The availability of new data sources on human mobility is opening new avenues
for investigating the interplay of social networks, human mobility and
dynamical processes such as epidemic spreading. Here we analyze data on the
time-resolved face-to-face proximity of individuals in large-scale real-world
scenarios. We compare two settings with very different properties, a scientific
conference and a long-running museum exhibition. We track the behavioral
networks of face-to-face proximity, and characterize them from both a static
and a dynamic point of view, exposing important differences as well as striking
similarities. We use our data to investigate the dynamics of a
susceptible-infected model for epidemic spreading that unfolds on the dynamical
networks of human proximity. The spreading patterns are markedly different for
the conference and the museum case, and they are strongly impacted by the
causal structure of the network data. A deeper study of the spreading paths
shows that the mere knowledge of static aggregated networks would lead to
erroneous conclusions about the transmission paths on the dynamical networks.
Description
What's in a crowd? Analysis of face-to-face behavioral networks
%0 Generic
%1 Isella2010
%A Isella, Lorenzo
%A Stehlé, Juliette
%A Barrat, Alain
%A Cattuto, Ciro
%A Pinton, Jean-François
%A den Broeck, Wouter Van
%D 2010
%K RFID SNA
%T What's in a crowd? Analysis of face-to-face behavioral networks
%U http://arxiv.org/abs/1006.1260
%X The availability of new data sources on human mobility is opening new avenues
for investigating the interplay of social networks, human mobility and
dynamical processes such as epidemic spreading. Here we analyze data on the
time-resolved face-to-face proximity of individuals in large-scale real-world
scenarios. We compare two settings with very different properties, a scientific
conference and a long-running museum exhibition. We track the behavioral
networks of face-to-face proximity, and characterize them from both a static
and a dynamic point of view, exposing important differences as well as striking
similarities. We use our data to investigate the dynamics of a
susceptible-infected model for epidemic spreading that unfolds on the dynamical
networks of human proximity. The spreading patterns are markedly different for
the conference and the museum case, and they are strongly impacted by the
causal structure of the network data. A deeper study of the spreading paths
shows that the mere knowledge of static aggregated networks would lead to
erroneous conclusions about the transmission paths on the dynamical networks.
@misc{Isella2010,
abstract = { The availability of new data sources on human mobility is opening new avenues
for investigating the interplay of social networks, human mobility and
dynamical processes such as epidemic spreading. Here we analyze data on the
time-resolved face-to-face proximity of individuals in large-scale real-world
scenarios. We compare two settings with very different properties, a scientific
conference and a long-running museum exhibition. We track the behavioral
networks of face-to-face proximity, and characterize them from both a static
and a dynamic point of view, exposing important differences as well as striking
similarities. We use our data to investigate the dynamics of a
susceptible-infected model for epidemic spreading that unfolds on the dynamical
networks of human proximity. The spreading patterns are markedly different for
the conference and the museum case, and they are strongly impacted by the
causal structure of the network data. A deeper study of the spreading paths
shows that the mere knowledge of static aggregated networks would lead to
erroneous conclusions about the transmission paths on the dynamical networks.
},
added-at = {2011-02-19T14:37:12.000+0100},
author = {Isella, Lorenzo and Stehlé, Juliette and Barrat, Alain and Cattuto, Ciro and Pinton, Jean-François and den Broeck, Wouter Van},
biburl = {https://www.bibsonomy.org/bibtex/21efcd0b67535d6818771ffee5d0e13a7/macek},
description = {What's in a crowd? Analysis of face-to-face behavioral networks},
interhash = {c7ace93cf4d7aed9fcf9e43e9001a8e3},
intrahash = {1efcd0b67535d6818771ffee5d0e13a7},
keywords = {RFID SNA},
note = {cite arxiv:1006.1260
},
timestamp = {2013-04-07T21:21:13.000+0200},
title = {What's in a crowd? Analysis of face-to-face behavioral networks},
url = {http://arxiv.org/abs/1006.1260},
year = 2010
}