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Open AccessArticle

Criticality and Information Dynamics in Epidemiological Models

Centre for Complex Systems, Faculty of Engineering and IT, University of Sydney, Sydney, NSW 2006, Australia
Marie Bashir Institute for Infectious Diseases and Biosecurity, University of Sydney, Westmead, NSW 2145, Australia
Author to whom correspondence should be addressed.
Current address: Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland.
Academic Editor: J. A. Tenreiro Machado
Entropy 2017, 19(5), 194;
Received: 2 March 2017 / Revised: 24 April 2017 / Accepted: 25 April 2017 / Published: 27 April 2017
(This article belongs to the Special Issue Complexity, Criticality and Computation (C³))
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 ( R 0 = 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. View Full-Text
Keywords: epidemiology; criticality; information dynamics; phase transitions; agent-based simulation epidemiology; criticality; information dynamics; phase transitions; agent-based simulation
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Erten, E.Y.; Lizier, J.T.; Piraveenan, M.; Prokopenko, M. Criticality and Information Dynamics in Epidemiological Models. Entropy 2017, 19, 194.

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