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Background: The COVID-19 pandemic stressed public health systems worldwide due to emergence of several highly transmissible variants of concern. Diverse and complex intervention policies deployed over the last years have shown varied effectiveness in controlling the pandemic. However, a systematic analysis and modelling of the combined effects of different viral lineages and complex intervention policies remains a challenge due to the lack of suitable measures of pandemic inequality and nonlinear effects. Methods: Using large-scale agent-based modelling and a high-resolution computational simulation matching census-based demographics of Australia, we carried out a systematic comparative analysis of several COVID-19 pandemic scenarios. The scenarios covered two most recent Australian census years (2016 and 2021), three variants of concern (ancestral, Delta and Omicron), and five representative intervention policies. We introduced pandemic Lorenz curves measuring an unequal distribution of the pandemic severity across local areas. We also quantified pandemic biomodality, distinguishing between urban and regional waves, and measured bifurcations in the effectiveness of interventions. Results: We quantified nonlinear effects of population heterogeneity on the pandemic severity, highlighting that (i) the population growth amplifies pandemic peaks, (ii) the changes in population size amplify the peak incidence more than the changes in density, and (iii) the pandemic severity is distributed unequally across local areas. We also examined and delineated the effects of urbanisation on the incidence bimodality, distinguishing between urban and regional pandemic waves. Finally, we quantified and examined the impact of school closures, complemented by partial interventions, and identified the conditions when inclusion of school closures may decisively control the transmission. Conclusions: Public health response to long-lasting pandemics must be frequently reviewed and adapted to demographic changes. To control recurrent waves, mass-vaccination rollouts need to be complemented by partial NPIs. Healthcare and vaccination resources need to be prioritised towards the localities and regions with high population growth and/or high density. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has brought the combined disciplines of public health, infectious disease and policy modelling squarely into the spotlight. Never before have decisions regarding public health measures and their impacts been such a topic of international deliberation, from the level of individuals and communities through to global leaders. Nor have models-developed at rapid pace and often in the absence of complete information-ever been so central to the decision-making process. However, after nearly 3 years of experience with modelling, policy-makers need to be more confident about which models will be most helpful to support them when taking public health decisions, and modellers need to better understand the factors that will lead to successful model adoption and utilization. We present a three-stage framework for achieving these ends. [ABSTRACT FROM AUTHOR]

An abstract of the article "Maximized directed information transfer in critical neuronal networks," by several researchers including, Joseph Lizier, Mikhail Prokopenko, and Michael Breakspear is presented.

An abstract of the research paper "Spatiotemporal Information Transfer Pattern Differences in Motor Selection," by Joseph T. Lizier and colleagues, which was presented at the Twentieth Annual Computational Neuroscience Meeting held at Stockholm, Sweden from July 23-28, 2011 is presented.

Distributed computation in the brain is a complex process, involving interactions between many regions in order to achieve a particular task. We present an analysis of functional Magnetic Resonance Imaging (fMRI) measurements of brain activity in 16 localized regions recorded while eight subjects (who gave informed written consent) control a mouse with their right hand to track a moving target on a computer screen. We examine the underlying interaction structure between region pairs and the changes in that structure as a function of the difficulty of the tracking task. Our approach is distinguished in using asymmetric, multivariate, information-theoretical analysis, which captures not only non-linear relationships, but also collective interactions arising from groups of up to seven voxels in each region, and the direction of these relationships. The statistical significance of the average transfer entropy (a directed measure of information transfer) yields a distinct 3-tier directed interaction structure that underlies all task difficulties (see Figure 1). Importantly, this structure connects movement planning to visual and motor control regions. Random-effects statistical parametric maps (SPM) analysis is then used to determine which pairs of regions have a) more in common (using the mutual information), or b) a more pronounced directional relationship (using the transfer entropy) as the task difficulty increases. Most significantly (see Figure 2), we identify an increased coupling between regions involved in movement planning (left SMA and left PMd) and execution (right cerebellum for right hand and right SC for eye movements) with task difficulty. It is likely these methods and extensions to them can be used to identify inter-regional structural changes due to other tasks and coherent information transfer structures in the cortex. [ABSTRACT FROM AUTHOR]