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. © 2021, Springer Nature Switzerland AG.
COVIDonto: An Ontology Model for Acquisition and Sharing of COVID-19 Data