A Brief Review of Application of Fuzzy Techniques Towards COVID-19 Pandemic Analysis
Abstract
COVID-19 continued to spread fast throughout the world since its outbreak from December, 2019. Most of the affected countries faced a huge challenge in managing the infection rate and providing the required treatments to the infected ones, which led the researchers to investigate the necessary causes and solutions regarding the infections. Researchers were also involved in estimating and forecasting the future trends and effects of COVID-19 as prediction is crucial to handling the unwanted pandemic situation. The uncertain nature of COVID-19 inspired researchers to adopt fuzzy sets for managing the pandemic. Researchers introduced various fuzzy logic-based models to analyze the pandemic situation and predict future directions. The aim of this study is to present an organized literature review to study the applicability of fuzzy set theory and its extensions in order to manage the pandemic situation. The COVID-19 related articles are grouped into six domains related to predictions (S1), related factor analysis (S2), prevention, control and managing the situation (S3), analysis of treatment (S4), after effects (S5), and distribution of vaccine (S6). Insights of the published articles are depicted using tabular representations. We have analyzed the significance of various categories to explore their societal impacts. This comprehensive review reveals a greater emphasis on experimenting with strategies to control the impact of COVID-19, while there is less focus on studying the effects of COVID-19, particularly in terms of vaccine distribution. The domain-wise data analysis from current research presents various approaches and directions. Additionally, this study predicts future research directions for each of the mentioned categories. Researchers initially focused on prevention, prediction, and control of COVID19. The analysis reports illustrate the effects of COVID-19 factors and their social impacts on communities.
References
J. Piret, B. Guy, Pandemics Throughout History. Frontiers in Microbiology, 2021. https://www.who.int/publications/i/item/10665-331495. https://www.who.int/publications/i/item/who-2019-ncov-vaccine-deployment-2021.1-eng.
R. M. Burke, “Active monitoring of persons exposed to patients with confirmed covid-19— united states, january–february 2020,” MMWR. Morbidity and mortality weekly report, vol. 69, 2020 W. E. Wei, “Presymptomatic transmission of sars-cov-2—singapore, january 23–march 16, 2020,” MMWR. Morbidity and mortality weekly report, vol. 69, 2020. https://covid-19.who.int/table.
S. Kousar, A. Ansar, N. Kausar, and G. Freen, “Multi-criteria decision-making for smog mitigation: a comprehensive analysis of health, economic, and ecological impacts,” Spectrum of Decision Making and Applications, vol. 2, no. 1, pp. 53–67, 2025. L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965.
L. A. Zadeh., “The concept of a linguistic variable and its application to approximate reasoning—i,” Information Sciences, vol. 8, no. 3, pp. 199–249, 1975.
K. T. Atanassov and K. T. Atanassov, Intuitionistic fuzzy sets. Springer, 1999.
K. T. Atanassov. and K. T. Atanassov, “Interval valued intuitionistic fuzzy sets,” Springer, 1999.
J.-S. Jang, “Anfis: adaptive-network-based fuzzy inference system,” IEEE Transactions on Systems, man, and Cybernetics, vol. 23, no. 3, pp. 665–685, 1993.
R. R. Yager, “On the theory of bags,” International Journal of General System, vol. 13, no. 1, pp. 23–37, 1986.
V. Torra, “Hesitant fuzzy sets,” International Journal of Intelligent Systems, vol. 25, no. 6, pp. 529–539, 2010.
A. SI and S. Das, “Mfs: Dynamic decision making approach to select optimal alternative in the presence of uncertainty,” Iranian Journal of Fuzzy Systems, vol. 22, no. 2, pp. 81–95, 2025.
J. A. Goguen, “L-fuzzy sets,” Journal of Mathematical Analysis and Applications, vol. 18, no. 1, pp. 145–174, 1967.
S. Miyamoto, “Fuzzy multisets and their generalizations,” in Workshop on membrane computing. Springer, 2000, pp. 225–235.
G. Wei, “Picture fuzzy cross-entropy for multiple attribute decision making problems,” Journal of Business Economics and Management, vol. 17, no. 4, pp. 491–502, 2016.
A. Si, S. Das, and S. Kar, “An approach to rank picture fuzzy numbers for decision making problems,” Decision Making: Applications in Management and Engineering, vol. 2, no. 2, pp. 54–64, 2019.O. Montiel, O. Castillo, P. Melin, and R. Sepulveda, “Mediative fuzzy logic: a new approach for contradictory knowledge management,” Soft Computing, vol. 12, no. 3, pp. 251–256, 2008.
M. Keshavarz-Ghorabaee, M. Amiri, M. Hashemi-Tabatabaei, E. K. Zavadskas, and A. Kaklauskas, “A new decision-making approach based on fermatean fuzzy sets and waspas for green construction supplier evaluation,” Mathematics, vol. 8, no. 12, p. 2202, 2020.
D. Tešić and D. Marinković, “Application of fermatean fuzzy weight operators and mcdm model dibr-dibr ii-nwbm-bm for efficiency-based selection of a complex combat system,” Journal of Decision Analytics and Intelligent Computing, vol. 3, no. 1, pp. 243–256, 2023.
T. M. Al-shami, H. Z. Ibrahim, A. Azzam, and A. I. EL-Maghrabi, “Sr-fuzzy sets and their weighted aggregated operators in application to decision-making,” Journal of Function Spaces, vol. 2022, no. 1, p. 3653225, 2022.
T. M. Al-shami, “(2, 1)-fuzzy sets: properties, weighted aggregated operators and their applications to multi-criteria decision-making methods,” Complex & Intelligent Systems, vol. 9, no. 2, pp. 1687–1705, 2023.
S. Ashraf, S. Abdullah, M. Aslam, M. Qiyas, and M. A. Kutbi, “Spherical fuzzy sets and its representation of spherical fuzzy t-norms and t-conorms,” Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 6089–6102, 2019.
