Epidemic Threshold in Temporally-Switching Networks
Speidel, L; Klemm, K; Eguíluz, VM; Masuda, N
in Temporal Network Epidemiology. (Edited by Masuda, N. and Holme, P.), Springer, 161-177 (2017)
in Temporal Network Epidemiology. (Edited by Masuda, N. and Holme, P.), Springer, 161-177 (2017)
Infectious diseases have been modelled on networks that summarise physical contacts or close proximity of individuals. These networks are known to be complex in both their structure and how they change over time. We present an overview of recent progress in numerically determining the epidemic threshold in temporally-switching networks, and illustrate that slower switching of snapshots relative to epidemic dynamics lowers the epidemic threshold. Therefore, ignoring the temporally-varying nature of networks may underestimate endemicity. We also identify a predictor for the magnitude of this shift which is based on the commutator norm of snapshot adjacency matrices.
Additional files
Related research projects
SPASIMM
Spatiotemporality in sociobological interactions, models and methods
I.P.: Konstantin Klemm, Víctor M. Eguíluz
The Physics of Complex Systems is experiencing an important research activity, first due to the development of the Theory of Complex Networks, and second due to Big Data. The model driven approach …