An Overview of Ontologies and Tool Support for COVID-19 Analytics

TL;DR

This paper reviews how ontologies and supporting tools can enhance COVID-19 data analytics by providing a formal framework for integrating diverse data sources and enabling better pandemic management.

Contribution

It offers an overview of ontology-based approaches and tools that facilitate COVID-19 data integration, reasoning, and decision-making.

Findings

Ontologies improve integration of COVID-19 data sources.

Ontology-based tools support pandemic hotspot identification.

Enhanced reasoning aids in pandemic management strategies.

Abstract

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.