ORCID Profile
0000-0002-4215-0344
Current Organisations
Australian National University
,
University of Western Australia
,
University of Sydney
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Publisher: American Physical Society (APS)
Date: 29-06-2015
Publisher: Springer Science and Business Media LLC
Date: 07-12-2011
Publisher: American Physical Society (APS)
Date: 22-10-2019
Publisher: Springer Science and Business Media LLC
Date: 10-10-2006
Publisher: Elsevier BV
Date: 06-2015
DOI: 10.1016/J.BBR.2015.03.003
Abstract: Androgen deprivation in males has detrimental effects on various tissues and bodily functions, some of which can be restored by estradiol (E2) administration. We investigated how the duration of androgen deprivation affects the autoregulation of estrogen receptors (ERs) levels in core brain areas associated with sexual behavior and cognition, as well as in pelvic floor muscles (PFM). We also measured c-Fos levels in brain areas associated with sexual behavior shortly after the rats mated. Prolonged castration increases ERα levels in the preoptic area (POA) and E2 treatment reverses these effects. In the POA, c-Fos levels after mating are not affected by the duration of androgen deprivation and/or E2 treatment. ERβ levels in the POA as well as c-Fos levels in the POA and the core area of nucleus accumbens correlate with the mounting frequency for E2-treated Short-Term castrates. Additionally, ERβ levels in the medial amygdala are positively correlated with the mounting frequency of Long-Term castrates that received E2 treatment. In the hippoc us, ERs are downregulated only when E2 is administered early after castration, whereas downregulation of ERα in the prefrontal cortex only occurs with delayed E2 treatment. Early, but not delayed, E2 treatment after castration increases ERβ levels in the bulbocavernosus and ERα levels in the levator ani of male rats. Our data suggest that the duration of androgen deprivation may influence the autoregulation of ERs by E2 treatment in select brain areas and pelvic floor muscles of male rats.
Publisher: MDPI AG
Date: 16-06-2021
DOI: 10.3390/E23060757
Abstract: The emergence of global order in complex systems with locally interacting components is most striking at criticality, where small changes in control parameters result in a sudden global reorganization. We study the thermodynamic efficiency of interactions in self-organizing systems, which quantifies the change in the system’s order per unit of work carried out on (or extracted from) the system. We analytically derive the thermodynamic efficiency of interactions for the case of quasi-static variations of control parameters in the exactly solvable Curie–Weiss (fully connected) Ising model, and demonstrate that this quantity erges at the critical point of a second-order phase transition. This ergence is shown for quasi-static perturbations in both control parameters—the external field and the coupling strength. Our analysis formalizes an intuitive understanding of thermodynamic efficiency across erse self-organizing dynamics in physical, biological, and social domains.
Publisher: American Physical Society (APS)
Date: 24-08-2016
Publisher: Bulletin of Marine Science
Date: 2016
Publisher: IEEE
Date: 2005
DOI: 10.1109/EH.2005.14
Publisher: Wiley
Date: 14-09-2021
DOI: 10.1111/ECOG.05953
Abstract: Animals follow specific movement patterns and search strategies to maximize encounters with essential resources (e.g. prey, favourable habitat) while minimizing exposures to suboptimal conditions (e.g. competitors, predators). While describing spatiotemporal patterns in animal movement from tracking data is common, understanding the associated search strategies employed continues to be a key challenge in ecology. Moreover, studies in marine ecology commonly focus on singular aspects of species' movements, however using multiple analytical approaches can further enable researchers to identify ecological phenomena and resolve fundamental ecological questions relating to movement. Here, we used a set of statistical physics‐based methods to analyze satellite tracking data from three co‐occurring apex predators (tiger, great hammerhead and bull sharks) that predominantly inhabit productive coastal regions of the northwest Atlantic Ocean and Gulf of Mexico. We analyzed data from 96 sharks and calculated a range of metrics, including each species' displacements, turning angles, dispersion, space‐use and community‐wide movement patterns to characterize each species' movements and identify potential search strategies. Our comprehensive approach revealed high interspecific variability in shark movement patterns and search strategies. Tiger sharks displayed near‐random movements consistent with a Brownian strategy commonly associated with movements through resource‐rich habitats. Great hammerheads showed a mixed‐movement strategy including Brownian and resident‐type movements, suggesting adaptation to widespread and localized high resource availability. Bull sharks followed a resident movement strategy with restricted movements indicating localized high resource availability. We hypothesize that the species‐specific search strategies identified here may help foster the co‐existence of these sympatric apex predators. Following this comprehensive approach provided novel insights into spatial ecology and assisted with identifying unique movement and search strategies. Similar future studies of animal movement will help characterize movement patterns and also enable the identification of search strategies to help elucidate the ecological drivers of movement and to understand species' responses to environmental change.
Publisher: Springer Science and Business Media LLC
Date: 29-11-2011
DOI: 10.1007/S12064-011-0137-9
Abstract: In recent years, information theory has come into the focus of researchers interested in the sensorimotor dynamics of both robots and living beings. One root for these approaches is the idea that living beings are information processing systems and that the optimization of these processes should be an evolutionary advantage. Apart from these more fundamental questions, there is much interest recently in the question how a robot can be equipped with an internal drive for innovation or curiosity that may serve as a drive for an open-ended, self-determined development of the robot. The success of these approaches depends essentially on the choice of a convenient measure for the information. This article studies in some detail the use of the predictive information (PI), also called excess entropy or effective measure complexity, of the sensorimotor process. The PI of a process quantifies the total information of past experience that can be used for predicting future events. However, the application of information theoretic measures in robotics mostly is restricted to the case of a finite, discrete state-action space. This article aims at applying the PI in the dynamical systems approach to robot control. We study linear systems as a first step and derive exact results for the PI together with explicit learning rules for the parameters of the controller. Interestingly, these learning rules are of Hebbian nature and local in the sense that the synaptic update is given by the product of activities available directly at the pertinent synaptic ports. The general findings are exemplified by a number of case studies. In particular, in a two-dimensional system, designed at mimicking embodied systems with latent oscillatory locomotion patterns, it is shown that maximizing the PI means to recognize and lify the latent modes of the robotic system. This and many other ex les show that the learning rules derived from the maximum PI principle are a versatile tool for the self-organization of behavior in complex robotic systems.
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Wiley
Date: 23-04-2015
DOI: 10.1111/JFB.12671
Abstract: The objective of this study was to determine the size and maturity status of the male blue sharks Prionace glauca attempting to mate with small, immature females in the north-west Atlantic Ocean. The relationship between male curved fork length (LFC ) and jaw gape was used in conjunction with the diameter of the mating scar to estimate the LFC and infer the maturity status of the male shark that produced the mating scar. The results indicate that mature males with a mean ± s.d. LFC of 218 cm ± 23 cm were attempting to mate with sexually immature females.
