A semantic FAIRness framework for epidemiological analysis of COVID-19 data in the UAE.

The increasing availability of Coronavirus disease 2019 (COVID-19)-related data has highlighted the need for robust epidemiological analysis to support public health decision-making, particularly in contexts where data are heterogeneous and fragmented. In the United Arab Emirates (UAE), COVID-19 research has generated diverse genomic, clinical, and epidemiological datasets, yet their integration and reuse remain challenging due to inconsistencies in data representation, semantics, and interoperability. This study aimed to review key genomic and epidemiological studies related to COVID-19 in the UAE and, informed by identified gaps, proposes a semantic FAIRness framework for epidemiological data integration and analysis. The framework leverages the FAIR data principles and semantic technologies to provide a conceptual architecture for aggregating heterogeneous data sources, transforming data using ontological models, and enabling semantic linkage and reasoning across datasets. At a conceptual level, the framework is intended to support comparative analysis across studies, facilitate transparent representation of uncertainty, and promote semantically interoperable data sharing among diverse stakeholders. While selected components of the framework build on prior proof-of-concept implementations, the framework as a whole has not yet been fully implemented or empirically evaluated. The proposed approach is therefore positioned as a foundation for future development and evaluation, with the potential to enhance evidence-informed epidemiological analysis and public health decision-making in the UAE and similar contexts.