A. Hussain and K. Ullah, “An intelligent decision support system for spherical fuzzy sugenoweber aggregation operators and real-life applications,” Spectrum of Mechanical Engineering and Operational Research, vol. 1, no. 1, pp. 177–188, 2024.
T. M. Al-shami, “An improvement of rough sets’ accuracy measure using containment neighborhoods with a medical application,” Information Sciences, vol. 569, pp. 110–124, 2021.
T. M. Al-Shami, “Maximal rough neighborhoods with a medical application,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 12, pp. 16 373–16 384, 2023.
Z.Wu and J. M. McGoogan, “Characteristics of and important lessons from the coronavirus disease 2019 (covid-19) outbreak in china: summary of a report of 72 314 cases from the chinese center for disease control and prevention,” Jama, vol. 323, no. 13, pp. 1239–1242, 2020.
S. Dey, R. Bhattacharya, S. Malakar, S. Mirjalili, and R. Sarkar, “Choquet fuzzy integralbased classifier ensemble technique for covid-19 detection,” Computers in Biology and Medicine, vol. 135, p. 104585, 2021.
S. Chakraborty and K. Mali, “Sufmofpa: A superpixel and meta-heuristic based fuzzy image segmentation approach to explicate covid-19 radiological images,” Expert Systems with Applications, vol. 167, p. 114142, 2021.
W. M. Shaban, A. H. Rabie, A. I. Saleh, and M. Abo-Elsoud, “Detecting covid-19 patients based on fuzzy inference engine and deep neural network,” Applied Soft Computing, vol. 99, pp. 106906, 2021.
T. Nivethitha, S. K. Palanisamy, K. M. Prakash, and K. Jeevitha, “Comparative study of ann and fuzzy classifier for forecasting electrical activity of heart to diagnose covid-19,” Materials Today: Proceedings, vol. 45, pp. 2293–2305, 2021.
A. M. Kozae, M. Shokry, and M. Omran, “Intuitionistic fuzzy set and its application in corona covid-19,” Applied and Computational Mathematics, vol. 9, no. 5, pp. 146–154, 2020.
M. Shatnawi, A. Shatnawi, Z. AlShara, and G. Husari, “Symptoms-based fuzzy-logic approach for covid-19 diagnosis,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 4, pp. 444–452, 2021.
D. Painuli, D. Mishra, S. Bhardwaj, and M. Aggarwal, “Fuzzy rule based system to predict covid19-a deadly virus,” Way, vol. 3, no. 4, p. 5, 2020.
P. U. Ejodamen and V. E. Ekong, “A fuzzy expert system model for the determination of coronavirus disease risk,” Cardiovascular Diseases, vol. 3, p. 4, 2021.
M. To˘ga，car, B. Ergen, and Z. C¨omert, “Covid-19 detection using deep learning models to exploit social mimic optimization and structured chest x-ray images using fuzzy color and stacking approaches,” Computers in Biology and Medicine, vol. 121, p. 103805, 2020.
P. Melin, J. C. Monica, D. Sanchez, and O. Castillo, “Multiple ensemble neural network models with fuzzy response aggregation for predicting covid-19 time series: the case of mexico,” Healthcare, vol. 8, no. 2. MDPI, 2020, p. 181.
P. Melin, J. C. Monica, D. Sanchez, and O. Castillo, “Multiple ensemble neural network models with fuzzy response aggregation for predicting covid-19 time series: the case of mexico,” Healthcare, vol. 8, no. 2. MDPI, 2020, p. 181.
P. Melin, J. C. Monica, D. Sanchez, and O. Castillo, “Multiple ensemble neural network models with fuzzy response aggregation for predicting covid-19 time series: the case of mexico,” Healthcare, vol. 8, no. 2. MDPI, 2020, p. 181.
A. Alderremy, J. G´omez-Aguilar, S. Aly, and K. M. Saad, “A fuzzy fractional model of coronavirus (covid-19) and its study with legendre spectral method,” Results in Physics, vol. 21, p. 103773, 2021.
N. Ghorui, A. Ghosh, S. P. Mondal, M. Y. Bajuri, A. Ahmadian, S. Salahshour, and M. Ferrara, “Identification of dominant risk factor involved in spread of covid-19 using hesitant fuzzy mcdm methodology,” Results in Physics, vol. 21, p. 103811, 2021.
A. Biswas, K. H. Gazi, P. M. Sankar, and A. Ghosh, “A decision-making framework for sustainable highway restaurant site selection: Ahp-topsis approach based on the fuzzy numbers,” Spectrum of Operational Research, vol. 2, no. 1, pp. 1–26, 2025.
U. Elraaid, I. Badi, and M. B. Bouraima, “Identifying and addressing obstacles to project management office success in construction projects: An ahp approach,” Spectrum of Decision Making and Applications, vol. 1, no. 1, pp. 33–45, 2024.
N. Dhiman and M. Sharma, “Fuzzy logic inference system for identification and prevention of coronavirus (covid-19),” International Journal of Innovative Technology and Exploring Engineering, vol. 9, no. 6, pp. 2278–3075, 2020.
M. Mangla, N. Sharma et al., “Fuzzy modelling of clinical and epidemiological factors for covid-19,” 2020.
A. Awasthi and S. K. Srivastava, “A fuzzy soft set theoretic approach in decision making of covid-19 risk in different regions,” Communications in Mathematics and Applications, no. 2, p. 285, 2021.
P. D’Urso, L. De Giovanni, and V. Vitale, “Spatial robust fuzzy clustering of covid 19 time series based on b-splines,” Spatial Statistics, vol. 49, p. 100518, 2022.
E. Yogachi, V. Nasution, and G. Prakarsa, “Design and development of fuzzy logic application mamdani method in predicting the number of covid-19 positive cases in west java,” in IOP conference series: Materials Science and Engineering, vol. 1115, no. 1. IOP Publishing, 2021, p. 012031.
A. Behnood, E. M. Golafshani, and S. M. Hosseini, “Determinants of the infection rate of the covid-19 in the us using anfis and virus optimization algorithm (voa),” Chaos, Solitons & Fractals, vol. 139, p. 110051, 2020.
https://www.who.int/publications/i/item/10665-331495.
https://ehs.cornell.edu/campus-health-safety/occupational-health/covid-19/covid-19hierarchy-controls.
