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

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.

Statistical physics of vaccination

“…months or years. These observations clearly point out the importance of better understanding the features of close proximity contacts in order to devise efficient vaccination strategies [394,593,594,713,714]. To this end, let us extend what discussed in Section 5 for annealed and static networks by considering the efficiency of different vaccination protocols on time-varying networks.…”

When is a Network a Network?

“…In this case, K opt further provides us with the optimal order of a (higher-order) graphical representation. We apply this to three data sets on temporal networks summarized in Table 1: (EMAIL) captures time-stamped E-Mail exchanges between 167 employees of a manufacturing company [16], (HOSP) contains timestamped contacts between 75 healthcare workers in a hospital [33] and (WORK) captures time-stamped contacts between 92 o ce workers in a company [8]. (HOSP) and (WORK) were recorded using sensor badges sensing face-to-face encounters at high temporal resolution [8,33].…”

Backtracking and Mixing Rate of Diffusion on Uncorrelated Temporal Networks

“…As a next step, we test the importance of this effect in real-world systems by considering four datasets of face-to-face contacts described in [21][22][23][24][25]. From the recorded contacts, we obtain the largest component with a typical size of a few hundred nodes.…”