Spatial and temporal clusters of Barmah Forest virus disease in Queensland, Australia

Objective To identify the spatial and temporal clusters of Barmah Forest virus (BFV) disease in Queensland in Australia, using geographical information systems (GIS) and spatial scan statistic (SaTScan). Methods We obtained BFV disease cases, population and statistical local areas boundary data from Queensland Health and Australian Bureau of Statistics respectively during 1992-2008 for Queensland. A retrospective Poisson-based analysis using SaTScan software and method was conducted in order to identify both purely spatial and space-time BFV disease high-rate clusters. A spatial cluster size of a proportion of the population and a 200km circle radius and varying time windows from 1 month to 12 months were chosen (for the space-time analysis). Results The spatial scan statistic detected a most likely significant purely spatial cluster (including 23 SLAs) and a most likely significant space-time cluster (including 24 SLAs) in approximately the same location. Significant secondary clusters were also identified from both the analyses in several locations. Conclusions This study provides evidence of the existence of statistically significant BFV disease clusters in Queensland, Australia. The study also demonstrated the relevance and applicability of SaTScan in analysing on-going surveillance data to identify clusters to facilitate the development of effective BFV disease prevention and control strategies in Queensland, Australia.

Phylogeography of Echinocladius martini Cranston (Diptera: Chironomidae) in closed forest streams of eastern Australia

The eastern Australian rainforests have experienced several cycles of range contraction and expansion since the late Miocene that are closely correlated with global glaciation events. Together with ongoing aridification of the continent, this has resulted in current distributions of native closed forest that are highly fragmented along the east coast. Several closed forest endemic taxa exhibit patterns of population genetic structure that are congruent with historical isolation of populations in discrete refugia and reflect evolutionary histories dramatically affected by vicariance. Currently, limited data are available regarding the impact of these past climatic fluctuations on freshwater invertebrate taxa. The non-biting midge species Echinocladius martini Cranston is distributed along the east coast and inhabits predominantly montane streams in closed forest habitat. Phylogeographic structure in E. martini was resolved here at a continental scale by incorporating data from a previous pilot study and expanding the sampling design to encompass populations in the Wet Tropics of north-eastern Queensland, south-east Queensland, New South Wales and Victoria. Patterns of phylogeographic structure revealed several deeply divergent mitochondrial lineages from central and south-eastern Australia that were previously unrecognised and were geographically endemic to closed forest refugia. Estimated divergence times were congruent with late Miocene onset of rainforest contractions across the east coast of Australia. This suggested that dispersal and gene flow among E. martini populations isolated in refugia has been highly restricted historically. Moreover, these data imply, in contrast to existing preconceptions about freshwater invertebrates, that this taxon may be acutely susceptible to habitat fragmentation.

Socio-environmental variability and Barmah Forest virus disease transmission : a review of epidemiological evidence and future prospects

Barmah Forest Virus (BFV) disease is the most rapidly emerging mosquito-borne disease in Australia. BFV transmission depends on factors such as climate, virus, vector and the human population. However, the impact of climatic and social factors on BFV remains to be determined. This paper provided an overview of current research and discusses the future research directions on the BFV transmission. These research findings could be regarded as an impetus towards BFV prevention and control strategies.

Spatio-temporal patterns of Barmah Forest virus disease in Queensland, Australia

Background Barmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis. Methods/Principal Findings We calculated the incidence rates and standardised incidence rates of BFV disease. Moran's I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (χ2 = 7587, df = 7327,p<0.01). There was a significant positive spatial autocorrelation of BFV incidence for all four periods, with the Moran's I statistic ranging from 0.1506 to 0.2901 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. Conclusions/Significance This is the first study to examine spatial and temporal variation in the incidence rates of BFV disease across Queensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland.