M. ur Rahman, M. Arfan, K. Shah, and J. G´omez-Aguilar, “Investigating a nonlinear dynamical model of covid-19 disease under fuzzy caputo, random and abc fractional order derivative,” Chaos, Solitons & Fractals, vol. 140, p. 110232, 2020.
R. S´ebastien, M. Olivier, and D. Andrei, “Use of fuzzy sets, aggregation operators and multi agent systems to simulate covid-19 transmission in a context of absence of barrier gestures and social distancing: application to an island region,” in 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020, pp. 2298–2305.
S. K. Sahoo, B. B. Choudhury, and P. R. Dhal, “A bibliometric analysis of material selection using mcdm methods: trends and insights,” Spectrum of Mechanical Engineering and Operational Research, vol. 1, no. 1, pp. 189–205, 2024.
S. G¨ul, “Fermatean fuzzy set extensions of saw, aras, and vikor with applications in covid19 testing laboratory selection problem,” Expert Systems, vol. 38, no. 8, p. e12769, 2021.
X. Deng and Z. Kong, “Humanitarian rescue scheme selection under the covid-19 crisis in china: Based on group decision-making method,” Symmetry, vol. 13, no. 4, p. 668, 2021.
V. Traneva, D. Mavrov, and S. Tranev, “Fuzzy two-factor analysis of covid-19 cases in europe,” in 2020 IEEE 10th International conference on Intelligent Systems (IS). IEEE, 2020, pp. 533–538.
M. A. Al-Qaness, A. A. Ewees, H. Fan, and M. Abd El Aziz, “Optimization method for forecasting confirmed cases of covid-19 in china,” Journal of Clinical Medicine, vol. 9, no. 3, p. 674, 2020.
N. Kumar and H. Kumar, “A novel hybrid fuzzy time series model for prediction of covid19 infected cases and deaths in india,” ISA transactions, vol. 124, pp. 69–81, 2022.
A. Adwibowo, “Fuzzy logic assisted covid 19 safety assessment of dental care,” Medrxiv, pp. 2020–06, 2020.
Y.-L. Fu and K.-C. Liang, “Fuzzy logic programming and adaptable design of medical products for the covid-19 anti-epidemic normalization,” Computer Methods and Programs in Biomedicine, vol. 197, p. 105762, 2020.
N. Hassan, T. Ahmad, A. Ashaari, S. R. Awang, S. S. Mamat, W. M. W. Mohamad, and A. A. A. Fuad, “A fuzzy graph approach analysis for covid-19 outbreak,” Results in Physics, vol. 25, p. 104267, 2021.
B. Ghosh and A. Biswas, “Status evaluation of provinces affected by covid-19: A qualitative assessment using fuzzy system,” Applied Soft Computing, vol. 109, p. 107540, 2021
A. Mardani, M. K. Saraji, A. R. Mishra, and P. Rani, “A novel extended approach under hesitant fuzzy sets to design a framework for assessing the key challenges of digital health interventions adoption during the covid-19 outbreak,” Applied Soft Computing, vol. 96, p.106613, 2020.
N. Kumar and S. Susan, “Particle swarm optimization of partitions and fuzzy order for fuzzy time series forecasting of covid-19,” Applied Soft Computing, vol. 110, p. 107611, 2021.
O. Castillo and P. Melin, “Forecasting of covid-19 time series for countries in the world based on a hybrid approach combining the fractal dimension and fuzzy logic,” Chaos, Solitons & Fractals, vol. 140, p. 110242, 2020.
O. Castillo. and P. Melin, “A new fuzzy fractal control approach of non-linear dynamic systems: The case of controlling the covid-19 pandemics,” Chaos, Solitons & Fractals, vol. 151, p. 111250, 2021.
L. Matrajt, J. Eaton, T. Leung, and E. R. Brown, “Vaccine optimization for covid-19: Who to vaccinate first?” Science Advances, vol. 7, no. 6, p. eabf1374, 2021.
F. Ahmad, S. Ahmad, and M. Zaindin, “Sustainable production and waste management policies for covid-19 medical equipment under uncertainty: A case study analysis,” Computers & Industrial Engineering, vol. 157, p. 107381, 2021.
O. Castillo and P. Melin, “A novel method for a covid-19 classification of countries based on an intelligent fuzzy fractal approach,” Healthcare, vol. 9, no. 2. MDPI, 2021, p. 196.
P. B. Dhandapani, D. Baleanu, J. Thippan, and V. Sivakumar, “On stiff, fuzzy ird-14 day average transmission model of covid-19 pandemic disease,” 2020.
S. Ahmad, S. Mehfuz, J. Beg, N. A. Khan, and A. H. Khan, “Withdrawn: Fuzzy cloud based covid-19 diagnosis assistant for identifying affected cases globally using mcdm,” 2021.
M. R. Mahmoudi, D. Baleanu, Z. Mansor, B. A. Tuan, and K.-H. Pho, “Fuzzy clustering method to compare the spread rate of covid-19 in the high risks countries,” Chaos, Solitons & Fractals, vol. 140, p. 110230, 2020.
https://www.cdc.gov/coronavirus/2019-ncov/vaccines/vaccine-benefits.html.
Guidance on developing a national deployment and vaccination plan for covid-19 vaccines, interim guidance, who, 2020.
A. Si, S. Das, and S. Kar, “Picture fuzzy set-based decision-making approach using dempster–shafer theory of evidence and grey relation analysis and its application in covid- 19 medicine selection,” Soft Computing, vol. 27, no. 6, pp. 3327–3341, 2023.
A. Si., S. Das, and S. Kar, “Preferred hospitalization of covid-19 patients using intuitionistic fuzzy set-based matching approach,” Granular Computing, vol. 8, no. 3, pp. 525–549, 2023.
