Performance of fuzzy multi-criteria decision analysis of emergency system in COVID-19 pandemic. An extensive narrative review

The actual coronavirus disease 2019 (COVID-19) pandemic has led to the limit of emergency systems worldwide, leading to the collapse of health systems, police, first responders, as well as other areas. Various ways of dealing with this world crisis have been proposed from many aspects, with fuzzy mu...

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Autores:
Clemente-Suárez, Vicente Javier
Navarro Jiménez, Eduardo
Ruisoto, Pablo
Dalamitros, Athanasios
Beltrán Velasco, Ana Isabel
Hormeno-Holgado, Alberto Joaquin
Laborde Cardenas, Carmen Cecilia
Tornero Aguilera, José Francisco
Tipo de recurso:
Article of journal
Fecha de publicación:
2021
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/8407
Acceso en línea:
https://hdl.handle.net/11323/8407
https://doi.org/10.3390/ijerph18105208
https://repositorio.cuc.edu.co/
Palabra clave:
Fuzzy decision analysis
Decision making
Uncertainty
Multi-criteria
Emergency
COVID-19
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_a78ac17af3e10247c45f3cb5947ab41a
oai_identifier_str oai:repositorio.cuc.edu.co:11323/8407
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Performance of fuzzy multi-criteria decision analysis of emergency system in COVID-19 pandemic. An extensive narrative review
title Performance of fuzzy multi-criteria decision analysis of emergency system in COVID-19 pandemic. An extensive narrative review
spellingShingle Performance of fuzzy multi-criteria decision analysis of emergency system in COVID-19 pandemic. An extensive narrative review
Fuzzy decision analysis
Decision making
Uncertainty
Multi-criteria
Emergency
COVID-19
title_short Performance of fuzzy multi-criteria decision analysis of emergency system in COVID-19 pandemic. An extensive narrative review
title_full Performance of fuzzy multi-criteria decision analysis of emergency system in COVID-19 pandemic. An extensive narrative review
title_fullStr Performance of fuzzy multi-criteria decision analysis of emergency system in COVID-19 pandemic. An extensive narrative review
title_full_unstemmed Performance of fuzzy multi-criteria decision analysis of emergency system in COVID-19 pandemic. An extensive narrative review
title_sort Performance of fuzzy multi-criteria decision analysis of emergency system in COVID-19 pandemic. An extensive narrative review
dc.creator.fl_str_mv Clemente-Suárez, Vicente Javier
Navarro Jiménez, Eduardo
Ruisoto, Pablo
Dalamitros, Athanasios
Beltrán Velasco, Ana Isabel
Hormeno-Holgado, Alberto Joaquin
Laborde Cardenas, Carmen Cecilia
Tornero Aguilera, José Francisco
dc.contributor.author.spa.fl_str_mv Clemente-Suárez, Vicente Javier
Navarro Jiménez, Eduardo
Ruisoto, Pablo
Dalamitros, Athanasios
Beltrán Velasco, Ana Isabel
Hormeno-Holgado, Alberto Joaquin
Laborde Cardenas, Carmen Cecilia
Tornero Aguilera, José Francisco
dc.subject.spa.fl_str_mv Fuzzy decision analysis
Decision making
Uncertainty
Multi-criteria
Emergency
COVID-19
topic Fuzzy decision analysis
Decision making
Uncertainty
Multi-criteria
Emergency
COVID-19
description The actual coronavirus disease 2019 (COVID-19) pandemic has led to the limit of emergency systems worldwide, leading to the collapse of health systems, police, first responders, as well as other areas. Various ways of dealing with this world crisis have been proposed from many aspects, with fuzzy multi-criteria decision analysis being a method that can be applied to a wide range of emergency systems and professional groups, aiming to confront several associated issues and challenges. The purpose of this critical review was to discuss the basic principles, present current applications during the first pandemic wave, and propose future implications of this methodology. For this purpose, both primary sources, such as scientific articles, and secondary ones, such as bibliographic indexes, web pages, and databases, were used. The main search engines were PubMed, SciELO, and Google Scholar. The method was a systematic literature review of the available literature regarding the performance of the fuzzy multi-criteria decision analysis of emergency systems in the COVID-19 pandemic. The results of this study highlight the importance of the fuzzy multi-criteria decision analysis method as a beneficial tool for healthcare workers and first responders’ emergency professionals to face this pandemic as well as to manage the created uncertainty and its related risks.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-06-24T14:42:35Z