Small and medium enterprises sourcing software as a service – a dynamic perspective on IS capabilities

Software as a Service (SaaS) is a promising approach for Small and Medium Enterprises (SMEs) firms, in particular those that are focused on growing fast and leveraging new technology, due to the potential benefits arising from its inherent scalability, reduced total cost of ownership and the ease of access to global innovations. This paper proposes a dynamic perspective on IS capabilities to understand and explain SMEs sourcing and levering SaaS. The model is derived from combining the IS capabilities of Feeny and Willcocks (1998) and the dynamic capabilities of Teece (2007) and contextualizing it for SMEs and SaaS. We conclude that SMEs sourcing and leveraging SaaS require leadership, business systems thinking and informed buying for sensing and seizing SaaS opportunities and require leadership and vendor development for transforming in terms of aligning and realigning specific tangible and intangible assets.

Dynamic weaving of aspects for business process management systems

Reducing complexity in Information Systems is a main concern in both research and industry. One strategy for reducing complexity is separation of concerns. This strategy advocates separating various concerns, like security and privacy, from the main concern. It results in less complex, easily maintainable, and more reusable Information Systems. Separation of concerns is addressed through the Aspect Oriented paradigm. This paradigm has been well researched and implemented in programming, where languages such as AspectJ have been developed. However, the rsearch on aspect orientation for Business Process Management is still at its beginning. While some efforts have been made proposing Aspect Oriented Business Process Modelling, it has not yet been investigated how to enact such process models in a Workflow Management System. In this paper, we define a set of requirements that specifies the execution of aspect oriented business process models. We create a Coloured Petri Net specification for the semantics of so-called Aspect Service that fulfils these requirements. Such a service extends the capability of a Workflow Management System with support for execution of aspect oriented business process models. The design specification of the Aspect Service is also inspected through state space analysis.

Computational fluid dynamic analysis of intracranial aneurysm bleb Formation (NS26P)

Purpose: The management of unruptured aneurysms remains controversial as treatment infers potential significant risk to the currently well patient. The decision to treat is based upon aneurysm location, size and abnormal morphology (e.g. bleb formation). A method to predict bleb formation would thus help stratify patient treatment. Our study aims to investigate possible associations between intra-aneurysmal flow dynamics and bleb formation within intracranial aneurysms. Competing theories on aetiology appear in the literature. Our purpose is to further clarify this issue. Methodology: We recruited data from 3D rotational angiograms (3DRA) of 30 patients with cerebral aneurysms and bleb formation. Models representing aneurysms pre-bleb formation were reconstructed by digitally removing the bleb, then computational fluid dynamics simulations were run on both pre and post bleb models. Pulsatile flow conditions and standard boundary conditions were imposed. Results: Aneurysmal flow structure, impingement regions, wall shear stress magnitude and gradients were produced for all models. Correlation of these parameters with bleb formation was sought. Certain CFD parameters show significant inter patient variability, making statistically significant correlation difficult on the partial data subset obtained currently. Conclusion: CFD models are readily producible from 3DRA data. Preliminary results indicate bleb formation appears to be related to regions of high wall shear stress and direct impingement regions of the aneurysm wall.

Integrated health prediction of bridge systems using dynamic object oriented Bayesian networks (DOOBNS)

