A Decision-Making Framework Using q-Rung Orthopair Probabilistic Hesitant Fuzzy Rough Aggregation Information for the Drug Selection to Treat COVID-19

Author:

Attaullah 1,Ashraf Shahzaib2,Rehman Noor2,AlSalman Hussain3ORCID,Gumaei Abdu H.4ORCID

Affiliation:

1. Department of Mathematics, Abdul Wali Khan University, Mardan 23200, Pakistan

2. Department of Mathematics and Statistics, Bacha Khan University, Charsadda 24420, Pakistan

3. Department of Computer Science, College of Computer and Information Sciences, King Saud University Riyadh, Riyadh 11543, Saudi Arabia

4. Computer Science Department, Faculty of Applied Sciences, Taiz University, Taiz 6803, Yemen

Abstract

In our current era, a new rapidly spreading pandemic disease called coronavirus disease (COVID-19), caused by a virus identified as a novel coronavirus (SARS-CoV-2), is becoming a crucial threat for the whole world. Currently, the number of patients infected by the virus is expanding exponentially, but there is no commercially available COVID-19 medication for this pandemic. However, numerous antiviral drugs are utilized for the treatment of the COVID-19 disease. Identification of the appropriate antivirus medicine to treat the infection of COVID-19 is still a complicated and uncertain decision. This study’s key objective is to develop a novel approach called q-rung orthopair probabilistic hesitant fuzzy rough set (q-ROPHFRS), which incorporates the q-rung orthopair fuzzy set, probabilistic hesitant fuzzy set, and rough set structures. New q-ROPHFR aggregation operators have been established: the q-ROPHFR Einstein weighted averaging (q-ROPHFREWA) operator and the q-ROPHFR Einstein weighted geometric (q-ROPHFREWG) operator. In this study, we explored some basic features of the developed operators. Afterward, to demonstrate the viability and feasibility of the established decision-making approach in real-world applications, a case study related to selecting drugs for COVID-19 pandemic is addressed. Furthermore, a comprehensive comparison with the q-rung orthopair probabilistic hesitant fuzzy rough TOPSIS technique is also presented to illustrate the benefits of the new framework. The obtained results confirm the reliability and effectiveness of the proposed approach for finding uncertainty in real-world decision-making.

Funder

King Saud University

Publisher

Hindawi Limited

Subject

Multidisciplinary,General Computer Science

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