Publisher: Informa UK Limited
Date: 06-2013
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: IOP Publishing
Date: 26-09-2008
Publisher: Springer Science and Business Media LLC
Date: 18-08-2022
Publisher: American Physical Society (APS)
Date: 26-04-2019
Publisher: IOP Publishing
Date: 02-2010
Publisher: Springer International Publishing
Date: 2017
Publisher: IGI Global
Date: 2006
DOI: 10.4018/978-1-59140-827-7.CH007
Abstract: An approach to the structural health management (SHM) of future aerospace vehicles is presented. Such systems will need to operate robustly and intelligently in very adverse environments, and be capable of self-monitoring (and ultimately, self-repair). Networks of embedded sensors, active elements, and intelligence have been selected to form a prototypical “smart skin” for the aerospace structure, and a methodology based on multi-agent networks developed for the system to implement aspects of SHM by processes of self-organisation. Problems are broken down with the aid of a “response matrix” into one of three different scenarios: critical, sub-critical, and minor damage. From these scenarios, three components are selected, these being: (a) the formation of “impact boundaries” around damage sites, (b) self-assembling “impact networks”, and (c) shape replication. A genetic algorithm exploiting phase transitions in systems dynamics has been developed to evolve localised algorithms for impact boundary formation, addressing component (a). An ant colony optimisation (ACO) algorithm, extended by way of an adaptive dead reckoning scheme (ADRS) and which incorporates a “pause” heuristic, has been developed to address (b). Both impact boundary formation and ACO-ADRS algorithms have been successfully implemented on a “concept demonstrator”, while shape replication algorithms addressing component (c) have been successfully simulated.
Publisher: MDPI AG
Date: 22-01-2020
DOI: 10.3390/E22020133
Abstract: 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).
Publisher: Springer Science and Business Media LLC
Date: 11-11-2020
DOI: 10.1038/S41467-020-19393-6
Abstract: There is a continuing debate on relative benefits of various mitigation and suppression strategies aimed to control the spread of COVID-19. Here we report the results of agent-based modelling using a fine-grained computational simulation of the ongoing COVID-19 pandemic in Australia. This model is calibrated to match key characteristics of COVID-19 transmission. An important calibration outcome is the age-dependent fraction of symptomatic cases, with this fraction for children found to be one-fifth of such fraction for adults. We apply the model to compare several intervention strategies, including restrictions on international air travel, case isolation, home quarantine, social distancing with varying levels of compliance, and school closures. School closures are not found to bring decisive benefits unless coupled with high level of social distancing compliance. We report several trade-offs, and an important transition across the levels of social distancing compliance, in the range between 70% and 80% levels, with compliance at the 90% level found to control the disease within 13–14 weeks, when coupled with effective case isolation and international travel restrictions.
Publisher: IEEE
Date: 2004
Publisher: The MIT Press
Date: 2004
Publisher: Public Library of Science (PLoS)
Date: 12-07-2012
Publisher: Frontiers Media SA
Date: 2016
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11893011_41
Publisher: The MIT Press
Date: 08-09-2004
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11893011_42
Publisher: Springer Science and Business Media LLC
Date: 18-07-2011
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 16-12-2020
Publisher: AIP
Date: 2005
DOI: 10.1063/1.1916892
Publisher: Unpublished
Date: 2011
Publisher: MIT Press - Journals
Date: 09-2005
DOI: 10.1162/106454605774270642
Abstract: We consider a hierarchical multicellular sensing and communication network, embedded in an ageless aerospace vehicle that is expected to detect and react to multiple impacts and damage over a wide range of impact energies. In particular, we investigate self-organization of impact boundaries enclosing critically damaged areas, and impact networks connecting remote cells that have detected noncritical impacts. Each level of the hierarchy is shown to have distinct higher-order emergent properties, desirable in self-monitoring and self-repairing vehicles. In addition, cells and communication messages are shown to need memory (hysteresis) in order to retain desirable emergent behavior within and between various hierarchical levels. Spatiotemporal robustness of self-organizing hierarchies is quantitatively measured with graph-theoretic and information-theoretic techniques, such as the Shannon entropy. This allows us to clearly identify phase transitions separating chaotic dynamics from ordered and robust patterns.
Publisher: Springer International Publishing
Date: 2018
Publisher: Elsevier BV
Date: 11-2020
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer Berlin Heidelberg
Date: 2001
Publisher: Springer Science and Business Media LLC
Date: 30-11-2011
DOI: 10.1007/S12064-011-0145-9
Abstract: We have recently presented a framework for the information dynamics of distributed computation that locally identifies the component operations of information storage, transfer, and modification. We have observed that while these component operations exist to some extent in all types of computation, complex computation is distinguished in having coherent structure in its local information dynamics profiles. In this article, we conjecture that coherent information structure is a defining feature of complex computation, particularly in biological systems or artificially evolved computation that solves human-understandable tasks. We present a methodology for studying coherent information structure, consisting of state-space diagrams of the local information dynamics and a measure of structure in these diagrams. The methodology identifies both clear and "hidden" coherent structure in complex computation, most notably reconciling conflicting interpretations of the complexity of the Elementary Cellular Automata rule 22.
Publisher: Springer London
Date: 24-11-2007
Publisher: Springer Science and Business Media LLC
Date: 18-07-2011
Publisher: Springer London
Date: 24-11-2007
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: American Physical Society (APS)
Date: 23-08-2018
Publisher: World Scientific Pub Co Pte Lt
Date: 05-2013
Publisher: Springer Berlin Heidelberg
Date: 2000
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2015
Publisher: MIT Press - Journals
Date: 02-2017
DOI: 10.1162/ARTL_A_00221
Abstract: We develop and apply several novel methods quantifying dynamic multi-agent team interactions. These interactions are detected information-theoretically and captured in two ways: via (i) directed networks (interaction diagrams) representing significant coupled dynamics between pairs of agents, and (ii) state-space plots (coherence diagrams) showing coherent structures in Shannon information dynamics. This model-free analysis relates, on the one hand, the information transfer to responsiveness of the agents and the team, and, on the other hand, the information storage within the team to the team's rigidity and lack of tactical flexibility. The resultant interaction and coherence diagrams reveal implicit interactions, across teams, that may be spatially long-range. The analysis was verified with a statistically significant number of experiments (using simulated football games, produced during RoboCup 2D Simulation League matches), identifying the zones of the most intense competition, the extent and types of interactions, and the correlation between the strength of specific interactions and the results of the matches.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2012
DOI: 10.1109/TCBB.2010.80
Publisher: Springer London
Date: 2013
Publisher: MIT Press - Journals
Date: 10-2011
DOI: 10.1162/ARTL_A_00040
Abstract: Small-world networks have been one of the most influential concepts in complex systems science, partly due to their prevalence in naturally occurring networks. It is often suggested that this prevalence is due to an inherent capability to store and transfer information efficiently. We perform an ensemble investigation of the computational capabilities of small-world networks as compared to ordered and random topologies. To generate dynamic behavior for this experiment, we imbue the nodes in these networks with random Boolean functions. We find that the ordered phase of the dynamics (low activity in dynamics) and topologies with low randomness are dominated by information storage, while the chaotic phase (high activity in dynamics) and topologies with high randomness are dominated by information transfer. Information storage and information transfer are somewhat balanced (crossed over) near the small-world regime, providing quantitative evidence that small-world networks do indeed have a propensity to combine comparably large information storage and transfer capacity.
