dynamic Graphs](https://mlsanity.com/paper/2209.08311/) (2022)
- [Structure and inference in hypergraphs with node attributes](https://mlsanity.com/paper/2311.03857/) (2023)
- [Continuous-time Graph Representation with Sequential Survival Process](https://mlsanity.com/paper/2312.13068/) (2023)
- [Gaussian Embedding of Temporal Networks](https://mlsanity.com/paper/2405.17253/) (2024)
- [Broad Spectrum Structure Discovery in Large-Scale Higher-Order Networks](https://mlsanity.com/paper/2505.21748/) (2025)
- [Predicting Influential Higher-Order Patterns in Temporal Network Data](https://mlsanity.com/paper/2107.12100/) (2021)
- [Systematic assessment of the quality of fit of the stochastic block model for empirical networks](https://mlsanity.com/paper/2201.01658/) (2022)
- [Inference and Visualization of Community Structure in Attributed Hypergraphs Using Mixed-Membership Stochastic Block Models](https://mlsanity.com/paper/2401.00688/) (2024)
- [Inference of Sequential Patterns for Neural Message Passing in Temporal Graphs](https://mlsanity.com/paper/2406.16552/) (2024)
- [Overcoming Bias in Community Detection Evaluation](https://mlsanity.com/paper/2102.03472/) (2021)
- [Bayesian Detection of Mesoscale Structures in Pathway Data on Graphs](https://mlsanity.com/paper/2301.11120/) (2023)
- [Unifying Structural Proximity and Equivalence for Enhanced Dynamic Network Embedding](https://mlsanity.com/paper/2503.19926/) (2025)
- [Community and hyperedge inference in multiple hypergraphs](https://mlsanity.com/paper/2505.04967/) (2025)
- [Improving Graph Embeddings in Machine Learning Using Knowledge Completion with Validation in a Case Study on COVID-19 Spread](https://mlsanity.com/paper/2511.12071/) (2025)
- [Learning Multi-Order Block Structure in Higher-Order Networks](https://mlsanity.com/paper/2511.21350/) (2025)
- [Graph Neural Networks for Source Detection: A Review and Benchmark Study](https://mlsanity.com/paper/2512.20657/) (2025)

## 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.