The serviceability and safety of bridges are crucial to people’s daily lives and to the national economy. Every effort should be taken to make sure that bridges function safely and properly as any damage or fault during the service life can lead to transport paralysis, catastrophic loss of property or even casualties. Nonetheless, aggressive environmental conditions, ever-increasing and changing traffic loads and aging can all contribute to bridge deterioration. With often constrained budget, it is of significance to identify bridges and bridge elements that should be given higher priority for maintenance, rehabilitation or replacement, and to select optimal strategy. Bridge health prediction is an essential underpinning science to bridge maintenance optimization, since the effectiveness of optimal maintenance decision is largely dependent on the forecasting accuracy of bridge health performance. The current approaches for bridge health prediction can be categorised into two groups: condition ratings based and structural reliability based. A comprehensive literature review has revealed the following limitations of the current modelling approaches: (1) it is not evident in literature to date that any integrated approaches exist for modelling both serviceability and safety aspects so that both performance criteria can be evaluated coherently; (2) complex system modelling approaches have not been successfully applied to bridge deterioration modelling though a bridge is a complex system composed of many inter-related bridge elements; (3) multiple bridge deterioration factors, such as deterioration dependencies among different bridge elements, observed information, maintenance actions and environmental effects have not been considered jointly; (4) the existing approaches are lacking in Bayesian updating ability to incorporate a variety of event information; (5) the assumption of series and/or parallel relationship for bridge level reliability is always held in all structural reliability estimation of bridge systems. To address the deficiencies listed above, this research proposes three novel models based on the Dynamic Object Oriented Bayesian Networks (DOOBNs) approach. Model I aims to address bridge deterioration in serviceability using condition ratings as the health index. The bridge deterioration is represented in a hierarchical relationship, in accordance with the physical structure, so that the contribution of each bridge element to bridge deterioration can be tracked. A discrete-time Markov process is employed to model deterioration of bridge elements over time. In Model II, bridge deterioration in terms of safety is addressed. The structural reliability of bridge systems is estimated from bridge elements to the entire bridge. By means of conditional probability tables (CPTs), not only series-parallel relationship but also complex probabilistic relationship in bridge systems can be effectively modelled. The structural reliability of each bridge element is evaluated from its limit state functions, considering the probability distributions of resistance and applied load. Both Models I and II are designed in three steps: modelling consideration, DOOBN development and parameters estimation. Model III integrates Models I and II to address bridge health performance in both serviceability and safety aspects jointly. The modelling of bridge ratings is modified so that every basic modelling unit denotes one physical bridge element. According to the specific materials used, the integration of condition ratings and structural reliability is implemented through critical failure modes. Three case studies have been conducted to validate the proposed models, respectively. Carefully selected data and knowledge from bridge experts, the National Bridge Inventory (NBI) and existing literature were utilised for model validation. In addition, event information was generated using simulation to demonstrate the Bayesian updating ability of the proposed models. The prediction results of condition ratings and structural reliability were presented and interpreted for basic bridge elements and the whole bridge system. The results obtained from Model II were compared with the ones obtained from traditional structural reliability methods. Overall, the prediction results demonstrate the feasibility of the proposed modelling approach for bridge health prediction and underpin the assertion that the three models can be used separately or integrated and are more effective than the current bridge deterioration modelling approaches. The primary contribution of this work is to enhance the knowledge in the field of bridge health prediction, where more comprehensive health performance in both serviceability and safety aspects are addressed jointly. The proposed models, characterised by probabilistic representation of bridge deterioration in hierarchical ways, demonstrated the effectiveness and pledge of DOOBNs approach to bridge health management. Additionally, the proposed models have significant potential for bridge maintenance optimization. Working together with advanced monitoring and inspection techniques, and a comprehensive bridge inventory, the proposed models can be used by bridge practitioners to achieve increased serviceability and safety as well as maintenance cost effectiveness.

One-year delayed effect of fog on malaria transmission : a time-series analysis in the rain forest area of Mengla County, south-west China

Background: Malaria is a major public health burden in the tropics with the potential to significantly increase in response to climate change. Analyses of data from the recent past can elucidate how short-term variations in weather factors affect malaria transmission. This study explored the impact of climate variability on the transmission of malaria in the tropical rain forest area of Mengla County, south-west China. Methods: Ecological time-series analysis was performed on data collected between 1971 and 1999. Auto-regressive integrated moving average (ARIMA) models were used to evaluate the relationship between weather factors and malaria incidence. Results: At the time scale of months, the predictors for malaria incidence included: minimum temperature, maximum temperature, and fog day frequency. The effect of minimum temperature on malaria incidence was greater in the cool months than in the hot months. The fog day frequency in October had a positive effect on malaria incidence in May of the following year. At the time scale of years, the annual fog day frequency was the only weather predictor of the annual incidence of malaria. Conclusion: Fog day frequency was for the first time found to be a predictor of malaria incidence in a rain forest area. The one-year delayed effect of fog on malaria transmission may involve providing water input and maintaining aquatic breeding sites for mosquitoes in vulnerable times when there is little rainfall in the 6-month dry seasons. These findings should be considered in the prediction of future patterns of malaria for similar tropical rain forest areas worldwide.