Publisher: MIT Press - Journals
Date: 10-2011
DOI: 10.1162/ARTL_A_00041
Abstract: We study the order-chaos phase transition in random Boolean networks (RBNs), which have been used as models of gene regulatory networks. In particular we seek to characterize the phase diagram in information-theoretic terms, focusing on the effect of the control parameters (activity level and connectivity). Fisher information, which measures how much system dynamics can reveal about the control parameters, offers a natural interpretation of the phase diagram in RBNs. We report that this measure is maximized near the order-chaos phase transitions in RBNs, since this is the region where the system is most sensitive to its parameters. Furthermore, we use this study of RBNs to clarify the relationship between Shannon and Fisher information measures.
Publisher: Inter-Research Science Center
Date: 23-08-2018
DOI: 10.3354/MEPS12671
Publisher: Springer Science and Business Media LLC
Date: 16-04-2019
DOI: 10.1038/S41598-019-42582-3
Abstract: We examine non-typhoidal Salmonella (S. Typhimurium or STM) epidemics as complex systems, driven by evolution and interactions of erse microbial strains, and focus on emergence of successful strains. Our findings challenge the established view that seasonal epidemics are associated with random sets of co-circulating STM genotypes. We use high-resolution molecular genotyping data comprising 17,107 STM isolates representing nine consecutive seasonal epidemics in Australia, genotyped by multiple-locus variable-number tandem-repeats analysis (MLVA). From these data, we infer weighted undirected networks based on distances between the MLVA profiles, depicting epidemics as networks of in idual bacterial strains. The network analysis demonstrated dichotomy in STM populations which split into two distinct genetic branches, with markedly different prevalences. This distinction revealed the emergence of dominant STM strains defined by their local network topological properties, such as centrality, while correlating the development of new epidemics with global network features, such as small-world propensity.
Publisher: Springer Berlin Heidelberg
Date: 2014
Publisher: American Institute of Mathematical Sciences (AIMS)
Date: 10-2012
Publisher: American Society of Ichthyologists and Herpetologists (ASIH)
Date: 18-12-2012
DOI: 10.1643/CE-11-012
Publisher: Springer Science and Business Media LLC
Date: 06-2017
Publisher: Springer Science and Business Media LLC
Date: 09-07-2020
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer International Publishing
Date: 2015
Publisher: AIP Publishing
Date: 09-2010
DOI: 10.1063/1.3486801
Abstract: Distributed computation can be described in terms of the fundamental operations of information storage, transfer, and modification. To describe the dynamics of information in computation, we need to quantify these operations on a local scale in space and time. In this paper we extend previous work regarding the local quantification of information storage and transfer, to explore how information modification can be quantified at each spatiotemporal point in a system. We introduce the separable information, a measure which locally identifies information modification events where separate inspection of the sources to a computation is misleading about its outcome. We apply this measure to cellular automata, where it is shown to be the first direct quantitative measure to provide evidence for the long-held conjecture that collisions between emergent particles therein are the dominant information modification events.
Publisher: Elsevier BV
Date: 10-2005
Publisher: Springer Science and Business Media LLC
Date: 23-06-2014
DOI: 10.1038/SREP05394
Publisher: AIP
Date: 2003
DOI: 10.1063/1.1570322
Publisher: Public Library of Science (PLoS)
Date: 10-2015
Publisher: Public Library of Science (PLoS)
Date: 22-08-2017
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11552451_109
Publisher: Springer Berlin Heidelberg
Date: 2014
Publisher: WORLD SCIENTIFIC
Date: 09-2012
Publisher: Springer Science and Business Media LLC
Date: 27-08-2010
DOI: 10.1007/S10827-010-0271-2
Abstract: The human brain undertakes highly sophisticated information processing facilitated by the interaction between its sub-regions. We present a novel method for interregional connectivity analysis, using multivariate extensions to the mutual information and transfer entropy. The method allows us to identify the underlying directed information structure between brain regions, and how that structure changes according to behavioral conditions. This method is distinguished in using asymmetric, multivariate, information-theoretical analysis, which captures not only directional and non-linear relationships, but also collective interactions. Importantly, the method is able to estimate multivariate information measures with only relatively little data. We demonstrate the method to analyze functional magnetic resonance imaging time series to establish the directed information structure between brain regions involved in a visuo-motor tracking task. Importantly, this results in a tiered structure, with known movement planning regions driving visual and motor control regions. Also, we examine the changes in this structure as the difficulty of the tracking task is increased. We find that task difficulty modulates the coupling strength between regions of a cortical network involved in movement planning and between motor cortex and the cerebellum which is involved in the fine-tuning of motor control. It is likely these methods will find utility in identifying interregional structure (and experimentally induced changes in this structure) in other cognitive tasks and data modalities.
Publisher: Springer Berlin Heidelberg
Date: 2001
Publisher: MDPI AG
Date: 02-01-2021
DOI: 10.3390/E23010066
Abstract: We propose a non-equilibrium framework for modelling the evolution of cities, which describes intra-urban migration as an irreversible diffusive process. We validate this framework using the actual migration data for the Australian capital cities. With respect to the residential relocation, the population is shown to be composed of two distinct groups, exhibiting different relocation frequencies. In the context of the developed framework, these groups can be interpreted as two components of a binary fluid mixture, each with its own diffusive relaxation time. Using this approach, we obtain long-term predictions of the cities’ spatial structures, which define their equilibrium population distribution.
Publisher: Springer Science and Business Media LLC
Date: 13-07-2009
Publisher: American Association for the Advancement of Science (AAAS)
Date: 05-10-2018
Abstract: The historic urban collapse of Angkor is linked to infrastructural complexity and climatic variability.
