Conference paper Open Access

The COVID-19 epidemiology and monitoring ontology

Queralt-Rosinach, Núria; Schofield, Paul N.; Hoehndorf, Robert; Weiland, Claus; Schultes, Erik; Bernabé, César H.; Roos, Marco

One year ago, the novel COVID-19 infectious disease emerged and spread, causing high mortality and morbidity rates worldwide. In the OBO Foundry, there are more than one hundred ontologies to share and analyse large-scale datasets for biological and biomedical sciences. However, this pandemic revealed that we lack tools for an efficient and timely exchange of this epidemiological data which is necessary to assess the impact of disease outbreaks, the efficacy of mitigating interventions and to provide a rapid response. Recently, several new COVID-19 ontologies have developed such as the IDO extension or CIDO. Hence, our research question was to determine if there was a good representation of epidemiological quantitative concepts in OBO ontologies. Our objectives were to identify missing COVID-19 epidemiological terms and implement axiom patterns for extensions to existing ontologies or to build a new, logically well-formed, and accurate ontology in OBO. In this study we present our findings and contributions for the bio-ontologies community.

This initiative has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement N°825575 (the European Joint Programme Rare Diseases), and the ZonMW Health Holland under the Trusted World of Corona (TWOC) project number LSHM20070.
Files (2.8 MB)
Name Size
90.5 kB Download
978.4 kB Download
1.7 MB Download
6.6 kB Download
All versions This version
Views 130130
Downloads 105105
Data volume 33.9 MB33.9 MB
Unique views 116116
Unique downloads 8888


Cite as