A. A. Sadat Asl, M. M. Ershadi, S. Sotudian, X. Li, and S. Dick, “Fuzzy expert systems for prediction of icu admission in patients with covid-19,” Intelligent Decision Technologies, vol. 16, no. 1, pp. 159–168, 2022.
M. A. Elleuch, A. B. Hassena, M. Abdelhedi, and F. S. Pinto, “Real-time prediction of covid19 patients health situations using artificial neural networks and fuzzy interval mathematical modeling,” Applied Soft Computing, vol. 110, p. 107643, 2021.
M. Jayalakshmi, L. Garg, K. Maharajan, K. Jayakumar, K. Srinivasan, A. K. Bashir, and K. Ramesh, “Fuzzy logic-based health monitoring system for covid’19 patients,” Computers, Materials and Continua, vol. 67, no. 2, pp. 2431–2447, 2021.
F. S. Yildirim, M. Sayan, T. Sanlidag, B. Uzun, D. U. Ozsahin, and I. Ozsahin, “Comparative evaluation of the treatment of covid-19 with multicriteria decision-making techniques,” Journal of Healthcare Engineering, vol. 2021, no. 1, p. 8864522, 2021.
S. Geetha, S. Narayanamoorthy, T. Manirathinam, and D. Kang, “Fuzzy case-based reasoning approach for finding covid-19 patients priority in hospitals at source shortage period,” Expert Systems with Applications, vol. 178, p. 114997, 2021.
Z. Ren, H. Liao, and Y. Liu, “Generalized z-numbers with hesitant fuzzy linguistic information and its application to medicine selection for the patients with mild symptoms of the covid-19,” Computers & Industrial Engineering, vol. 145, p. 106517, 2020.
A. R. Mishra, P. Rani, R. Krishankumar, K. Ravichandran, and S. Kar, “An extended fuzzy decision-making framework using hesitant fuzzy sets for the drug selection to treat the mild symptoms of coronavirus disease 2019 (covid-19),” Applied Soft Computing, vol. 103, p. 107155, 2021.
N. E. P. Babroudi, K. Sabri-Laghaie, and N. G. Ghoushchi, “Re-evaluation of the healthcare service quality criteria for the covid-19 pandemic: Z-number fuzzy cognitive map,” Applied Soft Computing, vol. 112, p. 107775, 2021.
R. Goswami, K. Roy, S. Dutta, K. Ray, S. Sarkar, K. Brahmachari, M. K. Nanda, M. Mainuddin, H. Banerjee, J. Timsina et al., “Multi-faceted impact and outcome of covid-19 on smallholder agricultural systems: Integrating qualitative research and fuzzy cognitive mapping to explore resilient strategies,” Agricultural Systems, vol. 189, p. 103051, 2021.
T. Chen and Y.-C. Wang, “A calibrated piecewise-linear fgm approach for travel destination recommendation during the covid-19 pandemic,” Applied Soft Computing, vol. 109, p.107535, 2021.
R. Mahapatra, S. Samanta, M. Pal, J.-G. Lee, S. K. Khan, U. Naseem, and R. S. Bhadoria, “Colouring of covid-19 affected region based on fuzzy directed graphs,” Computers, Materials & Continua, vol. 68, no. 1, pp. 1219–1233, 2021.
B. Tang, S. Guo, M. Yeboah, Z. Wang, and S. Cheng, “Quality evaluation of online courses during covid-19 pandemic based on integrated fce-ahp method,” Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1487–1498, 2021.
M. Nicola, Z. Alsafi, C. Sohrabi, A. Kerwan, A. Al-Jabir, C. Iosifidis, M. Agha, and R. Agha, “The socio-economic implications of the coronavirus pandemic (covid-19): Areview,” International Journal of Surgery, vol. 78, pp. 185–193, 2020.
N. Alkan and C. Kahraman, “Evaluation of government strategies against covid-19 pandemic using q-rung orthopair fuzzy topsis method,” Applied Soft Computing, vol. 110, p. 107653, 2021.
S. M. Hosseini, M. M. Paydar, and M. Hajiaghaei-Keshteli, “Recovery solutions for ecotourism centers during the covid-19 pandemic: Utilizing fuzzy dematel and fuzzy vikor methods,” Expert Systems with Applications, vol. 185, p. 115594, 2021.
F. Ecer and D. Pamucar, “Marcos technique under intuitionistic fuzzy environment for determining the covid-19 pandemic performance of insurance companies in terms of healthcare services,” Applied Soft Computing, vol. 104, p. 107199, 2021.
D. Nazarov, “Fuzzy model of digital assessment of donation systems’ level in covid-19,” in 2nd International Scientific and Practical Conference “Modern Management Trends and the Digital Economy: from Regional Development to Global Economic Growth,” Atlantis Press, 2020, pp. 1201–1206.
S. Ashraf and S. Abdullah, “Emergency decision support modeling for covid-19 based on spherical fuzzy information,” International Journal of Intelligent Systems, vol. 35, no. 11, pp. 1601–1645, 2020.
A. Bahuguna, D. Yadav, A. Senapati, and B. N. Saha, “A unified deep neuro-fuzzy approach for covid-19 twitter sentiment classification,” Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4587–4597, 2022.
E. C， akır, M. A. Ta，s, and Z. Ulukan, “Spherical bipolar fuzzy weighted multi-facility location modeling for mobile covid-19 vaccination clinics,” Journal of Intelligent & Fuzzy Systems, vol. 42, no. 1, pp. 237–250, 2021.
T. T. Le, J. P. Cramer, R. Chen, and S. Mayhew, “Evolution of the covid-19 vaccine development landscape,” Nat Rev Drug Discov, vol. 19, no. 10, pp. 667–668, 2020.
https://www.jetex.com/global-vaccine-logistics.
S. K. Sahoo and B. B. Choudhury, “A review on smart robotic wheelchairs with advancing mobility and independence for individuals with disabilities,” Journal of Decision Analytics and Intelligent Computing, vol. 3, no. 1, pp. 221–242, 2023.