Publisher: Springer International Publishing
Date: 2019
Publisher: American Physical Society (APS)
Date: 27-09-2018
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11839132_19
Publisher: Elsevier BV
Date: 07-2021
Publisher: MDPI AG
Date: 23-01-2200
DOI: 10.3390/E20020051
Abstract: The Kullback–Leibler (KL) ergence is a fundamental measure of information geometry that is used in a variety of contexts in artificial intelligence. We show that, when system dynamics are given by distributed nonlinear systems, this measure can be decomposed as a function of two information-theoretic measures, transfer entropy and stochastic interaction. More specifically, these measures are applicable when selecting a candidate model for a distributed system, where in idual subsystems are coupled via latent variables and observed through a filter. We represent this model as a directed acyclic graph (DAG) that characterises the unidirectional coupling between subsystems. Standard approaches to structure learning are not applicable in this framework due to the hidden variables however, we can exploit the properties of certain dynamical systems to formulate exact methods based on differential topology. We approach the problem by using reconstruction theorems to derive an analytical expression for the KL ergence of a candidate DAG from the observed dataset. Using this result, we present a scoring function based on transfer entropy to be used as a subroutine in a structure learning algorithm. We then demonstrate its use in recovering the structure of coupled Lorenz and Rössler systems.
Publisher: Frontiers Media SA
Date: 19-05-2014
Publisher: Springer Berlin Heidelberg
Date: 1998
Publisher: Informa UK Limited
Date: 10-2009
DOI: 10.2976/1.3175813
Publisher: IEEE
Date: 04-2008
DOI: 10.1109/IPSN.2008.35
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11553090_89
Publisher: Springer London
Date: 2013
Publisher: American Physical Society (APS)
Date: 31-03-2017
Publisher: MDPI AG
Date: 11-07-2019
Abstract: We present a series of SIR-network models, extended with a game-theoretic treatment of imitation dynamics which result from regular population mobility across residential and work areas and the ensuing interactions. Each considered SIR-network model captures a class of vaccination behaviours influenced by epidemic characteristics, interaction topology, and imitation dynamics. Our focus is the resultant vaccination coverage, produced under voluntary vaccination schemes, in response to these varying factors. Using the next generation matrix method, we analytically derive and compare expressions for the basic reproduction number R 0 for the proposed SIR-network models. Furthermore, we simulate the epidemic dynamics over time for the considered models, and show that if in iduals are sufficiently responsive towards the changes in the disease prevalence, then the more expansive travelling patterns encourage convergence to the endemic, mixed equilibria. On the contrary, if in iduals are insensitive to changes in the disease prevalence, we find that they tend to remain unvaccinated. Our results concur with earlier studies in showing that residents from highly connected residential areas are more likely to get vaccinated. We also show that the existence of the in iduals committed to receiving vaccination reduces R 0 and delays the disease prevalence, and thus is essential to containing epidemics.
Publisher: ACM
Date: 25-07-2005
Publisher: Public Library of Science (PLoS)
Date: 17-04-2023
DOI: 10.1371/JOURNAL.PGPH.0001427
Abstract: We modelled emergence and spread of the Omicron variant of SARS-CoV-2 in Australia between December 2021 and June 2022. This pandemic stage exhibited a erse epidemiological profile with emergence of co-circulating sub-lineages of Omicron, further complicated by differences in social distancing behaviour which varied over time. Our study delineated distinct phases of the Omicron-associated pandemic stage, and retrospectively quantified the adoption of social distancing measures, fluctuating over different time periods in response to the observable incidence dynamics. We also modelled the corresponding disease burden, in terms of hospitalisations, intensive care unit occupancy, and mortality. Supported by good agreement between simulated and actual health data, our study revealed that the nonlinear dynamics observed in the daily incidence and disease burden were determined not only by introduction of sub-lineages of Omicron, but also by the fluctuating adoption of social distancing measures. Our high-resolution model can be used in design and evaluation of public health interventions during future crises.
Publisher: Elsevier BV
Date: 11-2012
Publisher: Elsevier BV
Date: 12-2019
DOI: 10.1016/J.PLREV.2018.12.003
Abstract: In this paper we explore several fundamental relations between formal systems, algorithms, and dynamical systems, focussing on the roles of undecidability, universality, diagonalization, and self-reference in each of these computational frameworks. Some of these interconnections are well-known, while some are clarified in this study as a result of a fine-grained comparison between recursive formal systems, Turing machines, and Cellular Automata (CAs). In particular, we elaborate on the diagonalization argument applied to distributed computation carried out by CAs, illustrating the key elements of Gödel's proof for CAs. The comparative analysis emphasizes three factors which underlie the capacity to generate undecidable dynamics within the examined computational frameworks: (i) the program-data duality (ii) the potential to access an infinite computational medium and (iii) the ability to implement negation. The considered adaptations of Gödel's proof distinguish between computational universality and undecidability, and show how the diagonalization argument exploits, on several levels, the self-referential basis of undecidability.
Publisher: Frontiers Media SA
Date: 16-09-2020
Publisher: Springer Berlin Heidelberg
Date: 2014
Publisher: Springer Science and Business Media LLC
Date: 26-06-2009
Publisher: Springer Berlin Heidelberg
Date: 2000
Publisher: Springer Berlin Heidelberg
Date: 2014
Publisher: Springer Science and Business Media LLC
Date: 06-05-2020
DOI: 10.1038/S41598-020-64183-1
Abstract: Understanding the impact of behavior dependent mobility in the spread of epidemics and social disorders is an outstanding problem in computational epidemiology. We present a modelling approach for the study of mobility that adapts dynamically according to in idual state, epidemic/social-contagion state and network topology in accordance with limited data and/or common behavioral models. We demonstrate that even for simple compartmental network processes, our approach leads to complex spatial patterns of infection in the endemic state dependent on in idual behavior. Specifically, we characterize the resulting phenomena in terms of phase separation, highlighting phase transitions between distinct spatial states and determining the systems’ phase diagram. The existence of such phases implies that small changes in the populations’ perceptions could lead to drastic changes in the spatial extent and morphology of the epidemic/social phenomena.
Publisher: Frontiers Media SA
Date: 28-11-2016
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11554028_79
Publisher: American Physical Society (APS)
Date: 16-01-2018
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11554028_81
Publisher: Informa UK Limited
Date: 2020
DOI: 10.1080/17513758.2020.1720322
Abstract: We review research studies which use game theory to model the decision-making of in iduals during an epidemic, attempting to classify the literature and identify the emerging trends in this field. The literature is classified based on (i) type of population modelling (classical or network-based), (ii) frequency of the game (non-repeated or repeated), and (iii) type of strategy adoption (self-learning or imitation). The choice of model is shown to depend on many factors such as the immunity to the disease, the strength of immunity conferred by the vaccine, the size of population and the level of mixing therein. We highlight that while early studies used classical compartmental modelling with self-learning games, in recent years, there is a substantial growth of network-based modelling with imitation games. The review indicates that game theory continues to be an effective tool to model decision-making by in iduals with respect to intervention (vaccination or social distancing).
