Algorithms for Covid-19 outbreak using soft set theory: estimation and application.

Abstract

Coronavirus disease (Covid-19) is a novel pandemic disease. Covid-19 originates from SARS-COV2 and represents the cause of a potentially fatal disease as a global public health problem. However, we have to renew our knowledge about the symptoms of this disease day by day. If we look generally, although the main symptoms seen in this epidemic are fever, cough and shortness of breath, cases without symptoms are also reported. Moreover, in severe cases, pneumonia, severe respiratory failure, kidney failure and death may develop. In this paper, it is suggested that all the different symptoms that may occur in various regions of the world should be taken into consideration and each region should be evaluated within itself. Moreover, in order to have an idea of the general situation, it was taken into account in the average case. For this, two algorithms were built by using soft set theory. The first of the algorithms focuses on the analysis of the relationships between the symptoms and aims to measure a possible effect of the symptoms on each other. The second one aims to identify the most dominant symptom. The results obtained by utilizing both algorithms argue that it is more useful to examine different regions in order to better manage the epidemic. Moreover, some consistent results have been obtained as to which parameters a person should show first in order to Covid test.