A. d. C. Mungu´ıa-L´opez and J. M. Ponce-Ortega, “Fair allocation of potential covid-19 vaccines using an optimization-based strategy,” Process Integration and Optimization for Sustainability, vol. 5, no. 1, pp. 3–12, 2021.
S. T. Alam, S. Ahmed, S. M. Ali, S. Sarker, G. Kabir et al., “Challenges to covid-19 vaccine supply chain: Implications for sustainable development goals,” International Journal of Production Economics, vol. 239, p. 108193, 2021.
https://unctad.org/system/files/official-document/tdr2020 en.pdf.
https://www.cafamerica.org/wp-content/uploads/cafamerica cv19 volume 7 report final1.pdf.
R. M. Burke, “Active monitoring of persons exposed to patients with confirmed covid-19— united states, january–february 2020,” MMWR. Morbidity and mortality weekly report, vol. 69, 2020 W. E. Wei, “Presymptomatic transmission of sars-cov-2—singapore, january 23–march 16, 2020,” MMWR. Morbidity and mortality weekly report, vol. 69, 2020. https://covid-19.who.int/table.
S. Kousar, A. Ansar, N. Kausar, and G. Freen, “Multi-criteria decision-making for smog mitigation: a comprehensive analysis of health, economic, and ecological impacts,” Spectrum of Decision Making and Applications, vol. 2, no. 1, pp. 53–67, 2025. L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965.
L. A. Zadeh., “The concept of a linguistic variable and its application to approximate reasoning—i,” Information Sciences, vol. 8, no. 3, pp. 199–249, 1975.
K. T. Atanassov and K. T. Atanassov, Intuitionistic fuzzy sets. Springer, 1999.
K. T. Atanassov. and K. T. Atanassov, “Interval valued intuitionistic fuzzy sets,” Springer, 1999.
J.-S. Jang, “Anfis: adaptive-network-based fuzzy inference system,” IEEE Transactions on Systems, man, and Cybernetics, vol. 23, no. 3, pp. 665–685, 1993.
R. R. Yager, “On the theory of bags,” International Journal of General System, vol. 13, no. 1, pp. 23–37, 1986.
V. Torra, “Hesitant fuzzy sets,” International Journal of Intelligent Systems, vol. 25, no. 6, pp. 529–539, 2010.
A. SI and S. Das, “Mfs: Dynamic decision making approach to select optimal alternative in the presence of uncertainty,” Iranian Journal of Fuzzy Systems, vol. 22, no. 2, pp. 81–95, 2025.
J. A. Goguen, “L-fuzzy sets,” Journal of Mathematical Analysis and Applications, vol. 18, no. 1, pp. 145–174, 1967.
S. Miyamoto, “Fuzzy multisets and their generalizations,” in Workshop on membrane computing. Springer, 2000, pp. 225–235.
G. Wei, “Picture fuzzy cross-entropy for multiple attribute decision making problems,” Journal of Business Economics and Management, vol. 17, no. 4, pp. 491–502, 2016.
A. Si, S. Das, and S. Kar, “An approach to rank picture fuzzy numbers for decision making problems,” Decision Making: Applications in Management and Engineering, vol. 2, no. 2, pp. 54–64, 2019.O. Montiel, O. Castillo, P. Melin, and R. Sepulveda, “Mediative fuzzy logic: a new approach for contradictory knowledge management,” Soft Computing, vol. 12, no. 3, pp. 251–256, 2008.
M. Keshavarz-Ghorabaee, M. Amiri, M. Hashemi-Tabatabaei, E. K. Zavadskas, and A. Kaklauskas, “A new decision-making approach based on fermatean fuzzy sets and waspas for green construction supplier evaluation,” Mathematics, vol. 8, no. 12, p. 2202, 2020.
D. Tešić and D. Marinković, “Application of fermatean fuzzy weight operators and mcdm model dibr-dibr ii-nwbm-bm for efficiency-based selection of a complex combat system,” Journal of Decision Analytics and Intelligent Computing, vol. 3, no. 1, pp. 243–256, 2023.
T. M. Al-shami, H. Z. Ibrahim, A. Azzam, and A. I. EL-Maghrabi, “Sr-fuzzy sets and their weighted aggregated operators in application to decision-making,” Journal of Function Spaces, vol. 2022, no. 1, p. 3653225, 2022.
T. M. Al-shami, “(2, 1)-fuzzy sets: properties, weighted aggregated operators and their applications to multi-criteria decision-making methods,” Complex & Intelligent Systems, vol. 9, no. 2, pp. 1687–1705, 2023.
S. Ashraf, S. Abdullah, M. Aslam, M. Qiyas, and M. A. Kutbi, “Spherical fuzzy sets and its representation of spherical fuzzy t-norms and t-conorms,” Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 6089–6102, 2019.
A. Hussain and K. Ullah, “An intelligent decision support system for spherical fuzzy sugenoweber aggregation operators and real-life applications,” Spectrum of Mechanical Engineering and Operational Research, vol. 1, no. 1, pp. 177–188, 2024.
T. M. Al-shami, “An improvement of rough sets’ accuracy measure using containment neighborhoods with a medical application,” Information Sciences, vol. 569, pp. 110–124, 2021.
T. M. Al-Shami, “Maximal rough neighborhoods with a medical application,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 12, pp. 16 373–16 384, 2023.
Z.Wu and J. M. McGoogan, “Characteristics of and important lessons from the coronavirus disease 2019 (covid-19) outbreak in china: summary of a report of 72 314 cases from the chinese center for disease control and prevention,” Jama, vol. 323, no. 13, pp. 1239–1242, 2020.
S. Dey, R. Bhattacharya, S. Malakar, S. Mirjalili, and R. Sarkar, “Choquet fuzzy integralbased classifier ensemble technique for covid-19 detection,” Computers in Biology and Medicine, vol. 135, p. 104585, 2021.
S. Chakraborty and K. Mali, “Sufmofpa: A superpixel and meta-heuristic based fuzzy image segmentation approach to explicate covid-19 radiological images,” Expert Systems with Applications, vol. 167, p. 114142, 2021.