Publisher: The Royal Society
Date: 19-10-2018
Abstract: We present a novel approach to the study of epidemics on networks as thermodynamic phenomena, quantifying the thermodynamic efficiency of contagions, considered as distributed computational processes. Modelling SIS dynamics on a contact network statistical-mechanically, we follow the maximum entropy (MaxEnt) principle to obtain steady-state distributions and derive, under certain assumptions, relevant thermodynamic quantities both analytically and numerically. In particular, we obtain closed-form solutions for some cases, while interpreting key epidemic variables, such as the reproductive ratio of a SIS model, in a statistical mechanical setting. On the other hand, we consider configuration and free entropy, as well as the Fisher information, in the epidemiological context. This allowed us to identify criticality and distinct phases of epidemic processes. For each of the considered thermodynamic quantities, we compare the analytical solutions informed by the MaxEnt principle with the numerical estimates for SIS epidemics simulated on Watts–Strogatz random graphs.
Publisher: Wiley
Date: 12-06-2023
DOI: 10.1111/JFB.15464
Abstract: Sharks are an important attraction for aquaria however, larger species can rarely be kept indefinitely. To date, there has been little work tracking shark movements post‐release to the wild. The authors used high‐resolution biologgers to monitor a sub‐adult tiger shark's pre‐ and post‐release fine‐scale movements following 2 years of captivity in an aquarium. They also compared its movement with that of a wild shark tagged nearby. Despite the differences in movement between the two sharks, with vertical oscillations notably absent and greater levels of turning seen from the released shark, the captive shark survived the release. These biologgers improve insight into post‐release movements of captive sharks.
Publisher: The Royal Society
Date: 05-2016
Abstract: The cognitive ability to form social links that can bind in iduals together into large cooperative groups for safety and resource sharing was a key development in human evolutionary and social history. The ‘social brain hypothesis’ argues that the size of these social groups is based on a neurologically constrained capacity for maintaining long-term stable relationships. No model to date has been able to combine a specific socio-cognitive mechanism with the discrete scale invariance observed in ethnographic studies. We show that these properties result in nested layers of self-organizing Erdős–Rényi networks formed by each in idual's ability to maintain only a small number of social links. Each set of links plays a specific role in the formation of different social groups. The scale invariance in our model is distinct from previous ‘scale-free networks’ studied using much larger social groups here, the scale invariance is in the relationship between group sizes, rather than in the link degree distribution. We also compare our model with a dominance-based hierarchy and conclude that humans were probably egalitarian in hunter–gatherer-like societies, maintaining an average maximum of four or five social links connecting all members in a largest social network of around 132 people.
Publisher: IEEE
Date: 04-2013
Publisher: Elsevier BV
Date: 04-2022
DOI: 10.1016/J.IJID.2022.01.056
Abstract: To enhance monitoring of high-burden foodborne pathogens, there is opportunity to combine pangenome data with network analysis. Salmonella enterica subspecies Enterica serovar Enteritidis isolates were referred to the New South Wales (NSW) Enteric Reference Laboratory between August 2015 and December 2019 (1033 isolates in total), inclusive of a confirmed outbreak. All isolates underwent whole genome sequencing. Distances between genomes were quantified by in silico multiple-locus variable-number tandem repeat analysis (MLVA) as well as core single nucleotide polymorphisms (SNPs), which informed the construction of undirected networks. Centrality-prevalence spaces were generated from the undirected networks. Components on the undirected SNP network were considered alongside a phylogenetic tree representation. Outbreak isolates were identified as distinct components on the MLVA and SNP networks. The MLVA network-based centrality-prevalence space did not delineate the outbreak, whereas the outbreak was delineated in the SNP network-based centrality-prevalence space. Components on the undirected SNP network showed a high concordance to the SNP clusters based on phylogenetic analysis. Bacterial whole-genome data in network-based analysis can improve the resolution of population analysis. High concordance of network components and SNP clusters is promising for rapid population analyses of foodborne Salmonella spp. owing to the low overhead of network analysis.
Publisher: American Physical Society (APS)
Date: 13-10-2011
Publisher: The Royal Society
Date: 12-2018
DOI: 10.1098/RSOS.181132
Abstract: Despite the frequency with which mixed-species groups are observed in nature, studies of collective behaviour typically focus on single-species groups. Here, we quantify and compare the patterns of interactions between three fish species, threespine sticklebacks ( Gasterosteus aculeatus ), ninespine sticklebacks ( Pungitius pungitius ) and roach ( Rutilus rutilus ) in both single- and mixed-species shoals in the laboratory. Pilot data confirmed that the three species form both single- and mixed-species shoals in the wild. In our laboratory study, we found that single-species groups were more polarized than mixed-species groups, while single-species groups of threespine sticklebacks and roach were more cohesive than mixed shoals of these species. Furthermore, while there was no difference between the inter-in idual distances between threespine and ninespine sticklebacks within mixed-species groups, there was some evidence of segregation by species in mixed groups of threespine sticklebacks and roach. There were differences between treatments in mean pairwise transfer entropy, and in particular we identify species-differences in information use within the mixed-species groups, and, similarly, differences in responses to conspecifics and heterospecifics in mixed-species groups. We speculate that differences in the patterns of interactions between species in mixed-species groups may determine patterns of fission and fusion in such groups.
Publisher: Wiley
Date: 09-2009
DOI: 10.1002/CPLX.20249
Publisher: Elsevier BV
Date: 09-2018
Publisher: Elsevier BV
Date: 09-2021
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: MIT Press - Journals
Date: 07-2009
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: IOP Publishing
Date: 15-11-2010
Publisher: Springer Berlin Heidelberg
Date: 2014
Publisher: Springer Science and Business Media LLC
Date: 16-08-2019
DOI: 10.1038/S41597-019-0137-Z
Abstract: Between the 2011 and 2016 national censuses, the Australian Bureau of Statistics changed its anonymity policy compliance system for the distribution of census data. The new method has resulted in dramatic inconsistencies when comparing low-resolution data to aggregated high-resolution data. Hence, aggregated totals do not match true totals, and the mismatch gets worse as the data resolution gets finer. Here, we address several aspects of this inconsistency with respect to the 2016 usual-residence to place-of-work travel data. We introduce a re-s ling system that rectifies many of the artifacts introduced by the new ABS protocol, ensuring a higher level of consistency across partition sizes. We offer a surrogate high-resolution 2016 commuter dataset that reduces the difference between the aggregated and true commuter totals from ~34% to only ~7%, which is on the order of the discrepancy across partition resolutions in data from earlier years.