dc.date.available.none.fl_str_mv 2021-06-24T14:42:35Z
dc.date.issued.none.fl_str_mv 2021-05-14
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
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dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
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dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
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dc.identifier.issn.spa.fl_str_mv 1660-4601
1661-7827
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/8407
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.3390/ijerph18105208
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
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Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/8407
https://doi.org/10.3390/ijerph18105208
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv 1. Clemente-Suárez, V.J.; Dalamitros, A.A.; Beltran-Velasco, A.I.; Mielgo-Ayuso, J.; Tornero-Aguilera, J.F. Social and psychophysiological consequences of the COVID-19 pandemic: An extensive literature review. Front. Psychol. 2020, 11, 580225. [CrossRef]
2. Clemente-Suárez, V.J.; Hormeño-Holgado, A.; Jiménez, M.; Benitez-Agudelo, J.C.; Navarro-Jiménez, E.; Perez-Palencia, N.; Maestre-Serrano, R.; Laborde-Cárdenas, C.C.; Tornero-Aguilera, J.F. Dynamics of population immunity due to the herd effect in the COVID-19 pandemic. Vaccines 2020, 8, 236. [CrossRef]
3. Solis, J.; Franco-Paredes, C.; Henao-Martínez, A.F.; Krsak, M.; Zimmer, S.M. Structural vulnerability in the US revealed in three waves of COVID-19. Am. J. Trop. Med. Hig. 2020, 103, 25–27. [CrossRef]
4. Conti, P.; Caraffa, A.; Gallenga, C.E.; Kritas, S.K.; Frydas, I.; Younes, A.; Di Emidio, P.; Tetè, G.; Pregliasco, F.; Ronconi, G. The British variant of the new coronavirus-19 (Sars-Cov-2) should not create a vaccine problem. J. Biol. Regul. Homeost. Agents 2021, 35, 1–4.
5. Siu, G.K.-H.; Lee, L.-K.; Leung, K.S.-S.; Leung, J.S.-L.; Ng, T.T.-L.; Chan, C.T.-M.; Tam, K.K.-G.; Lao, H.-Y.; Wu, A.K.-L.; Yau, M.C.-Y.; et al. Will a new clade of SARS-CoV-2 imported into the community spark a fourth wave of the COVID-19 outbreak in Hong Kong? Emerg. Microbes Infect. 2020, 9, 2497–2500. [CrossRef] [PubMed]
6. Tsang, H.F.; Chan, L.W.C.; Cho, W.C.S.; Yu, A.C.S.; Yim, A.K.Y.; Chan, A.K.C.; Wong, S.C.C. An Update on COVID-19 Pandemic: The Epidemiology, Pathogenesis, Prevention and Treatment Strategies. Expert Rev. Anti-Infect. Ther. 2021, 29, 1–12. [CrossRef]
7. Pamuˇcar, D.; Žižovi´c, M.; Marinkovi´c, D.; Doljanica, D.; Jovanovi´c, S.V.; Brzakovi´c, P. Development of a multi-criteria model for sustainable reorganization of a healthcare system in an emergency situation caused by the COVID-19 pandemic. Sustainability 2020, 12, 7504. [CrossRef]
8. Yildirim, F.S.; Sayan, M.; Sanlidag, T.; Uzun, B.; Ozsahin, D.U.; Ozsahin, I. Comparative evaluation of the treatment of COVID-19 with multicriteria decision-making techniques. J. Healthc. Eng. 2021, 2021, 8864522. [CrossRef]
9. Abdullah, L. Fuzzy Multi Criteria Decision Making and its Applications: A Brief Review of Category. Procedia Soc. Behav. Sci. 2013, 97, 131–136. [CrossRef]
10. Carlsson, C.; Fullér, R. Fuzzy multiple criteria decision making: Recent developments. Fuzzy Set Syst. 1996, 78, 139–153. [CrossRef]
11. Vakaramoko Diaby, V.; Goeree, R. How to use multi-criteria decision analysis methods for reimbursement decision-making in healthcare: A step-by-step guide. Expert Rev. Pharm. Outcomes Res. 2014, 14, 81–99.