Spatial and temporal analysis of Barmah Forest virus disease in Queensland, Australia

Barmah Forest virus (BFV) disease is one of the most widespread mosquito-borne diseases in Australia. The number of outbreaks and the incidence rate of BFV in Australia have attracted growing concerns about the spatio-temporal complexity and underlying risk factors of BFV disease. A large number of notifications has been recorded continuously in Queensland since 1992. Yet, little is known about the spatial and temporal characteristics of the disease. I aim to use notification data to better understand the effects of climatic, demographic, socio-economic and ecological risk factors on the spatial epidemiology of BFV disease transmission, develop predictive risk models and forecast future disease risks under climate change scenarios. Computerised data files of daily notifications of BFV disease and climatic variables in Queensland during 1992-2008 were obtained from Queensland Health and Australian Bureau of Meteorology, respectively. Projections on climate data for years 2025, 2050 and 2100 were obtained from Council of Scientific Industrial Research Organisation. Data on socio-economic, demographic and ecological factors were also obtained from relevant government departments as follows: 1) socio-economic and demographic data from Australian Bureau of Statistics; 2) wetlands data from Department of Environment and Resource Management and 3) tidal readings from Queensland Department of Transport and Main roads. Disease notifications were geocoded and spatial and temporal patterns of disease were investigated using geostatistics. Visualisation of BFV disease incidence rates through mapping reveals the presence of substantial spatio-temporal variation at statistical local areas (SLA) over time. Results reveal high incidence rates of BFV disease along coastal areas compared to the whole area of Queensland. A Mantel-Haenszel Chi-square analysis for trend reveals a statistically significant relationship between BFV disease incidence rates and age groups (ƒÓ2 = 7587, p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. A cluster analysis was used to detect the hot spots/clusters of BFV disease at a SLA level. Most likely spatial and space-time clusters are detected at the same locations across coastal Queensland (p<0.05). The study demonstrates heterogeneity of disease risk at a SLA level and reveals the spatial and temporal clustering of BFV disease in Queensland. Discriminant analysis was employed to establish a link between wetland classes, climate zones and BFV disease. This is because the importance of wetlands in the transmission of BFV disease remains unclear. The multivariable discriminant modelling analyses demonstrate that wetland types of saline 1, riverine and saline tidal influence were the most significant risk factors for BFV disease in all climate and buffer zones, while lacustrine, palustrine, estuarine and saline 2 and saline 3 wetlands were less important. The model accuracies were 76%, 98% and 100% for BFV risk in subtropical, tropical and temperate climate zones, respectively. This study demonstrates that BFV disease risk varied with wetland class and climate zone. The study suggests that wetlands may act as potential breeding habitats for BFV vectors. Multivariable spatial regression models were applied to assess the impact of spatial climatic, socio-economic and tidal factors on the BFV disease in Queensland. Spatial regression models were developed to account for spatial effects. Spatial regression models generated superior estimates over a traditional regression model. In the spatial regression models, BFV disease incidence shows an inverse relationship with minimum temperature, low tide and distance to coast, and positive relationship with rainfall in coastal areas whereas in whole Queensland the disease shows an inverse relationship with minimum temperature and high tide and positive relationship with rainfall. This study determines the most significant spatial risk factors for BFV disease across Queensland. Empirical models were developed to forecast the future risk of BFV disease outbreaks in coastal Queensland using existing climatic, socio-economic and tidal conditions under climate change scenarios. Logistic regression models were developed using BFV disease outbreak data for the existing period (2000-2008). The most parsimonious model had high sensitivity, specificity and accuracy and this model was used to estimate and forecast BFV disease outbreaks for years 2025, 2050 and 2100 under climate change scenarios for Australia. Important contributions arising from this research are that: (i) it is innovative to identify high-risk coastal areas by creating buffers based on grid-centroid and the use of fine-grained spatial units, i.e., mesh blocks; (ii) a spatial regression method was used to account for spatial dependence and heterogeneity of data in the study area; (iii) it determined a range of potential spatial risk factors for BFV disease; and (iv) it predicted the future risk of BFV disease outbreaks under climate change scenarios in Queensland, Australia. In conclusion, the thesis demonstrates that the distribution of BFV disease exhibits a distinct spatial and temporal variation. Such variation is influenced by a range of spatial risk factors including climatic, demographic, socio-economic, ecological and tidal variables. The thesis demonstrates that spatial regression method can be applied to better understand the transmission dynamics of BFV disease and its risk factors. The research findings show that disease notification data can be integrated with multi-factorial risk factor data to develop build-up models and forecast future potential disease risks under climate change scenarios. This thesis may have implications in BFV disease control and prevention programs in Queensland.

