The aim of this article is to present a methodological approach for problems encountered in structural analysis. This approach is based upon the pretopological concepts of pseudoclosure and minimal closed subsets. The advantage of this approach is that it provides a framework which is general enough to model and formulate different types of connections that exist between the elements of a population. In addition, it has enabled us to develop a new structural analysis algorithm. An explanation of the definitions and properties of the pretopological concepts applied in this work is first shown and illustrated in sample settings. The structural analysis algorithm is then described and the results obtained in an economic study of the impact of geographic proximity on scientific collaborations are presented.

Abstract. Evolutionary clustering aims at capturing the temporal evolution of clusters. This issue is particularly important in the context of social media data that are naturally temporally driven. In this paper, we propose a new probabilistic model-based evolutionary clustering technique. The Temporal Multinomial Mixture (TMM) is an extension of classical mixture model that optimizes feature co-occurrences in the trade-off with temporal smoothness. Our model is evaluated for two recent case studies on opinion aggregation over time. We compare four different probabilistic clustering models and we show the superiority of our proposal in the task of instance-oriented clustering.

The response to the emergence of the 2009 influenza A(H1N1) pandemic was the result of a decade of pandemic planning, largely centred on the threat of an avian influenza A(H5N1) pandemic. Based on a literature review, this study aims to define a set of new pandemic scenarios that could be used in case of a future influenza pandemic. A total of 338 documents were identified using a searching strategy based on seven combinations of keywords. Eighty-three of these documents provided useful information on the 13 virusrelated and health-system-related parameters initially considered for describing scenarios. Among these, four parameters were finally selected (clinical attack rate, case fatality rate, hospital admission rate, and intensive care admission rate) and four different levels of severity for each of them were set. The definition of six most likely scenarios results from the combination of four different levels of severity of the four final parameters (256 possible scenarios). Although it has some limitations, this approach allows for more flexible scenarios and hence it is far from the classic scenarios structure used for pandemic plans until 2009.

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