12. Saaty, R.W. The Analytic Hierarchy process—What it is and how it is used. Math. Model. 1987, 9, 161–176. [CrossRef]
13. Rezaei, J. Best-worst multi-criteria decision-making method. Omega 2015, 53, 49–57. [CrossRef]
14. Rezaei, J. Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega 2016, 64, 126–130. [CrossRef]
15. Liang, F.; Brunelli, M.; Rezaei, J. Consistency issues in the Best Worst Method: Measurements and thresholds. Omega 2020, 96, 102175. [CrossRef]
16. Faizi, S.; Sałabun, W.; Nawaz, S. Best-Worst method and Hamacher aggregation operations for intuitionistic 2-tuple linguistic sets. Expert Syst. Appl. 2021, 115088, in press. [CrossRef]
17. Hosseini, S.M.; Bahadori, M.; Raadabadi, M.; Ravangard, R. Ranking hospitals based on the disasters preparedness using the TOPSIS technique in western Iran. Hosp. Top. 2019, 97, 23–31. [CrossRef] [PubMed]
18. Ortiz-Barrios, M.A.; Aleman-Romero, B.A.; Rebolledo-Rudas, J.; Maldonado-Mestre, H.; Montes-Villa, L.; De Felice, F.; Petrillo, A. The analytic decision-making preference model to evaluate the disaster readiness in emergency departments: The ADT model. J. Multi-Criteria Decis. Anal. 2017, 24, 204–226. [CrossRef]
19. Sarkar, S. COVID-19 Susceptibility Mapping Using Multicriteria Evaluation. Disaster Med. Public Health Prep. 2020, 14, 521–537. [CrossRef]
20. Sangiorgio, V.; Parisi, P. A multicriteria approach for risk assessment of Covid-19 in urban district lockdown. Saf. Sci. 2020, 130, 104862. [CrossRef]
21. Dijkman, J.G.; van Haeringen, H.; de Lange, S.J. Fuzzy numbers. J. Math. Anal. Appl. 1983, 92, 301–341. [CrossRef]
22. Kiker, G.A.; Bridges, T.S.; Varghese, A.; Seager, T.P.; Linkov, I. Application of multicriteria decision anal-ysis in environmental decision making. Integr. Environ. Assess. Manag. Int. J. 2005, 1, 95–108. [CrossRef]
23. Singh, H.; Gupta, M.M.; Meitzler, T.; Hou, Z.-G.; Garg, K.K.; Solo, A.M.G.; Zadeh, L.A. Real-Life Applications of Fuzzy Logic. Adv. Fuzzy Syst. 2013, 581879, 1–3. [CrossRef]
24. Kahraman, C. (Ed.) Fuzzy Multi-Criteria Decision Making: Theory and Applications with Recent Developments; Springer: Berlin, Germany, 2008; Volume 16.