W. M. Shaban, A. H. Rabie, A. I. Saleh, and M. Abo-Elsoud, “Detecting covid-19 patients based on fuzzy inference engine and deep neural network,” Applied Soft Computing, vol. 99, pp. 106906, 2021.
T. Nivethitha, S. K. Palanisamy, K. M. Prakash, and K. Jeevitha, “Comparative study of ann and fuzzy classifier for forecasting electrical activity of heart to diagnose covid-19,” Materials Today: Proceedings, vol. 45, pp. 2293–2305, 2021.
A. M. Kozae, M. Shokry, and M. Omran, “Intuitionistic fuzzy set and its application in corona covid-19,” Applied and Computational Mathematics, vol. 9, no. 5, pp. 146–154, 2020.
M. Shatnawi, A. Shatnawi, Z. AlShara, and G. Husari, “Symptoms-based fuzzy-logic approach for covid-19 diagnosis,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 4, pp. 444–452, 2021.
D. Painuli, D. Mishra, S. Bhardwaj, and M. Aggarwal, “Fuzzy rule based system to predict covid19-a deadly virus,” Way, vol. 3, no. 4, p. 5, 2020.
P. U. Ejodamen and V. E. Ekong, “A fuzzy expert system model for the determination of coronavirus disease risk,” Cardiovascular Diseases, vol. 3, p. 4, 2021.
M. To˘ga，car, B. Ergen, and Z. C¨omert, “Covid-19 detection using deep learning models to exploit social mimic optimization and structured chest x-ray images using fuzzy color and stacking approaches,” Computers in Biology and Medicine, vol. 121, p. 103805, 2020.
P. Melin, J. C. Monica, D. Sanchez, and O. Castillo, “Multiple ensemble neural network models with fuzzy response aggregation for predicting covid-19 time series: the case of mexico,” Healthcare, vol. 8, no. 2. MDPI, 2020, p. 181.
P. Melin, J. C. Monica, D. Sanchez, and O. Castillo, “Multiple ensemble neural network models with fuzzy response aggregation for predicting covid-19 time series: the case of mexico,” Healthcare, vol. 8, no. 2. MDPI, 2020, p. 181.
P. Melin, J. C. Monica, D. Sanchez, and O. Castillo, “Multiple ensemble neural network models with fuzzy response aggregation for predicting covid-19 time series: the case of mexico,” Healthcare, vol. 8, no. 2. MDPI, 2020, p. 181.
A. Alderremy, J. G´omez-Aguilar, S. Aly, and K. M. Saad, “A fuzzy fractional model of coronavirus (covid-19) and its study with legendre spectral method,” Results in Physics, vol. 21, p. 103773, 2021.
N. Ghorui, A. Ghosh, S. P. Mondal, M. Y. Bajuri, A. Ahmadian, S. Salahshour, and M. Ferrara, “Identification of dominant risk factor involved in spread of covid-19 using hesitant fuzzy mcdm methodology,” Results in Physics, vol. 21, p. 103811, 2021.
A. Biswas, K. H. Gazi, P. M. Sankar, and A. Ghosh, “A decision-making framework for sustainable highway restaurant site selection: Ahp-topsis approach based on the fuzzy numbers,” Spectrum of Operational Research, vol. 2, no. 1, pp. 1–26, 2025.
U. Elraaid, I. Badi, and M. B. Bouraima, “Identifying and addressing obstacles to project management office success in construction projects: An ahp approach,” Spectrum of Decision Making and Applications, vol. 1, no. 1, pp. 33–45, 2024.
N. Dhiman and M. Sharma, “Fuzzy logic inference system for identification and prevention of coronavirus (covid-19),” International Journal of Innovative Technology and Exploring Engineering, vol. 9, no. 6, pp. 2278–3075, 2020.
M. Mangla, N. Sharma et al., “Fuzzy modelling of clinical and epidemiological factors for covid-19,” 2020.
A. Awasthi and S. K. Srivastava, “A fuzzy soft set theoretic approach in decision making of covid-19 risk in different regions,” Communications in Mathematics and Applications, no. 2, p. 285, 2021.
P. D’Urso, L. De Giovanni, and V. Vitale, “Spatial robust fuzzy clustering of covid 19 time series based on b-splines,” Spatial Statistics, vol. 49, p. 100518, 2022.
E. Yogachi, V. Nasution, and G. Prakarsa, “Design and development of fuzzy logic application mamdani method in predicting the number of covid-19 positive cases in west java,” in IOP conference series: Materials Science and Engineering, vol. 1115, no. 1. IOP Publishing, 2021, p. 012031.
A. Behnood, E. M. Golafshani, and S. M. Hosseini, “Determinants of the infection rate of the covid-19 in the us using anfis and virus optimization algorithm (voa),” Chaos, Solitons & Fractals, vol. 139, p. 110051, 2020.
https://www.who.int/publications/i/item/10665-331495.
https://ehs.cornell.edu/campus-health-safety/occupational-health/covid-19/covid-19hierarchy-controls.
M. ur Rahman, M. Arfan, K. Shah, and J. G´omez-Aguilar, “Investigating a nonlinear dynamical model of covid-19 disease under fuzzy caputo, random and abc fractional order derivative,” Chaos, Solitons & Fractals, vol. 140, p. 110232, 2020.
R. S´ebastien, M. Olivier, and D. Andrei, “Use of fuzzy sets, aggregation operators and multi agent systems to simulate covid-19 transmission in a context of absence of barrier gestures and social distancing: application to an island region,” in 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020, pp. 2298–2305.
S. K. Sahoo, B. B. Choudhury, and P. R. Dhal, “A bibliometric analysis of material selection using mcdm methods: trends and insights,” Spectrum of Mechanical Engineering and Operational Research, vol. 1, no. 1, pp. 189–205, 2024.
S. G¨ul, “Fermatean fuzzy set extensions of saw, aras, and vikor with applications in covid19 testing laboratory selection problem,” Expert Systems, vol. 38, no. 8, p. e12769, 2021.
