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Malaria is one of the leading causes of death and illness in sub-Saharan Africa. In order to make timely decisions for control and elimination of malaria, researchers, and clinicians need access to integrated consistent knowledge sources. These knowledge sources often rely on dynamic and constantly changing databases and ontologies. It is crucial to manage changes and ensure that these changes do not cause inconsistencies in the integrated knowledge source. To this end, we propose the use of a formal model using graph transformations to monitor and manage the changes in a coherent way while preserving the consistency of the integrated structure through classical verification. In this paper, we use an algorithmic approach to graph transformation, instead of the more classical algebraic approach, to express the evolution of the data and ontological structures. In this model, each transformation is the result of applying rules to the graph, where the left-hand side is used to select a subgraph and the right-hand side is a sequence of elementary actions to be performed. Strategies are used to define how transformation rules should be applied. This approach enables us to define a Hoare-like calculus that can be used to verify the transformations and manage the changes. In this paper, we demonstrate the feasibility and significance of the proposed method through different use cases in malaria surveillance.

Applied Graph Transformation and Verification With Use Cases in Malaria Surveillance