25. Guo, S.; Zhao, H. Fuzzy best-worst multi-criteria decision-making method and its applications. Knowl. Based Syst. 2017, 121, 23–31. [CrossRef]
26. Tischler, G.L. Decision-making process in the emergency room. Archives Gen. Psychiatry 1966, 14, 69–78. [CrossRef] [PubMed]
27. Sharma, M.K.; Dhiman, N.; Mishra, V.N. Mediative fuzzy logic mathematical model: A contradictory management prediction in COVID-19 pandemic. Appl. Soft Comput. 2021, 105, 107285. [CrossRef]
28. Dhiman, N.; Sharma, M.K. Mediative Sugeno’s-TSK fuzzy logic based screening analysis to diagnosis of heart disease. Appl. Math. 2019, 10, 448–467. [CrossRef]
29. Shaban, W.M.; Rabie, A.H.; Saleh, A.I.; Abo-Elsoud, M.A. Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network. Appl. Soft Comput. 2021, 99, 106906. [CrossRef]
30. Ozturk, T.; Talo, M.; Yildirim, E.A.; Baloglu, U.B.; Yildirim, O.; Rajendra Acharya, U. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput. Biol. Med. 2020, 121, 103792. [CrossRef] [PubMed]
31. Pirouz, B.; Shaffiee Haghshenas, S.; Shaffiee Haghshenas, S.; Piro, P. Investigating a serious challenge in the sustainable development process: Analysis of confirmed cases of COVID-19 (new type of Coronavirus) through a binary classification using artificial intelligence and regression analysis. Sustainability 2020, 12, 2427. [CrossRef]
32. Sethy, P.K.; Behera, S.K. Detection of Coronavirus Disease (COVID-19) Based on Deep Features. Preprints 2020, 2020030300. [CrossRef]
33. Xu, X.; Jiang, X.; Ma, C.; Du, P.; Li, X.; Lv, S.; Li, L. A deep learning system to screen novel Coronavirus disease 2019 pneumonia. Eng. Beijing China 2020, 6, 1122–1129. [CrossRef]
34. Wang, J.-J.; Jing, Y.-Y.; Zhang, C.-F.; Zhao, J.-H. Review on multi-criteria decision analysis aid in sustainable energy decisionmaking. Renew. Sustain. Energy Rev. 2009, 13, 2263–2278. [CrossRef]
35. Batur Sir, G.D.; Sir, E. Pain Treatment Evaluation in COVID-19 Patients with Hesitant Fuzzy Linguistic Multicriteria DecisionMaking. J. Healthc. Eng. 2021, 8831114, 1–11. [CrossRef] [PubMed]
36. Fu, Y.-L.; Liang, K.-C. Fuzzy logic programming and adaptable design of medical products for the COVID-19 anti-epidemic normalization. Comput. Methods Programs Biomed. 2020, 197, 105762. [CrossRef]
37. Palouj, M.; Lavaei Adaryani, R.; Alambeigi, A.; Movarej, M.; Safi Sis, Y. Surveying the impact of the coronavirus (COVID-19) on the poultry supply chain: A mixed methods study. Food Control 2021, 126, 108084. [CrossRef] [PubMed]
38. Oliveira, J.F.; Jorge, D.C.; Veiga, R.V.; Rodrigues, M.S.; Torquato, M.F.; Silva, N.B.; Andrade, R.F. Mathematical modeling of COVID-19 in 14. 8 million individuals in Bahia, Brazil. Nat. Commun. 2021, 12, 1–13. [CrossRef]
39. Caetano, M.A.L. Can Catastrophe Theory Explain Expansion and Contagious of Covid-19? medRxiv 2021. [CrossRef]
40. Crítica y Unidades Coronarias; Semicyuc.org Website. Recomendaciones Éticas Para La Toma De Decisiones En La Situación Excepcional De Crisis Por Pandemia Covid-19 En Las Unidades De Cuidados Intensivos. (SEMICYUC). Semicyuc.org Website. Available online: https://semicyuc.org/wp-content/uploads/2020/03/%C3%89tica_SEMICYUC-COVID-19.pdf (accessed on 20 April 2021).
41. Madrid’s New COVID-19 Hospital Faces Backlash. Cgtn.com Website. Available online: https://newseu.cgtn.com/news/2020-1 2-03/Madrid-s-new-COVID-19-hospital-faces-backlash-VU85oyZLxe/index.html (accessed on 20 April 2021).