X. Deng and Z. Kong, “Humanitarian rescue scheme selection under the covid-19 crisis in china: Based on group decision-making method,” Symmetry, vol. 13, no. 4, p. 668, 2021.
V. Traneva, D. Mavrov, and S. Tranev, “Fuzzy two-factor analysis of covid-19 cases in europe,” in 2020 IEEE 10th International conference on Intelligent Systems (IS). IEEE, 2020, pp. 533–538.
M. A. Al-Qaness, A. A. Ewees, H. Fan, and M. Abd El Aziz, “Optimization method for forecasting confirmed cases of covid-19 in china,” Journal of Clinical Medicine, vol. 9, no. 3, p. 674, 2020.
N. Kumar and H. Kumar, “A novel hybrid fuzzy time series model for prediction of covid19 infected cases and deaths in india,” ISA transactions, vol. 124, pp. 69–81, 2022.
A. Adwibowo, “Fuzzy logic assisted covid 19 safety assessment of dental care,” Medrxiv, pp. 2020–06, 2020.
Y.-L. Fu and K.-C. Liang, “Fuzzy logic programming and adaptable design of medical products for the covid-19 anti-epidemic normalization,” Computer Methods and Programs in Biomedicine, vol. 197, p. 105762, 2020.
N. Hassan, T. Ahmad, A. Ashaari, S. R. Awang, S. S. Mamat, W. M. W. Mohamad, and A. A. A. Fuad, “A fuzzy graph approach analysis for covid-19 outbreak,” Results in Physics, vol. 25, p. 104267, 2021.
B. Ghosh and A. Biswas, “Status evaluation of provinces affected by covid-19: A qualitative assessment using fuzzy system,” Applied Soft Computing, vol. 109, p. 107540, 2021
A. Mardani, M. K. Saraji, A. R. Mishra, and P. Rani, “A novel extended approach under hesitant fuzzy sets to design a framework for assessing the key challenges of digital health interventions adoption during the covid-19 outbreak,” Applied Soft Computing, vol. 96, p.106613, 2020.
N. Kumar and S. Susan, “Particle swarm optimization of partitions and fuzzy order for fuzzy time series forecasting of covid-19,” Applied Soft Computing, vol. 110, p. 107611, 2021.
O. Castillo and P. Melin, “Forecasting of covid-19 time series for countries in the world based on a hybrid approach combining the fractal dimension and fuzzy logic,” Chaos, Solitons & Fractals, vol. 140, p. 110242, 2020.
O. Castillo. and P. Melin, “A new fuzzy fractal control approach of non-linear dynamic systems: The case of controlling the covid-19 pandemics,” Chaos, Solitons & Fractals, vol. 151, p. 111250, 2021.
L. Matrajt, J. Eaton, T. Leung, and E. R. Brown, “Vaccine optimization for covid-19: Who to vaccinate first?” Science Advances, vol. 7, no. 6, p. eabf1374, 2021.
F. Ahmad, S. Ahmad, and M. Zaindin, “Sustainable production and waste management policies for covid-19 medical equipment under uncertainty: A case study analysis,” Computers & Industrial Engineering, vol. 157, p. 107381, 2021.
O. Castillo and P. Melin, “A novel method for a covid-19 classification of countries based on an intelligent fuzzy fractal approach,” Healthcare, vol. 9, no. 2. MDPI, 2021, p. 196.
P. B. Dhandapani, D. Baleanu, J. Thippan, and V. Sivakumar, “On stiff, fuzzy ird-14 day average transmission model of covid-19 pandemic disease,” 2020.
S. Ahmad, S. Mehfuz, J. Beg, N. A. Khan, and A. H. Khan, “Withdrawn: Fuzzy cloud based covid-19 diagnosis assistant for identifying affected cases globally using mcdm,” 2021.
M. R. Mahmoudi, D. Baleanu, Z. Mansor, B. A. Tuan, and K.-H. Pho, “Fuzzy clustering method to compare the spread rate of covid-19 in the high risks countries,” Chaos, Solitons & Fractals, vol. 140, p. 110230, 2020.
https://www.cdc.gov/coronavirus/2019-ncov/vaccines/vaccine-benefits.html.
Guidance on developing a national deployment and vaccination plan for covid-19 vaccines, interim guidance, who, 2020.
A. Si, S. Das, and S. Kar, “Picture fuzzy set-based decision-making approach using dempster–shafer theory of evidence and grey relation analysis and its application in covid- 19 medicine selection,” Soft Computing, vol. 27, no. 6, pp. 3327–3341, 2023.
A. Si., S. Das, and S. Kar, “Preferred hospitalization of covid-19 patients using intuitionistic fuzzy set-based matching approach,” Granular Computing, vol. 8, no. 3, pp. 525–549, 2023.
A. A. Sadat Asl, M. M. Ershadi, S. Sotudian, X. Li, and S. Dick, “Fuzzy expert systems for prediction of icu admission in patients with covid-19,” Intelligent Decision Technologies, vol. 16, no. 1, pp. 159–168, 2022.
M. A. Elleuch, A. B. Hassena, M. Abdelhedi, and F. S. Pinto, “Real-time prediction of covid19 patients health situations using artificial neural networks and fuzzy interval mathematical modeling,” Applied Soft Computing, vol. 110, p. 107643, 2021.
M. Jayalakshmi, L. Garg, K. Maharajan, K. Jayakumar, K. Srinivasan, A. K. Bashir, and K. Ramesh, “Fuzzy logic-based health monitoring system for covid’19 patients,” Computers, Materials and Continua, vol. 67, no. 2, pp. 2431–2447, 2021.
F. S. Yildirim, M. Sayan, T. Sanlidag, B. Uzun, D. U. Ozsahin, and I. Ozsahin, “Comparative evaluation of the treatment of covid-19 with multicriteria decision-making techniques,” Journal of Healthcare Engineering, vol. 2021, no. 1, p. 8864522, 2021.
