{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T16:54:27Z","timestamp":1762102467558,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,1,22]],"date-time":"2020-01-22T00:00:00Z","timestamp":1579651200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DP160102742"],"award-info":[{"award-number":["DP160102742"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>We investigated phase transitions in spatial connectivity during influenza pandemics, relating epidemic thresholds to the formation of clusters defined in terms of average infection. We employed a large-scale agent-based model of influenza spread at a national level: the Australian Census-based Epidemic Model (AceMod). In using the AceMod simulation framework, which leverages the 2016 Australian census data and generates a surrogate population of \u224823.4 million agents, we analysed the spread of simulated epidemics across geographical regions defined according to the Australian Statistical Geography Standard. We considered adjacent geographic regions with above average prevalence to be connected, and the resultant spatial connectivity was then analysed at specific time points of the epidemic. Specifically, we focused on the times when the epidemic prevalence peaks, either nationally (first wave) or at a community level (second wave). Using the percolation theory, we quantified the connectivity and identified critical regimes corresponding to abrupt changes in patterns of the spatial distribution of infection. The analysis of criticality is confirmed by computing Fisher Information in a model-independent way. The results suggest that the post-critical phase is characterised by different spatial patterns of infection developed during the first or second waves (distinguishing urban and rural epidemic peaks).<\/jats:p>","DOI":"10.3390\/e22020133","type":"journal-article","created":{"date-parts":[[2020,1,22]],"date-time":"2020-01-22T11:17:57Z","timestamp":1579691877000},"page":"133","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Phase Transitions in Spatial Connectivity during Influenza Pandemics"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1707-9248","authenticated-orcid":false,"given":"Nathan","family":"Harding","sequence":"first","affiliation":[{"name":"Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, NSW 2006, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard","family":"Spinney","sequence":"additional","affiliation":[{"name":"Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, NSW 2006, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4215-0344","authenticated-orcid":false,"given":"Mikhail","family":"Prokopenko","sequence":"additional","affiliation":[{"name":"Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, NSW 2006, Australia"},{"name":"Marie Bashir Institute for Infectious Diseases and Biosecurity, University of Sydney, Westmead, NSW 2145, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2752","DOI":"10.1073\/pnas.1708856115","article-title":"Forecasting the spatial transmission of influenza in the United States","volume":"115","author":"Pei","year":"2018","journal-title":"Proc. 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