Antivirus Mask Selection Under Spherical Fuzzy Information

Year 2021, , 1037 - 1048, 30.08.2021

Serhat Aydın , Emrah Köksalmış

https://doi.org/10.16984/saufenbilder.902048

Cited By: 2

Abstract

Many individuals are facing antivirus mask scarcity with the exponential spread of COVID-19. A functional antivirus mask needs to be selected and made usable for everyone. Selection mask problem contains qualitative criteria, therefore utilizing fuzzy logic for this problem is a useful approach. To optimize the efficiency of choosing antivirus masks, we propose to use one of the new types of ordinary fuzzy sets, named Spherical fuzzy sets. For this purpose, we determine 4 different alternatives and 4 criteria. Then, we gather the data under spherical information and applied the Spherical fuzzy AHP method to the problem. Then, we propose an entropy based Spherical fuzzy AHP method. We compare the results of Spherical fuzzy AHP method, and an entropy based Spherical fuzzy AHP method. Moreover, we present a sensitivity analysis to demonstrate how our model is sensitive to changes in weights of criteria. Finally, the best antivirus mask is determined for public use and we present the advantages of the proposed method in results section.

Keywords

Mask selection, spherical fuzzy sets, spherical AHP, COVID-19

References

• [1] D. Cucinotta, and M. Vanelli, “WHO declares COVID-19 a pandemic”, Acta Biomed, vol. 91 no.1, pp 57-160, 2020.
• [2] https://www.bbc.com/news/world-51235105. Access date: 31.01.2021.
• [3] B. W. Schuller, D. M. Schuller, K. Qian, J. Liu, H. Zheng, X. Li, “Covid-19 and computer audition: An overview on what speech and sound analysis could contribute in the sars-Cov-2 corona crisis”, ArXiv preprint arXiv:2003.11117, 2020
• [4] https://www.who.int/health topics/coronavirus. Access date: 31.01.2021.
• [5] General Secretariat for National Defence “National plan for the prevention and control of influenza pandemic”, General Secretariat for National Defence Report Plan, Paris, pp.1-78, 2007.
• [6] The Department of Health and Ageing, “Australian health management plan for pandemic influenza: important information for all Australians”, Canberra: Department of Health and Ageing, Australian Health Management Plan, Australia, pp. 1-232, 2006.
• [7] United States Department of Health and Human Services , “Department of health and human services”, HHS pandemic influenza plan. Washington, pp.1-396, 2005.
• [8] C.R. MacIntyre, S. Cauchemez, D.E. Dwyer, H. Seale, P. Cheung, G. Browne, M. Fasher, J. Wood, Z. Gao, R. Booy, N. Ferguson, “Face mask use and control of respiratory virus transmission in households”, Emerging Infectious Diseases, vol. 15, no.2, pp 233–41, 2009.
• [9] N.C.J. Brienen, A. Timen, J. Wallinga, J.E. Van Steenbergen, P.F.M. Teunis, “The effect of mask use on the spread of influenza during a pandemic”, Risk Anal, vol. 30 no. 8, pp. 1210–8, 2009.
• [10] L.A. Zadeh, “Fuzzy sets”, In Fuzzy sets, Fuzzy logic, and Fuzzy systems, vol. 6, pp. 394-432, 1996.
• [11] K. Atanassov, “Intuitionistic fuzzy sets”, International Journal Bioautomation, vol. 20, no. 1, 2016.
• [12] R.R. Yager,, “Pythagorean fuzzy subsets”. In 2013 Joint Ifsa World Congress and Nafips Annual Meeting, Edmonton, AB, Canada, pp. 57-61, 2013.
• [13] R.R. Yager, “Generalized orthopair fuzzy sets”, IEEE Transactions on Fuzzy Systems, vol. 25, no. 5, pp. 1222-1230, 2016.
• [14] F. Kutlu Gündoğdu, C. Kahraman, “Spherical fuzzy sets and spherical fuzzy TOPSIS method”, Journal of Intelligent and Fuzzy Systems, vol. 36, no.1, pp. 337-352, 2019.
• [15] T.L. Saaty, The Analytic Hierarchy Process, McGraw- Hill, New York pp.78-99, 1980.
• [16] T.L. Saaty, “Decision making with dependence and feedback: The analytic network process” (Vol. 4922). Pittsburgh: RWS publications, 1996.
• [17] G.H. Tzeng, J.J. Huang, "Multiple Attribute Decision Making: Methods and Applications". Springer-Verlag Berlin Heidelberg, pp. 69-76, 2011.
• [18] B. Roy, "Classement et choix en présence de points de vue multiples”, Revue Française D'informatique et de Recherche Opérationnelle, vol.2, no. 8, pp. 57-75, 1968.
• [19] Z. Yang, X. Li, H. Garg, M. Qi, “Decision support algorithm for selecting an antivirus mask over COVID-19 pandemic under spherical normal fuzzy environment”, International Journal of Environmental Research and Public Health, vol. 17, no. 10, pp. 3407-3418, 2020.
• [20] G. Shahzadi, M. Akram, “Group decision-making for the selection of an antivirus mask under fermatean fuzzy soft information”, Journal of Intelligent and Fuzzy Systems, (Preprint), pp. 1-16, 2021.
• [21] D. Wang, Y. You, X. Zhou, Z. Zong, H. Huang, H. Zhang, X. Yong, Y. Cheng, L. Yang, Q. Guo, Y. Long, Y. Liu, J. Huang, L. Du, “Selection of homemade mask materials for preventing transmission of COVID-19: A laboratory study”, Plos One, vol. 15, no. 10, e0240285, 2020.
• [22] D.S.C. Lam, R.L.M. Wong, K.H.W. Lai, C.N. Ko, H.Y. Leung, V.Y.W. Lee, J.Y.N.M. Lau, S.S. Huang, “COVID-19: special precautions in ophthalmic practice and FAQs on personal protection and mask selection”, Asia-Pacific Journal of Ophthalmology, vol.9, no. 2, pp. 67-77 2020.
• [23] S. Feng, C. Shen, N. Xia, W. Song, M. Fan, B.J. Cowling, “Rational use of face masks in the COVID-19 pandemic”, The Lancet Respiratory Medicine, vol. 8, no.5, pp. 434-436, 2020.
• [24] S. Q. Zheng, L.Yang, P.X. Zhou, H.B. Li, F. Liu, R.S. Zhao, “Recommendations and guidance for providing pharmaceutical care services during COVID-19 pandemic: A China perspective”, Research in Social and Administrative Pharmacy, vol. 17, no. 1, pp. 1819–1824, 2020.
• [25] J.J. Bartoszko, M.A.M. Farooqi, W. Alhazzani, M. Loeb, “Medical masks vs N95 respirators for preventing COVID‐19 in healthcare workers: A systematic review and meta‐analysis of randomized trials”, Influenza and Other Respiratory Viruses, vol. 14, no. 4, pp. 365-373, 2020.
• [26] F. K. Gündoğdu, C. Kahraman, “A novel spherical fuzzy analytic hierarchy process and its renewable energy application”, Soft Computing, vol. 24, no. 6, pp. 4607-4621, 2020.