S. Geetha, S. Narayanamoorthy, T. Manirathinam, and D. Kang, “Fuzzy case-based reasoning approach for finding covid-19 patients priority in hospitals at source shortage period,” Expert Systems with Applications, vol. 178, p. 114997, 2021.
Z. Ren, H. Liao, and Y. Liu, “Generalized z-numbers with hesitant fuzzy linguistic information and its application to medicine selection for the patients with mild symptoms of the covid-19,” Computers & Industrial Engineering, vol. 145, p. 106517, 2020.
A. R. Mishra, P. Rani, R. Krishankumar, K. Ravichandran, and S. Kar, “An extended fuzzy decision-making framework using hesitant fuzzy sets for the drug selection to treat the mild symptoms of coronavirus disease 2019 (covid-19),” Applied Soft Computing, vol. 103, p. 107155, 2021.
N. E. P. Babroudi, K. Sabri-Laghaie, and N. G. Ghoushchi, “Re-evaluation of the healthcare service quality criteria for the covid-19 pandemic: Z-number fuzzy cognitive map,” Applied Soft Computing, vol. 112, p. 107775, 2021.
R. Goswami, K. Roy, S. Dutta, K. Ray, S. Sarkar, K. Brahmachari, M. K. Nanda, M. Mainuddin, H. Banerjee, J. Timsina et al., “Multi-faceted impact and outcome of covid-19 on smallholder agricultural systems: Integrating qualitative research and fuzzy cognitive mapping to explore resilient strategies,” Agricultural Systems, vol. 189, p. 103051, 2021.
T. Chen and Y.-C. Wang, “A calibrated piecewise-linear fgm approach for travel destination recommendation during the covid-19 pandemic,” Applied Soft Computing, vol. 109, p.107535, 2021.
R. Mahapatra, S. Samanta, M. Pal, J.-G. Lee, S. K. Khan, U. Naseem, and R. S. Bhadoria, “Colouring of covid-19 affected region based on fuzzy directed graphs,” Computers, Materials & Continua, vol. 68, no. 1, pp. 1219–1233, 2021.
B. Tang, S. Guo, M. Yeboah, Z. Wang, and S. Cheng, “Quality evaluation of online courses during covid-19 pandemic based on integrated fce-ahp method,” Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1487–1498, 2021.
M. Nicola, Z. Alsafi, C. Sohrabi, A. Kerwan, A. Al-Jabir, C. Iosifidis, M. Agha, and R. Agha, “The socio-economic implications of the coronavirus pandemic (covid-19): Areview,” International Journal of Surgery, vol. 78, pp. 185–193, 2020.
N. Alkan and C. Kahraman, “Evaluation of government strategies against covid-19 pandemic using q-rung orthopair fuzzy topsis method,” Applied Soft Computing, vol. 110, p. 107653, 2021.
S. M. Hosseini, M. M. Paydar, and M. Hajiaghaei-Keshteli, “Recovery solutions for ecotourism centers during the covid-19 pandemic: Utilizing fuzzy dematel and fuzzy vikor methods,” Expert Systems with Applications, vol. 185, p. 115594, 2021.
F. Ecer and D. Pamucar, “Marcos technique under intuitionistic fuzzy environment for determining the covid-19 pandemic performance of insurance companies in terms of healthcare services,” Applied Soft Computing, vol. 104, p. 107199, 2021.
D. Nazarov, “Fuzzy model of digital assessment of donation systems’ level in covid-19,” in 2nd International Scientific and Practical Conference “Modern Management Trends and the Digital Economy: from Regional Development to Global Economic Growth,” Atlantis Press, 2020, pp. 1201–1206.
S. Ashraf and S. Abdullah, “Emergency decision support modeling for covid-19 based on spherical fuzzy information,” International Journal of Intelligent Systems, vol. 35, no. 11, pp. 1601–1645, 2020.
A. Bahuguna, D. Yadav, A. Senapati, and B. N. Saha, “A unified deep neuro-fuzzy approach for covid-19 twitter sentiment classification,” Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4587–4597, 2022.
E. C， akır, M. A. Ta，s, and Z. Ulukan, “Spherical bipolar fuzzy weighted multi-facility location modeling for mobile covid-19 vaccination clinics,” Journal of Intelligent & Fuzzy Systems, vol. 42, no. 1, pp. 237–250, 2021.
T. T. Le, J. P. Cramer, R. Chen, and S. Mayhew, “Evolution of the covid-19 vaccine development landscape,” Nat Rev Drug Discov, vol. 19, no. 10, pp. 667–668, 2020.
https://www.jetex.com/global-vaccine-logistics.
S. K. Sahoo and B. B. Choudhury, “A review on smart robotic wheelchairs with advancing mobility and independence for individuals with disabilities,” Journal of Decision Analytics and Intelligent Computing, vol. 3, no. 1, pp. 221–242, 2023.
A. d. C. Mungu´ıa-L´opez and J. M. Ponce-Ortega, “Fair allocation of potential covid-19 vaccines using an optimization-based strategy,” Process Integration and Optimization for Sustainability, vol. 5, no. 1, pp. 3–12, 2021.
S. T. Alam, S. Ahmed, S. M. Ali, S. Sarker, G. Kabir et al., “Challenges to covid-19 vaccine supply chain: Implications for sustainable development goals,” International Journal of Production Economics, vol. 239, p. 108193, 2021.
https://unctad.org/system/files/official-document/tdr2020 en.pdf.
https://www.cafamerica.org/wp-content/uploads/cafamerica cv19 volume 7 report final1.pdf.
Published
2025-06-30
How to Cite
SI, Amalendu; DAS, Sujit; KAR, Samarjit.
A Brief Review of Application of Fuzzy Techniques Towards COVID-19 Pandemic Analysis.
Yugoslav Journal of Operations Research, [S.l.], june 2025.
ISSN 2334-6043.
Available at: <https://yujor.fon.bg.ac.rs/index.php/yujor/article/view/1345>. Date accessed: 07 july 2025.
doi: https://doi.org/10.2298/YJOR240915012S.
Issue
Section
Review Articles
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