Publisher: Oxford University Press (OUP)
Date: 19-04-0019
Abstract: Animal groups are often composed of in iduals that vary according to behavioral, morphological, and internal state parameters. Understanding the importance of such in idual-level heterogeneity to the establishment and maintenance of coherent group responses is of fundamental interest in collective behavior. We examined the influence of hunger on the in idual and collective behavior of groups of shoaling fish, x-ray tetras (Pristella maxillaris). Fish were assigned to one of two nutritional states, satiated or hungry, and then allocated to 5 treatments that represented different ratios of satiated to hungry in iduals (8 hungry, 8 satiated, 4:4 hungry:satiated, 2:6 hungry:satiated, 6:2 hungry:satiated). Our data show that groups with a greater proportion of hungry fish swam faster and exhibited greater nearest neighbor distances. Within groups, however, there was no difference in the swimming speeds of hungry versus well-fed fish, suggesting that group members conform and adapt their swimming speed according to the overall composition of the group. We also found significant differences in mean group transfer entropy, suggesting stronger patterns of information flow in groups comprising all, or a majority of, hungry in iduals. In contrast, we did not observe differences in polarization, a measure of group alignment, within groups across treatments. Taken together these results demonstrate that the nutritional state of animals within social groups impacts both in idual and group behavior, and that members of heterogenous groups can adapt their behavior to facilitate coherent collective motion.
Publisher: IOP Publishing
Date: 02-02-2021
Abstract: On May 28th and 29th, a two day workshop was held virtually, facilitated by the Beyond Center at ASU and Moogsoft Inc. The aim was to bring together leading scientists with an interest in network science and epidemiology to attempt to inform public policy in response to the COVID-19 pandemic. Epidemics are at their core a process that progresses dynamically upon a network, and are a key area of study in network science. In the course of the workshop a wide survey of the state of the subject was conducted. We summarize in this paper a series of perspectives of the subject, and where the authors believe fruitful areas for future research are to be found.
Publisher: Springer Science and Business Media LLC
Date: 19-05-2020
DOI: 10.1038/S41598-020-65208-5
Abstract: Modern urban science views differences in attractiveness of residential suburbs as the main driver of resettlement within a city. In particular, certain suburbs may attract residents due to lower commute costs, and this is believed to lead to compactification of a city, with highly populated central business district and sprawled suburbia. In this paper we assess residential resettlement patterns in Australian capital cities by analyzing the 2011 and 2016 Australian Census data. Rather than explicitly defining a residential attractiveness of each suburb in subjective terms, we introduce and calibrate a model which quantifies the intra-city migration flows in terms of the attractiveness potentials (and their differences), inferring these from the data. We discover that, despite the existence of well-known static agglomeration patterns favouring central districts over the suburbia, the dynamic flows that shape the intra-city migration over the last decade reveal the preference directed away from the central districts with a high density of jobs and population, towards the less populated suburbs on the periphery. Furthermore, we discover that the relocation distance of such resettlement flows plays a vital role, and explains a significant part of the variation in migration flows: the resettlement flow markedly decreases with the relocation distance. Finally, we propose a conjecture that these directional resettlement flows are explained by the cities’ structure, with monocentric cities exhibiting outward flows with much higher reluctance to long-distance relocation. This conjecture is verified across the major Australian capitals: both monocentric (Sydney, Melbourne, Brisbane, Adelaide, Perth, Hobart) and polycentric (Darwin and Canberra).
Publisher: The Royal Society
Date: 2021
Abstract: Urban dynamics in large metropolitan areas result from complex interactions across social, economic and political factors, including population distribution, flows of wealth and infrastructure requirements. We develop a Census-calibrated model of urban dynamics for the Greater Sydney and Melbourne areas for 2011 and 2016, highlighting the evolution of population distributions and the housing market structure in these two cities in terms of their mortgage and rent distributions. We show that there is a tendency to homophily between renters and mortgage holders: renters tend to cluster nearer commercial centres, whereas mortgagors tend to populate the outskirts of these centres. We also identify a critical threshold at which the long-term evolution of these two cities will bifurcate between a ‘sprawling’ and a ‘polycentric’ configuration, showing that both cities lie on the polycentric side of the critical point in the long-run. Importantly, there is a ergence of these centric tendencies between the renters and mortgage holders. The polycentric patterns characterizing the mortgagors are focused around commercial centres, and we show that the emergent housing patterns follow the major transport routes through the cities.
Publisher: IEEE
Date: 2007
Publisher: MDPI
Date: 20-11-2017
DOI: 10.3390/ECEA-4-05027
Publisher: Public Library of Science (PLoS)
Date: 09-07-2020
Publisher: Public Library of Science (PLoS)
Date: 04-03-2014
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Frontiers Media SA
Date: 11-2019
Publisher: Research Square Platform LLC
Date: 10-08-2021
DOI: 10.21203/RS.3.RS-757351/V1
Abstract: As of July 2021, there is a continuing outbreak of the B.1.617.2 (Delta) variant of SARS-CoV-2 in Sydney, Australia. The outbreak is of major concern as the Delta variant is estimated to have twice the reproductive number of previous variants that circulated in Australia in 2020, which is worsened by low levels of acquired immunity in the population. Using a re-calibrated agent-based model, we explored a feasible range of non-pharmaceutical interventions, in terms of both mitigation (case isolation, home quarantine) and suppression (school closures, social distancing). Our nowcasting modelling indicates that the level of social distancing currently attained in Sydney is inadequate for the outbreak control. A counter-factual analysis suggests that if 80% of agents comply with social distancing, then at least a month is needed for the new daily cases to reduce from their peak to below ten. A small reduction in social distancing compliance to 70% lengthens this period to 45 days.
Publisher: Springer Science and Business Media LLC
Date: 22-03-2021
Publisher: American Physical Society (APS)
Date: 15-02-2008
Publisher: Elsevier BV
Date: 03-2009
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11840541_46
Publisher: MDPI AG
Date: 02-10-2017
DOI: 10.3390/G8040042
Abstract: This paper generalises the Hawk-Dove evolutionary game by introducing cost sharing ratios for both players, and applies the generalised Hawk-Dove model to conflict management in projects through investigating the stability of Nash equilibria. A model with clashing interests between a project owner and a contractor is considered to derive their strategy adaptation given the cost sharing ratios. As expected, the pure Nash equilibria are shown to be dominantly stable while the mixed strategy equilibrium is observed to be unstable, across the range of considered cost sharing ratios. In addition, simulations are conducted on the strategy adaptation and stability of the equilibria under noisy and latent conditions. The obtained results can be used by project managers in optimising their strategy in practice.
