TY - JOUR
T1 - Diffusion in colocation contact networks
T2 - the impact of nodal spatiotemporal dynamics
AU - Thomas, Bryce
AU - Jurdak, Raja
AU - Zhao, Kun
AU - Atkinson, Ian
N1 - Copyright the Author(s) 2016. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.
PY - 2016/8/8
Y1 - 2016/8/8
N2 - Temporal contact networks are studied to understand dynamic spreading phenomena such as communicable diseases or information dissemination. To establish how spatiotemporal dynamics of nodes impact spreading potential in colocation contact networks, we propose "inducement-shuffling" null models which break one or more correlations between times, locations and nodes. By reconfiguring the time and/or location of each node's presence in the network, these models induce alternative sets of colocation events giving rise to contact networks with varying spreading potential. This enables second-order causal reasoning about how correlations in nodes' spatiotemporal preferences not only lead to a given contact network but ultimately influence the network's spreading potential. We find the correlation between nodes and times to be the greatest impediment to spreading, while the correlation between times and locations slightly catalyzes spreading. Under each of the presented null models we measure both the number of contacts and infection prevalence as a function of time, with the surprising finding that the two have no direct causality.
AB - Temporal contact networks are studied to understand dynamic spreading phenomena such as communicable diseases or information dissemination. To establish how spatiotemporal dynamics of nodes impact spreading potential in colocation contact networks, we propose "inducement-shuffling" null models which break one or more correlations between times, locations and nodes. By reconfiguring the time and/or location of each node's presence in the network, these models induce alternative sets of colocation events giving rise to contact networks with varying spreading potential. This enables second-order causal reasoning about how correlations in nodes' spatiotemporal preferences not only lead to a given contact network but ultimately influence the network's spreading potential. We find the correlation between nodes and times to be the greatest impediment to spreading, while the correlation between times and locations slightly catalyzes spreading. Under each of the presented null models we measure both the number of contacts and infection prevalence as a function of time, with the surprising finding that the two have no direct causality.
UR - http://www.scopus.com/inward/record.url?scp=84983804213&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0152624
DO - 10.1371/journal.pone.0152624
M3 - Article
C2 - 27501240
AN - SCOPUS:84983804213
SN - 1932-6203
VL - 11
SP - 1
EP - 21
JO - PLoS ONE
JF - PLoS ONE
IS - 8
M1 - e0152624
ER -