Using information communication technologies to develop dynamic curriculum frameworks for diverse cohorts: a case study from event management

This article investigates the role of information communication technologies (ICTs) in establishing a well-aligned, authentic learning environment for a diverse cohort of non-cognate and cognate students studying event management in a higher education context. Based on a case study which examined the way ICTs assisted in accommodating diverse learning needs, styles and stages in an event management subject offered in the Creative Industries Faculty at Queensland University of Technology in Brisbane, Australia, the article uses an action research approach to generate grounded, empirical data on the effectiveness of the dynamic, individualised curriculum frameworks that the use of ICTs makes possible. The study provides insights into the way non-cognate and cognate students respond to different learning tools. It finds that whilst non-cognate and cognate students do respond to learning tools differently, due to a differing degree of emphasis on technical, task or theoretical competencies, the use of ICTs allows all students to improve their performance by providing multiple points of entry into the content. In this respect, whilst the article focuses on the way ICTs can be used to develop an authentic, well-aligned curriculum model that meets the needs of event management students in a higher education context, with findings relevant for event educators in Business, Hospitality, Tourism and Creative Industries, the strategies outlined may also be useful for educators in other fields who are faced with similar challenges when designing and developing curriculum for diverse cohorts.

Effects of various power system stabilizers on improving power system dynamic performance

To ensure the small-signal stability of a power system, power system stabilizers (PSSs) are extensively applied for damping low frequency power oscillations through modulating the excitation supplied to synchronous machines, and increasing interest has been focused on developing different PSS schemes to tackle the threat of damping oscillations to power system stability. This paper examines four different PSS models and investigates their performances on damping power system dynamics using both small-signal eigenvalue analysis and large-signal dynamic simulations. The four kinds of PSSs examined include the Conventional PSS (CPSS), Single Neuron based PSS (SNPSS), Adaptive PSS (APSS) and Multi-band PSS (MBPSS). A steep descent parameter optimization algorithm is employed to seek the optimal PSS design parameters. To evaluate the effects of these PSSs on improving power system dynamic behaviors, case studies are carried out on an 8-unit 24-bus power system through both small-signal eigenvalue analysis and large-signal time-domain simulations.

Dynamic equivalent state estimation for multi-area power systems with synchronized phasor measurement units

This paper proposes a new approach for state estimation of angles and frequencies of equivalent areas in large power systems with synchronized phasor measurement units. Defining coherent generators and their correspondent areas, generators are aggregated and system reduction is performed in each area of inter-connected power systems. The structure of the reduced system is obtained based on the characteristics of the reduced linear model and measurement data to form the non-linear model of the reduced system. Then a Kalman estimator is designed for the reduced system to provide an equivalent dynamic system state estimation using the synchronized phasor measurement data. The method is simulated on two test systems to evaluate the feasibility of the proposed method.