Granular Description of Data Structures: A Two-Phase Design.

The study is concerned with a description of large numeric data with the aid of building a limited collection of representative information granules with the objective of capturing the structure of the original data. The proposed development scheme consists of two steps. First, a clustering algorithm characterized by high flexibility of coping with the diverse geometry of data structure and efficient computational overhead is invoked. At the second step, a clustering algorithm applied to the clusters already formed during the first phase, yielding a collection of numeric prototypes is involved and the numeric prototypes produced there are then generalized into their granular prototypes. The quality of granular prototypes is quantified while their build-up is supported by the mechanisms of granular computing such as the principle of justifiable granularity. In this paper, the clustering algorithms of DBSCAN and fuzzy C -means were used in successive phases of the processed approach. The experimental studies concerning synthetic data and publicly available data are covered and the performance of the developed approach is assessed along with a comparative analysis.

Rule-Based Modeling With DBSCAN-Based Information Granules.

Rule-based models are applicable to model the behavior of complex and nonlinear systems. Due to limited experience and randomness involving constructing information granules, an insufficient credible rules division could reduce the model's accuracy. This paper proposes a new rule-based modeling approach, which utilizes density-based spatial clustering of applications with noise (DBSCAN)-based information granules to construct the rules. First, bear in mind the advantages of density-based clustering, DBSCAN is proposed to generate data structures. Based on these data structures, two rule-based models are constructed: 1) models using DBSCAN clusters to construct granules and rules directly and 2) models generating subgranules in each DBSCAN cluster for rule formation. Experiments involving these two models are completed, and obtained results are compared with those generated with a traditional model involving fuzzy C -means-based granules. Numerical results show that the rule-based model, which builds rules from subgranules of DBSCAN structures, performs the best in analyzing system behaviors.

Indigenous populations health protection: a Canadian perspective.

The disproportionate effects of the 2009 H1N1 pandemic on many Canadian Aboriginal communities have drawn attention to the vulnerability of these communities in terms of health outcomes in the face of emerging and reemerging infectious diseases. Exploring the particular challenges facing these communities is essential to improving public health planning. In alignment with the objectives of the Pandemic Influenza Outbreak Research Modelling (Pan-InfORM) team, a Canadian public health workshop was held at the Centre for Disease Modelling (CDM) to: (i) evaluate post-pandemic research findings; (ii) identify existing gaps in knowledge that have yet to be addressed through ongoing research and collaborative activities; and (iii) build upon existing partnerships within the research community to forge new collaborative links with Aboriginal health organizations. The workshop achieved its objectives in identifying main research findings and emerging information post pandemic, and highlighting key challenges that pose significant impediments to the health protection and promotion of Canadian Aboriginal populations. The health challenges faced by Canadian indigenous populations are unique and complex, and can only be addressed through active engagement with affected communities. The academic research community will need to develop a new interdisciplinary framework, building upon concepts from 'Communities of Practice', to ensure that the research priorities are identified and targeted, and the outcomes are translated into the context of community health to improve policy and practice.

Pandemic influenza: Modelling and public health perspectives.

We describe the application of mathematical models in the study of disease epidemics with particular focus on pandemic influenza. We outline the general mathematical approach and the complications arising from attempts to apply it for disease outbreak management in a real public health context.

Canada in the face of the 2009 H1N1 pandemic.

BACKGROUND: Initial public health responses to the 2009 influenza H1N1 pandemic were based on difficult decisions in the face of substantial uncertainty. Policy effectiveness depends critically on such decisions, and future planning for maximum protection of community health requires understanding of the impact of public health responses in observed scenarios. OBJECTIVES: In alignment with the objectives of the Pandemic Influenza Outbreak Research Modelling Team (Pan-InfORM) and the Centre for Disease Modelling (CDM), a focused workshop was organized to: (i) evaluate Canada's response to the spring and autumn waves of the novel H1N1 pandemic; (ii) learn lessons from public health responses, and identify challenges that await public health planners and decision-makers; and (iii) understand how best to integrate resources to overcome these challenges. MAIN OUTCOME MEASURES: We report on key presentations and discussions that took place to achieve the objectives of the workshop. CONCLUSIONS: Future emerging infectious diseases are likely to bring far greater challenges than those imposed by the 2009 H1N1 pandemic. Canada must address these challenges and enhance its capacity for emergency responses by integrating modelling, surveillance, planning, and decision-making.

Managing public health crises: the role of models in pandemic preparedness.

BACKGROUND: Given the enormity of challenges involved in pandemic preparedness, design and implementation of effective and cost-effective public health policies is a major task that requires an integrated approach through engagement of scientific, administrative, and political communities across disciplines. There is ample evidence to suggest that modeling may be a viable approach to accomplish this task. METHODS: To demonstrate the importance of synergism between modelers, public health experts, and policymakers, the University of Winnipeg organized an interdisciplinary workshop on the role of models in pandemic preparedness in September 2008. The workshop provided an excellent opportunity to present outcomes of recent scientific investigations that thoroughly evaluate the merits of preventive, therapeutic, and social distancing mechanisms, where community structures, priority groups, healthcare providers, and responders to emergency situations are given specific consideration. RESULTS: This interactive workshop was clearly successful in strengthening ties between various disciplines and creating venues for modelers to effectively communicate with policymakers. The importance of modeling in pandemic planning was highlighted, and key parameters that affect policy decision-making were identified. Core assumptions and important activities in Canadian pandemic plans at the provincial and national levels were also discussed. CONCLUSIONS: There will be little time for thoughtful and rapid reflection once an influenza pandemic strikes, and therefore preparedness is an unavoidable priority. Modeling and simulations are key resources in pandemic planning to map out interdependencies and support complex decision-making. Models are most effective in formulating strategies for managing public health crises when there are synergies between modelers, planners, and policymakers.

A pattern recognition application framework for biomedical datasets.

Scopira facilitates the development of high-performance applications by providing many useful subsystems, flexible and efficient data models, low-level tools such as memory management and serialization, GUI constructs, high-level visualization modules, and the ability to implement parallel algorithms with MPI. Scopira plug-in extensions have been developed to enable Matlab scripts to easily call any Scopira module, thus facilitating the migration of prototypes to highly efficient C++ applications. Scopira is continuously under development and future capabilities will include the ability to develop distributed programs using agents, applicable to grid-computing data mining applications. Scopira has proven to be a successful programming framework for implementing high-performance biomedical data analysis applications. It is based on C++, an efficient object-oriented language, and the source code is available as an open-source project for other researchers to use and adapt to their own research endeavours. Scopira has been compiled to work on Linux and Windows XP operating systems with a port to the Mac OS under development. Scopira, EvIdent and RDP are freely available for download from www.scopira.org.