42. Alzamora, B.; Barros, R.T.V. Analysis and financial sustainability of MSW management in Belo Horizonte (Brazil). Int. J. Environ. Waste Manag. 2022, in press. [CrossRef]
43. Depuydt, P.; Guidet, B. Triage policy of severe Covid-19 patients: What to do now? Ann. Intensive Care 2021, 11, 18. [CrossRef]
44. Vujanovic, A.A.; Lebeaut, A.; Leonard, S. Exploring the impact of the COVID-19 pandemic on the mental health of first responders. Cogn. Behav. Ther. 2021, 1–16. [CrossRef] [PubMed]
45. Zolnikov, T.R.; Furio, F. Stigma on first responders during COVID-19. Stigma Health 2020, 5, 375–379. [CrossRef]
46. De Kock, J.H.; Latham, H.A.; Leslie, S.J.; Grindle, M.; Munoz, S.-A.; Ellis, L.; O’Malley, C.M. A rapid review of the impact of COVID-19 on the mental health of healthcare workers: Implications for supporting psychological well-being. BMC Public Health 2021, 21, 104. [CrossRef]
47. Xiong, J.; Lipsitz, O.; Nasri, F.; Lui, L.M.W.; Gill, H.; Phan, L.; McIntyre, R.S. Impact of COVID-19 pandemic on mental health in the general population: A systematic review. J. Affect. Disord. 2020, 277, 55–64. [CrossRef]
48. Lebrasseur, A.; Fortin-Bédard, N.; Lettre, J.; Bussières, E.-L.; Best, K.; Boucher, N.; Routhier, F. Impact of COVID-19 on people with physical disabilities: A rapid review. Disabil. Health J. 2021, 14, 101014. [CrossRef]
49. Li, W.; Yang, Y.; Liu, Z.-H.; Zhao, Y.-J.; Zhang, Q.; Zhang, L.; Xiang, Y.-T. Progression of mental health services during the COVID-19 outbreak in China. Int. J. Biol. Sci. 2020, 16, 1732–1738. [CrossRef]
50. Taquet, M.; Luciano, S.; Geddes, J.R.; Harrison, P.J. Bidirectional associations between COVID-19 and psychiatric disorder: Retrospective cohort studies of 62 354 COVID-19 cases in the USA. Lancet Psychiatry 2021, 8, 130–140. [CrossRef]
51. Giorgi, G.; Lecca, L.I.; Alessio, F.; Finstad, G.L.; Bondanini, G.; Lulli, L.G.; Mucci, N. COVID-19-related mental health effects in the workplace: A narrative review. Int. J. Environ. Res. Public Health 2020, 17, 7857. [CrossRef]
52. McKnight-Eily, L.R.; Okoro, C.A.; Strine, T.W.; Verlenden, J.; Hollis, N.D.; Njai, R.; Thomas, C. Racial and ethnic disparities in the prevalence of stress and worry, mental health conditions, and increased substance use among adults during the COVID-19 pandemic—United States, April and May 2020. Mmwr. Morb. Mortal. Wkly. Rep. 2021, 70, 162–166. [CrossRef]
53. Alcover, C.-M.; Salgado, S.; Nazar, G.; Ramírez-Vielma, R.; González-Suhr, C. Job Insecurity, Financial Threat and Mental Health in the COVID-19 Context: The Buffer Role of Perceived Social Support. MedRxiv 2020. [CrossRef]
54. Cengiz, K.; Onar, S.C.; Oztaysi, B. Fuzzy multicriteria decision-making: A literature review. Int. J. Comput. Intell. Syst. 2015, 8, 637–666.
55. Matarazzo, G.; Fernandes, A.; Alcadipani, R. Police institutions in the face of the pandemic: Sensemaking, leadership, and discretion. Rev. Adm. Pública 2020, 54, 898–908.
56. Kofman, Y.B.; Garfin, D.R. Home is not always a haven: The domestic violence crisis amid the COVID-19 pandemic. Psychol. Trauma Theory Res. Pract. Policy 2020, 12, S199–S201. [CrossRef]
57. Jennings, W.G.; Perez, N.M. The immediate impact of COVID-19 on law enforcement in the United States. Am. J. Crim. Justice Ajcj 2020, 45, 1–12. [CrossRef]
58. Bonkiewicz, L.; Ruback, R.B. The role of the police in evacuations: Responding to the social impact of a disaster. Police Q. 2012, 15, 137–156. [CrossRef]
59. Shortland, N.; Thompson, L.; Alison, L. Police perfection: Examining the effect of trait maximization on police decision-making. Front. Psychol. 2020, 11, 1817. [CrossRef]
60. Sánchez-Lozano, J.M.; Serna, J.; Dolón-Payán, A. Evaluating military training aircrafts through the combination of multi-criteria decision-making processes with fuzzy logic. A case study in the Spanish Air Force Academy. Aerosp. Sci. Technol. 2015, 42, 58–65. [CrossRef]
61. Yilmaz, B.Ö.; Tozan, H.; Karadayi, M.A. Multi-Criteria Decision Making (MCDM) Applications in Military Healthcare Field. J. Health Syst. Policies 2020, 2, 149–181.
