Criticality and information dynamics in epidemiological models

E. Yagmur Erten*, Joseph T. Lizier, Mahendra Piraveenan, Mikhail Prokopenko

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)
240 Downloads (Pure)


Understanding epidemic dynamics has always been a challenge. As witnessed from the ongoing Zika or the seasonal Influenza epidemics, we still need to improve our analytical methods to better understand and control epidemics. While the emergence of complex sciences in the turn of the millennium have resulted in their implementation in modelling epidemics, there is still a need for improving our understanding of critical dynamics in epidemics. In this study, using agent-based modelling, we simulate a Susceptible-Infected-Susceptible (SIS) epidemic on a homogeneous network. We use transfer entropy and active information storage from information dynamics framework to characterise the critical transition in epidemiological models. Our study shows that both (bias-corrected) transfer entropy and active information storage maximise after the critical threshold (R0 = 1). This is the first step toward an information dynamics approach to epidemics. Understanding the dynamics around the criticality in epidemiological models can provide us insights about emergent diseases and disease control.

Original languageEnglish
Article number194
Pages (from-to)1-11
Number of pages11
Issue number5
Publication statusPublished - 1 May 2017
Externally publishedYes

Bibliographical note

Copyright the Author(s) 2017. 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.


  • epidemiology
  • criticality
  • information dynamics
  • phase transitions
  • agent-based simulation


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