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The COVID-19 pandemic has caused the deaths of millions of people around the world. The scientific community faces a tough struggle to reduce the effects of this pandemic. Several investigations dealing with different perspectives have been carried out. However, it is not easy to find studies focused on COVID-19 contagion chains. A deep analysis of contagion chains may contribute new findings that can be used to reduce the effects of COVID-19. For example, some interesting chains with specific behaviors could be identified and more in-depth analyses could be performed to investigate the reasons for such behaviors. To represent, validate and analyze the information of contagion chains, we adopted an ontological approach. Ontologies are artificial intelligence techniques that have become widely accepted solutions for the representation of knowledge and corresponding analyses. The semantic representation of information by means of ontologies enables the consistency of the information to be checked, as well as automatic reasoning to infer new knowledge. The ontology was implemented in Ontology Web Language (OWL), which is a formal language based on description logics. This approach could have a special impact on smart cities, which are characterized as using information to enhance the quality of basic services for citizens. In particular, health services could take advantage of this approach to reduce the effects of COVID-19.

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