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

arXiv: https://arxiv.org/abs/2110.06397 · HTML: https://mlsanity.com/paper/2110.06397/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 37 references that are in the map.
- First posted on arXiv 12 Oct 2021.

## Builds on (most cited first, 4 of 4)

- [CODO: An Ontology for Collection and Analysis of Covid-19 Data](https://mlsanity.com/paper/2009.01210/) (2020)
- [A Survey of COVID-19 Contact Tracing Apps](https://mlsanity.com/paper/2006.10306/) (2020)
- [An Interactive Data Visualization and Analytics Tool to Evaluate Mobility and Sociability Trends During COVID-19](https://mlsanity.com/paper/2006.14882/) (2020)
- [Towards Integrated and Open COVID-19 Data](https://mlsanity.com/paper/2008.04045/) (2020)

## Built on by (most cited first, 1 of 1)

- [Forecasting COVID- 19 cases using Statistical Models and Ontology-based Semantic Modelling: A real time data analytics approach](https://mlsanity.com/paper/2206.02795/) (2022)

## Abstract (arXiv)

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.