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

M. Génois, C. Vestergaard, J. Fournet, A. Panisson, I. Bonmarin, A. Barrat, Network Science 3, 326 (2015)

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.

URL: https://www.cambridge.org/core/journals/network-science/article/data-on-facetoface-contacts-in-an-office-building-suggest-a-lowcost-vaccination-strategy-based-on-community-linkers/18AB49AB4F2AEA33CE7501F06ADBC8E8

BIBTEX:

@article{NWS:9950811,
author = {GÉNOIS,MATHIEU and VESTERGAARD,CHRISTIAN L. and FOURNET,JULIE and PANISSON,ANDRÉ and BONMARIN,ISABELLE and BARRAT,ALAIN},
title = {Data on face-to-face contacts in an office building suggest a low-cost vaccination strategy based on community linkers},
journal = {Network Science},
volume = {3},
issue = {03},
month = {9},
year = {2015},
issn = {2050-1250},
pages = {326--347},
numpages = {22},
doi = {10.1017/nws.2015.10},
URL = {http://journals.cambridge.org/article_S2050124215000107},
}