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Epidemic Threshold in Temporally-Switching Networks

  • Leo Speidel
  • Konstantin Klemm
  • Víctor M. Eguíluz
  • Naoki Masuda
Chapter
Part of the Theoretical Biology book series (THBIO)

Abstract

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.

Notes

Acknowledgements

LS acknowledges the support provided through the Engineering and Physical Sciences Research Council (EPSRC) [grant number EP/G03706X/1]. LS and NM acknowledge the support provided through JST, ERATO, Kawarabayashi Large Graph Project. KK acknowledges funding from the Ramón y Cajal program of MINECO. KK and VME acknowledge support from projects SPASIMM (FIS2016-80067-P AEI/FEDER, UE) and NOMAQ (FIS2014-60343-P). NM acknowledges the support provided through JST, CREST.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Leo Speidel
    • 1
    • 2
  • Konstantin Klemm
    • 3
    • 4
  • Víctor M. Eguíluz
    • 4
  • Naoki Masuda
    • 5
  1. 1.Department of StatisticsUniversity of OxfordOxfordUK
  2. 2.Doctoral Training Centre in Systems BiologyUniversity of OxfordOxfordUK
  3. 3.School of Science and TechnologyNazarbayev UniversityAstanaKazakhstan
  4. 4.Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB)Palma de MallorcaSpain
  5. 5.Department of Engineering MathematicsUniversity of BristolClifton, BristolUK

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