2021/10/12 by Aakash Ahmad, Madhushi Bandara, Ahmad, Aakash +9
Biochemistry, Genetics and Molecular Biology · Computer Science · Medicine · #Anomaly Detection Techniques and Applications #Biomedical Text Mining and Ontologies #Computation and Language (cs.CL) #Data-Driven Disease Surveillance #FOS: Computer and information sciences #Software Engineering (cs.SE) #Symbolic Computation (cs.SC)
paper · pdf · doi:10.48550/arxiv.2110.06397
openalex publication_date 2021/10/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The outbreak of the SARS-CoV-2 pandemic of the new COVID-19 disease (COVID-19 for short) demands empowering existing medical, economic, and social emergency backend systems with data analytics capabilities. An impediment in taking advantages of data analytics in these systems is the lack of a unified framework or reference model. Ontologies are highlighted as a promising solution to bridge this gap by providing a formal representation of COVID-19 concepts such as symptoms, infections rate, contact tracing, and drug modelling. Ontology-based solutions enable the integration of diverse data sources that leads to a better understanding of pandemic data, management of smart lockdowns by identifying pandemic hotspots, and knowledge-driven inference, reasoning, and recommendations to tackle surrounding issues.