Abstract. Collaborative modeling can enhance productivity and quality of modeling in system development and enterprise engineering projects by helping to construct agreement and a sense of model ownership among stakeholders/modelers. Most of these stakeholders have relatively low expertise in formal modeling; advanced modeler-oriented support for collaborative modeling is a possible remedy. As a basis for further development of such support (methods, tools), we have carried out a detailed exploratory study of the interaction between modelers, involving diverse aspects of modeling: goal setting, modeling language concepts, planning, etc. Central in our approach is the study of how collaborative modelers negotiate, set, use, and deal with the various rules/goals governing interactive modeling sessions. We describe the conceptual framework and approach used for our analysis, and present findings from a case study which focused on the first phases of a session concerning basic Business Process Modeling. We also compare our findings to some existing work, to demonstrate the relevance of our approach.

Collaborative modeling is one of the approaches used to enhance productivity in many enterprise modeling and system development projects. Determining the success of such a collaborative effort needs an evaluation of a number of factors which affect the quality of not only the end-products – the models, but also that of other modeling artifacts: the modeling language, the modeling procedure and the support tool. Although a number of quality frameworks have been developed, few of these frameworks have received practical validation and many offer little guidance about how the evaluation is operationalized. The Collaborative Modeling Evaluation (COME) framework presented in this paper offers a holistic approach to the evaluation of the four modeling artifacts. It employs the Analytic Hierarchy Process (AHP), a well-established method from Operations Research, to score the artifacts’ quality dimensions and to aggregate the modelers’ priorities and preferences. Results from a modeling experiment demonstrate both the theoretical and practical significance of the framework.

Abstract. Collaborative modeling uses and produces modeling artifacts whose quality can help us gauge the effectiveness and efficiency of the modeling process. Such artifacts include the modeling language, the modeling procedure, the products and the support tool or medium. To effectively assess the quality of any collaborative modeling process, the (inter-) dependencies of these artifacts and their effect on modeling process quality need to be analyzed. Although a number of research studies have assessed and measured the quality of collaborative processes, no formal (causal) model has been developed to assess the quality of the collaborative modeling process through a combination of modeling artifacts. This paper develops a Collaborative Modeling Process Quality (CMPQ) construct for assessing the quality of collaborative modeling. A modeling session involving 107 students was used to validate and measure the quality constructs in the model.

Typhoid disease continues to be a global public health burden. Uganda is one of the African countries characterized by high incidences of typhoid disease. Over 80% of the Ugandan districts are endemic for typhoid, largely attributable to lack of reliable knowledge to support disease surveillance. Spatial-temporal studies exploring major characteristics of the disease within the local population have remained limited in Uganda. The main goal of the study was to reveal spatial-temporal trends and distribution patterns of typhoid disease in Uganda for the period 2012 to 2017. Spatial-temporal statistics revealed monthly and annual trends of the disease at both regional and national levels. Results show that outbreaks occurred during 2015 and 2017 in central and eastern regions, respectively. Spatial scan statistic using the discrete Poisson model revealed spatial clusters of the disease for each of the years from 2012 to 2017, together with populations at risk. Most of the disease clustering was in the central region, followed by western and eastern regions (P <0.01). The northern region was the safest throughout the study period. This knowledge helps surveillance teams to i) plan and enforce preventive measures; ii) effectively prepare for outbreaks; iii) make targeted interventions for resource optimization; and iv) evaluate effectiveness of the intervention methods in the study period. This exploratory research forms a foundation of using Geographical Information Systems (GIS) in other related subsequent research studies to discover hidden spatial patterns that are difficult to discover with conventional methods.

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