{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T07:28:11Z","timestamp":1790839691142,"version":"4.1.0"},"reference-count":48,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2017,4,27]],"date-time":"2017-04-27T00:00:00Z","timestamp":1493251200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Australian Research Council Discovery grant","award":["DP160102742"],"award-info":[{"award-number":["DP160102742"]}]},{"name":"Australian Research Council DECRA grant","award":["DE160100630"],"award-info":[{"award-number":["DE160100630"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/e19050194","type":"journal-article","created":{"date-parts":[[2017,4,27]],"date-time":"2017-04-27T13:01:15Z","timestamp":1493298075000},"page":"194","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Criticality and Information Dynamics in Epidemiological Models"],"prefix":"10.3390","volume":"19","author":[{"given":"E.","family":"Erten","sequence":"first","affiliation":[{"name":"Centre for Complex Systems, Faculty of Engineering and IT, University of Sydney, Sydney, NSW 2006, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9910-8972","authenticated-orcid":false,"given":"Joseph","family":"Lizier","sequence":"additional","affiliation":[{"name":"Centre for Complex Systems, Faculty of Engineering and IT, University of Sydney, Sydney, NSW 2006, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahendra","family":"Piraveenan","sequence":"additional","affiliation":[{"name":"Centre for Complex Systems, Faculty of Engineering and IT, University of Sydney, Sydney, NSW 2006, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mikhail","family":"Prokopenko","sequence":"additional","affiliation":[{"name":"Centre for Complex Systems, Faculty of Engineering and IT, University of Sydney, Sydney, NSW 2006, Australia"},{"name":"Marie Bashir Institute for Infectious Diseases and Biosecurity, University of Sydney, Westmead, NSW 2145, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,4,27]]},"reference":[{"key":"ref_1","unstructured":"Bernoulli, D. 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