Development of CODO: A Comprehensive Tool for COVID-19 Data Representation, Analysis, and Visualization

TL;DR

This paper presents CODO, a comprehensive ontological tool designed to integrate, analyze, and visualize diverse COVID-19 data, facilitating better understanding and decision-making during the pandemic.

Contribution

The paper introduces CODO, an extensive COVID-19 ontology that covers multiple domains and ensures semantic interoperability for data integration and analysis.

Findings

CODO effectively aggregates diverse COVID-19 datasets.

CODO supports data visualization and analysis across multiple domains.

CODO adheres to W3C standards for data integration.

Abstract

Artificial intelligence (AI) has become indispensable for managing and processing the vast amounts of data generated during the COVID-19 pandemic. Ontology, which formalizes knowledge within a domain using standardized vocabularies and relationships, plays a crucial role in AI by enabling automated reasoning, data integration, semantic interoperability, and extracting meaningful insights from extensive datasets. The diversity of COVID-19 datasets poses challenges in comprehending this information for both human and machines. Existing COVID-19 ontologies are designed to address specific aspects of the pandemic but lack comprehensive coverage across all essential dimensions. To address this gap, CODO, an integrated ontological model has been developed encompassing critical facets of COVID-19 information such as aetiology, epidemiology, transmission, pathogenesis, diagnosis, prevention, genomics, therapeutic safety, and more. This paper reviews CODO since its inception in 2020, detailing its developments and highlighting CODO as a tool for the aggregation, representation, analysis, and visualization of diverse COVID-19 data. The major contribution of this paper is to provide a summary of the development of CODO, and outline the overall development and evaluation approach. By adhering to best practices and leveraging W3C standards, CODO ensures data integration and semantic interoperability, supporting effective navigation of COVID-19 complexities across various domains.