Publisher: The Royal Society
Date: 02-2023
DOI: 10.1098/RSOS.221164
Abstract: The efficient market hypothesis (EMH), based on rational expectations and market equilibrium, is the dominant perspective for modelling economic markets. However, the most notable critique of the EMH is the inability to model periods of out-of-equilibrium dynamics without significant external news. When such dynamics emerge endogenously, the traditional economic frameworks prove insufficient. This work offers an alternate perspective explaining the endogenous emergence of punctuated out-of-equilibrium dynamics based on bounded rational agents. In a concise market entrance game, we show how boundedly rational strategic reasoning can lead to endogenously emerging crises, exhibiting fat tails in returns. We also show how other common stylized facts, such as clustered volatility, arise due to agent ersity (or lack thereof) and the varying learning updates across the agents. This work explains various stylized facts and crisis emergence in economic markets, in the absence of any external news, based on agent interactions and bounded rational reasoning.
Publisher: The Royal Society
Date: 12-2015
Abstract: We introduce a novel measure, Fisher transfer entropy (FTE), which quantifies a gain in sensitivity to a control parameter of a state transition, in the context of another observable source. The new measure captures both transient and contextual qualities of transfer entropy and the sensitivity characteristics of Fisher information. FTE is exemplified for a ferromagnetic two-dimensional lattice Ising model with Glauber dynamics and is shown to erge at the critical point.
Publisher: Springer Science and Business Media LLC
Date: 04-2014
Publisher: Informa UK Limited
Date: 2009
DOI: 10.2976/1.3233933
Publisher: The Royal Society
Date: 19-10-2018
Abstract: Computation is a useful concept far beyond the disciplinary boundaries of computer science. Perhaps the most important class of natural computers can be found in biological systems that perform computation on multiple levels. From molecular and cellular information processing networks to ecologies, economies and brains, life computes. Despite ubiquitous agreement on this fact going back as far as von Neumann automata and McCulloch–Pitts neural nets, we so far lack principles to understand rigorously how computation is done in living, or active, matter. What is the ultimate nature of natural computation that has evolved, and how can we use these principles to engineer intelligent technologies and biological tissues?
Publisher: MDPI AG
Date: 27-04-2017
DOI: 10.3390/E19050194
Publisher: Springer Berlin Heidelberg
Date: 2002
Publisher: American Association for the Advancement of Science (AAAS)
Date: 07-12-2018
Abstract: Simulations associate urbanization with earlier peaks, higher peak prevalence, and shifting bimodality of pandemics in Australia.
Publisher: Springer Berlin Heidelberg
Date: 2003
Publisher: Springer Science and Business Media LLC
Date: 27-01-2010
Publisher: IEEE
Date: 09-2006
Publisher: Springer Science and Business Media LLC
Date: 24-12-2008
Publisher: Informa UK Limited
Date: 08-2012
Publisher: Springer Berlin Heidelberg
Date: 2014
Publisher: Springer London
Date: 24-11-2007
Publisher: Springer Berlin Heidelberg
Date: 2004
Publisher: Springer Science and Business Media LLC
Date: 08-10-2022
DOI: 10.1186/S12961-022-00902-6
Abstract: 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 in iduals 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.
Publisher: Wiley
Date: 16-07-2019
DOI: 10.1002/EAP.1947
Abstract: Telemetry is a key, widely used tool to understand marine megafauna distribution, habitat use, behavior, and physiology however, a critical question remains: "How many animals should be tracked to acquire meaningful data sets?" This question has wide-ranging implications including considerations of statistical power, animal ethics, logistics, and cost. While power analyses can inform s le sizes needed for statistical significance, they require some initial data inputs that are often unavailable. To inform the planning of telemetry and biologging studies of marine megafauna where few or no data are available or where resources are limited, we reviewed the types of information that have been obtained in previously published studies using different s le sizes. We considered s le sizes from one to >100 in iduals and synthesized empirical findings, detailing the information that can be gathered with increasing s le sizes. We complement this review with simulations, using real data, to show the impact of s le size when trying to address various research questions in movement ecology of marine megafauna. We also highlight the value of collaborative, synthetic studies to enhance s le sizes and broaden the range, scale, and scope of questions that can be answered.
Publisher: Springer Science and Business Media LLC
Date: 08-03-2018
Publisher: IEEE
Date: 10-2008
DOI: 10.1109/SASO.2008.60
Publisher: MDPI AG
Date: 09-10-2014
DOI: 10.3390/E16105232
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11554028_25
Publisher: IEEE
Date: 07-2007
DOI: 10.1109/SASO.2007.13
Publisher: IEEE
Date: 09-2009
DOI: 10.1109/SASO.2009.38
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: MDPI AG
Date: 26-05-2021
DOI: 10.3390/E23060669
Abstract: Bounded rationality is an important consideration stemming from the fact that agents often have limits on their processing abilities, making the assumption of perfect rationality inapplicable to many real tasks. We propose an information-theoretic approach to the inference of agent decisions under Smithian competition. The model explicitly captures the boundedness of agents (limited in their information-processing capacity) as the cost of information acquisition for expanding their prior beliefs. The expansion is measured as the Kullblack–Leibler ergence between posterior decisions and prior beliefs. When information acquisition is free, the homo economicus agent is recovered, while in cases when information acquisition becomes costly, agents instead revert to their prior beliefs. The maximum entropy principle is used to infer least biased decisions based upon the notion of Smithian competition formalised within the Quantal Response Statistical Equilibrium framework. The incorporation of prior beliefs into such a framework allowed us to systematically explore the effects of prior beliefs on decision-making in the presence of market feedback, as well as importantly adding a temporal interpretation to the framework. We verified the proposed model using Australian housing market data, showing how the incorporation of prior knowledge alters the resulting agent decisions. Specifically, it allowed for the separation of past beliefs and utility maximisation behaviour of the agent as well as the analysis into the evolution of agent beliefs.
Publisher: Cold Spring Harbor Laboratory
Date: 22-04-2020
DOI: 10.1101/2020.04.19.048751
Abstract: Community transmission of the new coronavirus SARS-CoV-2 is a major public health concern that remains difficult to assess. We present a genomic survey of SARS-CoV-2 from a during the first 10 weeks of COVID-19 activity in New South Wales, Australia. Transmission events were monitored prospectively during the critical period of implementation of national control measures. SARS-CoV-2 genomes were sequenced from 209 patients diagnosed with COVID-19 infection between January and March 2020. Only a quarter of cases appeared to be locally acquired and genomic-based estimates of local transmission rates were concordant with predictions from a computational agent-based model. This convergent assessment indicates that genome sequencing provides key information to inform public health action and has improved our understanding of the COVID-19 evolution from outbreak to epidemic.
