=Paper=
{{Paper
|id=Vol-3415/paper-34
|storemode=property
|title=Updating the CEMO ontology for future epidemiological challenges
|pdfUrl=https://ceur-ws.org/Vol-3415/paper-34.pdf
|volume=Vol-3415
|dblpUrl=https://dblp.org/rec/conf/swat4ls/Queralt-Rosinach23
}}
==Updating the CEMO ontology for future epidemiological challenges==
https://ceur-ws.org/Vol-3415/paper-34.pdf
Updating the CEMO ontology for future
epidemiological challenges
Núria Queralt-Rosinach1,∗ , Paul N. Schofield2 , Marco Roos1 and Robert Hoehndorf3
1
Leiden University Medical Center, Leiden, The Netherlands
2
University of Cambridge, Cambridge, United Kingdom
3
King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia
Abstract
The COVID-19 epidemiology and monitoring ontology (CEMO) is an OWL ontology built during the
COVID-19 pandemic for better exchange, integration and reuse of epidemiological information. Here,
we present an update of the development of the ontology and future directions in order to make it usable
under different scenarios and new challenges.
Keywords
Ontologies, Epidemiology, COVID-19, FAIR
1. Motivation
The COVID-19 outbreak seriously challenged worldwide research data infrastructure for patient
monitoring and public health surveillance, and sharply exposed our problems on sharing
and analyzing health data. Epidemiology is the area of science that uses population statistical
analysis to monitor and provide evidence on how disease outbreaks are spread or contained. The
COVID-19 epidemiology and monitoring ontology (CEMO) is an OWL ontology built during the
COVID-19 pandemic for better exchange, integration and reuse of epidemiological information
[1] publicly available on GitHub. Making these data readily available for computational analysis
is essential for efficient outbreak surveillance and evidence-based decision making for public
health to provide rapid responses. Failure to providing this information unambiguously and in
a machine readable way, not only blocks robust national policymaking, but also across borders.
CEMO was developed to fill the gap in the biomedical ontological landscape to represent epi-
demiological quantitative data. The ontology was developed following knowledge engineering
best practices, and importantly the OBO principles in order to maximize its use for efficient
biomedical analysis. Furthermore, and as an RDA COVID-19 recommendation on data sharing,
we incorporated a patient-population link to enable reasoning and analytics of person-level
SWAT4HCLS 2023: The 14th International Conference on Semantic Web Applications and Tools for Health Care and Life
Sciences
∗
Corresponding author.
Envelope-Open n.queralt_rosinach@lumc.nl (N. Queralt-Rosinach); pns12@cam.ac.uk (P. N. Schofield); m.roos@lumc.nl
(M. Roos); robert.hoehndorf@kaust.edu.sa (R. Hoehndorf)
Orcid 0000-0003-0169-8159 (N. Queralt-Rosinach); 0000-0002-5111-7263 (P. N. Schofield); 0000-0002-8691-772X
(M. Roos); 0000-0001-8149-5890 (R. Hoehndorf)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
CEUR
http://ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
real world observations over epidemiological surveillance information based on the GA4GH
Phenopackets and OHDSI OMOP data model representations. Here, we present an update of
the development of the ontology and future directions in order to make it usable under different
scenarios and new challenges.
2. Modifications
Firstly, we are improving the logical structure of the ontology. We decided to build an OBO
ontology and use the OWL 2, a DL-based formalism and Semantic Web standard for knowledge
representation, to enable data sharing and formal reasoning. Interoperability in OBO is fostered
by adopting the BFO hierarchy. But, in the first version of the ontology we reused a GFO-based
design pattern to represent time courses, which led to logical inconsistency between these two
foundational ontologies due to incompatible conceptualizations of time in BFO and GFO. We are
also improving the commitment of the ontology to the OBO principles and the epidemiological
representation. Secondly, we are extending the ontology to be reused in particular use-cases
such as VODAN-Africa and waste-water surveillance. The Virus Outbreak Data Network
(VODAN) Africa is a collaboration of researchers and health practitioners across 15 African
countries and 83 health facilities to enable access to and analysis of critical data needed to fight
the novel COVID-19 in Africa following the data-visiting approach, i.e., data stays under the
control of the owner and allows the consumers (e.g. analysts or machine learning algorithms)
to come to the data to work with it. Thirdly, to tackle health conditions associated with climate
change we are enriching the ontology with extreme climate events epidemiology. Outbreaks of
climate-sensitive infectious diseases in the aftermath of extreme climatic events, such as floods
and heatwaves, are of high public health concern. We are currently curating climate-related
terms and evaluating how to include them in a COVID-19 epidemiology data model. With this
new addition we intend to facilitate investigation of the association between extreme climatic
events and COVID-19, and potentially any other disease outbreaks. Finally, we are collaborating
with the COVID-19 ontology harmonization effort for several OBO ontologies being developed
internationally. We expect to use this model in FAIR-based projects such as TWOC.
Acknowledgments
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), the collaboration project Trusted World of Corona (TWOC) which is co-funded by
the PPP Allowance made available by Health Holland, Top Sector Life Sciences & Health, to
stimulate public-private partnerships, and The Alan Turing Institute.
References
[1] N. Queralt-Rosinach, P. Schofield, R. Hoehndorf, C. Weiland, E. Schultes, C. H. Bernabé,
M. Roos, The covid-19 epidemiology and monitoring ontology (2021). doi:h t t p s : / / d o i . o r g /
10.5281/zenodo.5752958.