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COVIDonto: An Ontology Model for Acquisition and Sharing of COVID-19 Data

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Model and Data Engineering (MEDI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12732))

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Abstract

The collection and sharing of accurate data is paramount to the fight against COVID-19. However, the health system in many countries is fragmented. Furthermore, because no one was prepared for COVID-19, manual information systems have been put in place in many health facilities to collect and record COVID-19 data. This reality brings many challenges such as delay, inaccuracy and inconsistency in the COVID-19 data collected for the control and monitoring of the pandemic. Recent studies have developed ontologies for COVID-19 data modeling and acquisition. However, the scopes of these ontologies have been the modeling of patients, available medical infrastructures, and biology and biomedical aspects of COVID-19. This study extends these existing ontologies to develop the COVID-19 ontology (COVIDonto) to model the origin, symptoms, spread and treatment of COVID-19. The NeOn methodology was followed to gather data from secondary sources to formalize the COVIDonto ontology in Description Logics (DLs). The COVIDonto ontology was implemented in a machine-executable form with the Web Ontology Language (OWL) in Protégé ontology editor. The COVIDonto ontology is a formal executable model of COVID-19 that can be leveraged in web-based applications to integrate health facilities in a country for the automatic acquisition and sharing of COVID-19 data. Moreover, the COVIDonto could serve as a medium for cross-border interoperability of government systems of various countries and facilitate data sharing in the fight against the COVID-19 pandemic.

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Authors and Affiliations

  1. School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal King Edward Avenue, Scottsville, Pietermaritzburg, 3209, South Africa

    Jean Vincent Fonou-Dombeu, Thimershen Achary, Emma Genders, Shiv Mahabeer & Shivani Mahashakti Pillay

Authors
  1. Jean Vincent Fonou-Dombeu
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  2. Thimershen Achary
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  3. Emma Genders
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  4. Shiv Mahabeer
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  5. Shivani Mahashakti Pillay
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Corresponding author

Correspondence to Jean Vincent Fonou-Dombeu .

Editor information

Editors and Affiliations

  1. University of Nantes, Nantes, France

    Christian Attiogbé

  2. Tallinn University of Technology, Tallinn, Estonia

    Sadok Ben Yahia

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Fonou-Dombeu, J.V., Achary, T., Genders, E., Mahabeer, S., Pillay, S.M. (2021). COVIDonto: An Ontology Model for Acquisition and Sharing of COVID-19 Data. In: Attiogbé, C., Ben Yahia, S. (eds) Model and Data Engineering. MEDI 2021. Lecture Notes in Computer Science(), vol 12732. Springer, Cham. https://doi.org/10.1007/978-3-030-78428-7_18

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  • DOI: https://doi.org/10.1007/978-3-030-78428-7_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78427-0

  • Online ISBN: 978-3-030-78428-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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