CODCA – COVID-19 ONTOLOGY FOR DATA COLLECTION AND ANALYSIS IN E-HEALTH

Abstract. Coronavirus (Covid-19) pandemic is one of the most deadly diseases that cause the death of millions around the world. Automatic collection and analysis of Covid-19 patient data will help medical practitioners in containing the virus. For this purpose, Semantic Web technologies can be utilized, which allows machine-processable data and enables data sharing, and reuse across machines. In this paper, we propose a Covid-19 ontology (named CODCA) that helps in collecting, analysing, and sharing medical information about people in the e-health domain. In particular, the proposed ontology uses information about medical history, drug history, vaccination history, and symptoms in order to analyse Covid-19 risk factors of people and their treatment plans. In this way, information about Covid-19 patients can be automatically processed and can be re-usable by other applications. We also demonstrate extensive semantic queries (i.e. SPARQL queries) to search the created metadata. Furthermore, we illustrate the usage of semantic rules (i.e. SWRL) so that treatment plans for individual patients can be inferred from the available knowledge.