Phase Transitions in Spatial Connectivity during Influenza Pandemics 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 largescale agentbased model of influenza spread at a national level: the Australian Censusbased Epidemic Model (ACEmathsizesmallMod). In using the ACEmathsizesmallMod simulation framework, which leverages the 2016 Australian census data and generates a surrogate population of xe2x89x8823.4 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 modelindependent way. The results suggest that the postcritical phase is characterised by different spatial patterns of infection developed during the first or second waves (distinguishing urban and rural epidemic peaks).

Hybrid Modeling of Ebola Propagation The Ebola virus disease (EVD) epidemic that occurred in West Africa between 201416 resulted in over 28,000 cases and 11,000 deaths  one of the deadliest to date. A generalized model of the spatiotemporal progression of EVD for Liberia, Guinea, and Sierra Leone in 201416 remains elusive. There is also a disconnect in the literature on which interventions are most effective in curbing disease progression. To solve these two key issues, we designed a hybrid agentbased and compartmental model that switches from one paradigm to the other on a stochastic threshold. We modeled disease progression with promising accuracy using WHO datasets.