62. Karadayi, M.A.; Ekinci, Y.; Tozan, H. A fuzzy MCDM framework for weapon systems selection. In Operations Research for Military Organizations; IGI Global: Hershey, PA, USA, 2019; pp. 185–204.
63. Pearce, A.P.; Naumann, D.N.; O’Reilly, D. Mission command: Applying principles of military leadership to the SARSCov-2 (covid-19) crisis. BMJ Mil Health 2021, 167, 3–4. [CrossRef]
64. Karsak, E.E.; Ethem Tolga, E. Fuzzy multi-criteria decision-making procedure for evaluating advanced manufacturing system investments. Int. J. Prod. Econ. 2001, 69, 49–64. [CrossRef]
65. Dalalah, D.; Hayajneh, M.; Batieha, F. A fuzzy multi-criteria decision making model for supplier selection. Expert Syst. Appl. 2011, 38, 8384–8391. [CrossRef]
66. Chang, T.; Wang, T. Using the fuzzy multi-criteria decision making approach for measuring the possibility of successful knowledge management. Inf. Sci. 2009, 179, 355–370. [CrossRef]
67. Chou, T.-Y.; Chou, S.-C.T.; Tzeng, G.-H. Evaluating IT/IS investments: A fuzzy multi-criteria decision model approach. Eur. J. Oper. Res. 2006, 173, 1026–1046. [CrossRef]
68. Wang, C.-N.; Yang, C.-Y.; Cheng, H.-C. A fuzzy multicriteria decision-making (MCDM) model for sustainable supplier evaluation and selection based on triple bottom line approaches in the garment industry. Processes 2019, 7, 400. [CrossRef]
69. Kaya, ˙I.; Çolak, M.; Terzi, F. A comprehensive review of fuzzy multi criteria decision making methodologies for energy policy making. Energy Strategy Rev. 2019, 24, 207–228. [CrossRef]
70. Khemiri, R.; Elbedoui-Maktouf, K.; Grabot, B.; Zouari, B. A fuzzy multi-criteria decision-making approach for managing performance and risk in integrated procurement–production planning. Int. J. Prod. Res. 2017, 55, 5305–5329. [CrossRef]
71. Soto-Baño, M.A.; Clemente-Suárez, V.J. Psicología de emergencias en España: Delimitación conceptual, ámbitos de actuación y propuesta de un sistema asistencial. Papeles del Psicól 2021, 42, 56–66.
72. Soto-Baño, M.A.; Clemente-Suárez, V.J. Psicología de emergencias en España: Análisis actual, normativa y proposición reguladora. Papeles del Psicól 2021, 42, 46–55.