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: The Royal Society
Date: 04-2020
Abstract: When new, highly infectious strains of influenza emerge, global pandemics can occur before an effective vaccine is developed. Without a strain-specific vaccine, pandemics can only be mitigated by employing combinations of low-efficacy pre-pandemic vaccines and reactive response measures that are carried out as the pandemic unfolds. Unfortunately, the application of reactive interventions can lead to unintended consequences that may exacerbate unpredictable spreading dynamics and cause more drawn-out epidemics. Here, we employ a detailed model of pandemic influenza in Australia to simulate the combination of pre-pandemic vaccination and reactive antiviral prophylaxis. This study focuses on population-level coupling effects between the respective methods, and the associated spatio-temporal fluctuations in pandemic dynamics produced by reactive strategies. Our results show that combining strategies can produce either mutual improvement of performance or interference that reduces the effectiveness of each strategy when they are used together. We demonstrate that these coupling effects between intervention strategies are extremely sensitive to delay times, compliance rates and the type of contact targeting used to administer prophylaxis.
Publisher: Elsevier BV
Date: 07-2013
Publisher: MDPI AG
Date: 04-08-2017
DOI: 10.3390/E19080403
Publisher: Public Library of Science (PLoS)
Date: 30-10-2020
DOI: 10.1371/JOURNAL.PCBI.1008401
Abstract: Modelling the emergence of foodborne pathogens is a crucial step in the prediction and prevention of disease outbreaks. Unfortunately, the mechanisms that drive the evolution of such continuously adapting pathogens remain poorly understood. Here, we combine molecular genotyping with network science and Bayesian inference to infer directed genotype networks—and trace the emergence and evolutionary paths—of Salmonella Typhimurium (STM) from nine years of Australian disease surveillance data. We construct networks where nodes represent STM strains and directed edges represent evolutionary steps, presenting evidence that the structural (i.e., network-based) features are relevant to understanding the functional (i.e., fitness-based) progression of co-evolving STM strains. This is argued by showing that outbreak severity, i.e., prevalence, correlates to: (i) the network path length to the most prevalent node ( r = −0.613, N = 690) and (ii) the network connected-component size ( r = 0.739). Moreover, we uncover distinct exploration-exploitation pathways in the genetic space of STM, including a strong evolutionary drive through a transition region. This is examined via the 6,897 distinct evolutionary paths in the directed network, where we observe a dominant 66% of these paths decrease in network centrality, whilst increasing in prevalence. Furthermore, 72.4% of all paths originate in the transition region, with 64% of those following the dominant direction. Further, we find that the length of an evolutionary path strongly correlates to its increase in prevalence ( r = 0.497). Combined, these findings indicate that longer evolutionary paths result in genetically rare and virulent strains, which mostly evolve from a single transition point. Our results not only validate our widely-applicable approach for inferring directed genotype networks from data, but also provide a unique insight into the elusive functional and structural drivers of STM bacteria.
Publisher: Elsevier BV
Date: 09-2020
Publisher: The Royal Society
Date: 07-2022
DOI: 10.1098/RSOS.211919
Abstract: Computational models of infectious disease can be broadly categorized into two types: in idual-based (agent-based) or compartmental models. While there are clear conceptual distinctions between these methodologies, a fair comparison of the approaches is difficult to achieve. Here, we carry out such a comparison by building a set of compartmental metapopulation models from an agent-based representation of a real population. By adjusting the compartmental model to approximately match the dynamics of the agent-based model, we identify two key qualitative properties of the in idual-based dynamics which are lost upon aggregation into metapopulations. These are (i) the local depletion of susceptibility to infection and (ii) decoupling of different regional groups due to correlation between commuting behaviours and contact rates. The first of these effects is a general consequence of aggregating small, closely connected groups (i.e. families) into larger homogeneous metapopulations. The second can be interpreted as a consequence of aggregating two distinct types of in iduals: school children, who travel short distances but have many potentially infectious contacts, and adults, who travel further but tend to have fewer contacts capable of transmitting infection. Our results could be generalized to other types of correlations between the characteristics of in iduals and the behaviours that distinguish them.
Publisher: American Physical Society (APS)
Date: 06-08-2018
Publisher: Springer Berlin Heidelberg
Date: 2003
Publisher: Springer Berlin Heidelberg
Date: 1999
Publisher: MIT Press - Journals
Date: 10-2011
DOI: 10.1162/ARTL_E_00037
Publisher: MDPI AG
Date: 02-2013
DOI: 10.3390/E15020524
Publisher: Springer Berlin Heidelberg
Date: 1997
Publisher: Frontiers Media SA
Date: 24-02-2022
DOI: 10.3389/FPUBH.2022.823043
Abstract: An outbreak of the Delta (B.1.617.2) variant of SARS-CoV-2 that began around mid-June 2021 in Sydney, Australia, quickly developed into a nation-wide epidemic. The ongoing epidemic is of major concern as the Delta variant is more infectious than previous variants that circulated in Australia in 2020. Using a re-calibrated agent-based model, we explored a feasible range of non-pharmaceutical interventions, including case isolation, home quarantine, school closures, and stay-at-home restrictions (i.e., “social distancing.”) Our modelling indicated that the levels of reduced interactions in workplaces and across communities attained in Sydney and other parts of the nation were inadequate for controlling the outbreak. A counter-factual analysis suggested that if 70% of the population followed tight stay-at-home restrictions, then at least 45 days would have been needed for new daily cases to fall from their peak to below ten per day. Our model predicted that, under a progressive vaccination rollout, if 40–50% of the Australian population follow stay-at-home restrictions, the incidence will peak by mid-October 2021: the peak in incidence across the nation was indeed observed in mid-October. We also quantified an expected burden on the healthcare system and potential fatalities across Australia.
Publisher: The Royal Society
Date: 10-2018
DOI: 10.1098/RSOS.180863
Abstract: Urban transformations within large and growing metropolitan areas often generate critical dynamics affecting social interactions, transport connectivity and income flow distribution. We develop a statistical–mechanical model of urban transformations, exemplified for Greater Sydney, and derive a thermodynamic description highlighting critical regimes. We consider urban dynamics at two time scales: fast dynamics for the distribution of population and income, modelled via the maximum entropy principle, and slower dynamics evolving the urban structure under spatially distributed competition. We identify phase transitions between dispersed and polycentric phases, induced by varying the social disposition —a factor balancing the suburbs’ attractiveness—in contrast with the travel impedance . Using the Fisher information, we identify critical thresholds and quantify the thermodynamic cost of urban transformation, as the minimal work required to vary the underlying parameter. Finally, we introduce the notion of thermodynamic efficiency of urban transformation , as the ratio of the order gained during a change to the amount of required work, showing that this measure is maximized at criticality.
No related grants have been discovered for Mikhail Prokopenko.