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Data that is needed to detect outbreaks of known and unknown diseases is often gathered from sources that are scattered in many geographical locations. Often these scattered data exist in a wide variety of formats, structures, and models. The collection, pre-processing, and analysis of these data to detect potential disease outbreaks is very challenging, time-consuming and error-prone. To fight disease outbreaks, healthcare practition-ers, epidemiologists and researchers need to access the scattered data in a secure and timely manner. They also require a uniform and logical framework or methodology to access the relevant data. In this paper, authors propose a federated framework for Disease Outbreak Notification Systems (DONSFed). Using advanced design and an XML technique patented in the US in 2016 by our team, the framework was tested and validated as part of this work. The proposed approach enables healthcare professionals to quickly and uniformly access data that is required to detect potential disease outbreaks. This research focuses on implementing a cloud-based prototype as a proof-of-concept to demonstrate the functionalities and to verify the concept of the proposed framework.

Framework for Disease Outbreak Notification Systems with an Optimized Federation Layer