73. Yao, S. Fuzzy-based multi-criteria decision analysis of environmental regulation and green economic efficiency in a post-COVID-19 scenario: The case of China. Environ. Sci. Pollut. Res. Int. 2021, 1–27. [CrossRef]
74. Majumder, P.; Biswas, P.; Majumder, S. Application of new TOPSIS approach to identify the most significant risk factor and continuous monitoring of death of COVID-19. Electron. J. Gen. Med. 2020, 17, em234. [CrossRef]
75. Clemente-Suárez, V.J.; Navarro-Jiménez, E.; Jimenez, M.; Hormeño-Holgado, A.; Martinez-Gonzalez, M.B.; Benitez-Agudelo, J.C.; Perez-Palencia, N.; Laborde-Cárdenas, C.C.; Tornero-Aguilera, J.F. Impact of COVID-19 Pandemic in Public Mental Health: An Extensive Narrative Review. Sustainability 2021, 13, 3221. [CrossRef]
76. Rodriguez-Besteiro, S.; Tornero-Aguilera, J.F.; Fernández-Lucas, J.; Clemente-Suárez, V.J. Gender Differences in the COVID-19 Pandemic Risk Perception, Psychology, and Behaviors of Spanish University Students. Int. J. Environ. Res. Public Health 2021, 18, 3908. [CrossRef]
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spelling Clemente-Suárez, Vicente JavierNavarro Jiménez, EduardoRuisoto, PabloDalamitros, AthanasiosBeltrán Velasco, Ana IsabelHormeno-Holgado, Alberto JoaquinLaborde Cardenas, Carmen CeciliaTornero Aguilera, José Francisco2021-06-24T14:42:35Z2021-06-24T14:42:35Z2021-05-141660-46011661-7827https://hdl.handle.net/11323/8407https://doi.org/10.3390/ijerph18105208Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The actual coronavirus disease 2019 (COVID-19) pandemic has led to the limit of emergency systems worldwide, leading to the collapse of health systems, police, first responders, as well as other areas. Various ways of dealing with this world crisis have been proposed from many aspects, with fuzzy multi-criteria decision analysis being a method that can be applied to a wide range of emergency systems and professional groups, aiming to confront several associated issues and challenges. The purpose of this critical review was to discuss the basic principles, present current applications during the first pandemic wave, and propose future implications of this methodology. For this purpose, both primary sources, such as scientific articles, and secondary ones, such as bibliographic indexes, web pages, and databases, were used. The main search engines were PubMed, SciELO, and Google Scholar. The method was a systematic literature review of the available literature regarding the performance of the fuzzy multi-criteria decision analysis of emergency systems in the COVID-19 pandemic. The results of this study highlight the importance of the fuzzy multi-criteria decision analysis method as a beneficial tool for healthcare workers and first responders’ emergency professionals to face this pandemic as well as to manage the created uncertainty and its related risks.Clemente-Suárez, Vicente Javier-will be generated-orcid-0000-0002-2397-2801-600Navarro Jiménez, Eduardo-will be generated-orcid-0000-0002-8171-662X-600Ruisoto, Pablo-will be generated-orcid-0000-0003-1252-0479-600Dalamitros, Athanasios-will be generated-orcid-0000-0003-1069-2146-600Beltrán Velasco, Ana Isabel-will be generated-orcid-0000-0002-9893-0227-600Hormeno-Holgado, Alberto Joaquin-will be generated-orcid-0000-0001-7858-661X-600Laborde Cardenas, Carmen Cecilia-will be generated-orcid-0000-0001-6225-8072-600Tornero Aguilera, José Francisco-will be generated-orcid-0000-0002-0747-8133-600application/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2International Journal of Environmental Research and Public Healthhttps://www.mdpi.com/1660-4601/18/10/5208/htmFuzzy decision analysisDecision makingUncertaintyMulti-criteriaEmergencyCOVID-19Performance of fuzzy multi-criteria decision analysis of emergency system in COVID-19 pandemic. An extensive narrative reviewArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Clemente-Suárez, V.J.; Dalamitros, A.A.; Beltran-Velasco, A.I.; Mielgo-Ayuso, J.; Tornero-Aguilera, J.F. Social and psychophysiological consequences of the COVID-19 pandemic: An extensive literature review. Front. Psychol. 