ROC: An Ontology for Country Responses towards COVID-19

TL;DR

The ROC ontology models and standardizes data on country responses to COVID-19, enabling integrated analysis of government measures and their effectiveness across nations.

Contribution

It introduces a semantic standard-compliant ontology for COVID-19 response data, facilitating data integration and analysis across heterogeneous sources.

Findings

Supports statistical evaluation of government responses

Enables data linking from multiple sources

Facilitates comparative analysis of COVID-19 measures

Abstract

The ROC ontology for country responses to COVID-19 provides a model for collecting, linking and sharing data on the COVID-19 pandemic. It follows semantic standardization (W3C standards RDF, OWL, SPARQL) for the representation of concepts and creation of vocabularies. ROC focuses on country measures and enables the integration of data from heterogeneous data sources. The proposed ontology is intended to facilitate statistical analysis to study and evaluate the effectiveness and side effects of government responses to COVID-19 in different countries. The ontology contains data collected by OxCGRT from publicly available information. This data has been compiled from information provided by ECDC for most countries, as well as from various repositories used to collect data on COVID-19.