ABSTRACT
The concepts of relations and information measures have importance whenever we deal with medical diagnosis problems. The aim of this paper is to investigate the global pandemic COVID-19 scenario using relations and information measures in an interval-valued T-spherical fuzzy (IVTSF) environment. An IVTSF set (IVTSFS) allows describing four aspects of human opinions i.e., membership, abstinence, non-membership, and refusal grade that process information in a significant way and reduce information loss. We propose similarity measures and relations in the IVTSF environment and investigate their properties. Both information measures and relations are applied in a medical diagnosis problem keeping in view the global pandemic COVID-19. How to determine the diagnosis based on symptoms of a patient using similarity measures and relations is discussed. Finally, the advantages of dealing with such problems using the IVTSF framework are demonstrated with examples.