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Article

Phase Transitions in Spatial Connectivity during Influenza Pandemics

1
Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, NSW 2006, Australia
2
Marie Bashir Institute for Infectious Diseases and Biosecurity, University of Sydney, Westmead, NSW 2145, Australia
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(2), 133; https://doi.org/10.3390/e22020133
Received: 12 December 2019 / Revised: 14 January 2020 / Accepted: 16 January 2020 / Published: 22 January 2020
(This article belongs to the Special Issue New Advances in Biocomplexity)
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 ≈23.4 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). View Full-Text
Keywords: critical dynamics; epidemiology; phase transitions; agent-based modelling; epidemic modelling critical dynamics; epidemiology; phase transitions; agent-based modelling; epidemic modelling
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MDPI and ACS Style

Harding, N.; Spinney, R.; Prokopenko, M. Phase Transitions in Spatial Connectivity during Influenza Pandemics. Entropy 2020, 22, 133. https://doi.org/10.3390/e22020133

AMA Style

Harding N, Spinney R, Prokopenko M. Phase Transitions in Spatial Connectivity during Influenza Pandemics. Entropy. 2020; 22(2):133. https://doi.org/10.3390/e22020133

Chicago/Turabian Style

Harding, Nathan, Richard Spinney, and Mikhail Prokopenko. 2020. "Phase Transitions in Spatial Connectivity during Influenza Pandemics" Entropy 22, no. 2: 133. https://doi.org/10.3390/e22020133

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