2020, 11, 580225. [CrossRef]2. Clemente-Suárez, V.J.; Hormeño-Holgado, A.; Jiménez, M.; Benitez-Agudelo, J.C.; Navarro-Jiménez, E.; Perez-Palencia, N.; Maestre-Serrano, R.; Laborde-Cárdenas, C.C.; Tornero-Aguilera, J.F. Dynamics of population immunity due to the herd effect in the COVID-19 pandemic. Vaccines 2020, 8, 236. [CrossRef]3. Solis, J.; Franco-Paredes, C.; Henao-Martínez, A.F.; Krsak, M.; Zimmer, S.M. Structural vulnerability in the US revealed in three waves of COVID-19. Am. J. Trop. Med. Hig. 2020, 103, 25–27. [CrossRef]4. Conti, P.; Caraffa, A.; Gallenga, C.E.; Kritas, S.K.; Frydas, I.; Younes, A.; Di Emidio, P.; Tetè, G.; Pregliasco, F.; Ronconi, G. The British variant of the new coronavirus-19 (Sars-Cov-2) should not create a vaccine problem. J. Biol. Regul. Homeost. Agents 2021, 35, 1–4.5. Siu, G.K.-H.; Lee, L.-K.; Leung, K.S.-S.; Leung, J.S.-L.; Ng, T.T.-L.; Chan, C.T.-M.; Tam, K.K.-G.; Lao, H.-Y.; Wu, A.K.-L.; Yau, M.C.-Y.; et al. Will a new clade of SARS-CoV-2 imported into the community spark a fourth wave of the COVID-19 outbreak in Hong Kong? Emerg. Microbes Infect. 2020, 9, 2497–2500. [CrossRef] [PubMed]6. Tsang, H.F.; Chan, L.W.C.; Cho, W.C.S.; Yu, A.C.S.; Yim, A.K.Y.; Chan, A.K.C.; Wong, S.C.C. An Update on COVID-19 Pandemic: The Epidemiology, Pathogenesis, Prevention and Treatment Strategies. Expert Rev. Anti-Infect. Ther. 2021, 29, 1–12. [CrossRef]7. Pamuˇcar, D.; Žižovi´c, M.; Marinkovi´c, D.; Doljanica, D.; Jovanovi´c, S.V.; Brzakovi´c, P. Development of a multi-criteria model for sustainable reorganization of a healthcare system in an emergency situation caused by the COVID-19 pandemic. Sustainability 2020, 12, 7504. [CrossRef]8. Yildirim, F.S.; Sayan, M.; Sanlidag, T.; Uzun, B.; Ozsahin, D.U.; Ozsahin, I. Comparative evaluation of the treatment of COVID-19 with multicriteria decision-making techniques. J. Healthc. Eng. 2021, 2021, 8864522. [CrossRef]9. Abdullah, L. Fuzzy Multi Criteria Decision Making and its Applications: A Brief Review of Category. Procedia Soc. Behav. Sci. 2013, 97, 131–136. [CrossRef]10. Carlsson, C.; Fullér, R. Fuzzy multiple criteria decision making: Recent developments. Fuzzy Set Syst. 1996, 78, 139–153. [CrossRef]11. Vakaramoko Diaby, V.; Goeree, R. How to use multi-criteria decision analysis methods for reimbursement decision-making in healthcare: A step-by-step guide. Expert Rev. Pharm. Outcomes Res. 2014, 14, 81–99.12. Saaty, R.W. The Analytic Hierarchy process—What it is and how it is used. Math. Model. 1987, 9, 161–176. [CrossRef]13. Rezaei, J. Best-worst multi-criteria decision-making method. Omega 2015, 53, 49–57. [CrossRef]14. Rezaei, J. Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega 2016, 64, 126–130. [CrossRef]15. Liang, F.; Brunelli, M.; Rezaei, J. Consistency issues in the Best Worst Method: Measurements and thresholds. Omega 2020, 96, 102175. [CrossRef]16. Faizi, S.; Sałabun, W.; Nawaz, S. Best-Worst method and Hamacher aggregation operations for intuitionistic 2-tuple linguistic sets. Expert Syst. Appl. 2021, 115088, in press. [CrossRef]17. Hosseini, S.M.; Bahadori, M.; Raadabadi, M.; Ravangard, R. Ranking hospitals based on the disasters preparedness using the TOPSIS technique in western Iran. Hosp. Top. 2019, 97, 23–31. [CrossRef] [PubMed]18. Ortiz-Barrios, M.A.; Aleman-Romero, B.A.; Rebolledo-Rudas, J.; Maldonado-Mestre, H.; Montes-Villa, L.; De Felice, F.; Petrillo, A. The analytic decision-making preference model to evaluate the disaster readiness in emergency departments: The ADT model. J. Multi-Criteria Decis. Anal. 2017, 24, 204–226. [CrossRef]19. Sarkar, S. COVID-19 Susceptibility Mapping Using Multicriteria Evaluation. Disaster Med. Public Health Prep. 2020, 14, 521–537. [CrossRef]20. Sangiorgio, V.; Parisi, P. A multicriteria approach for risk assessment of Covid-19 in urban district lockdown. Saf. Sci. 2020, 130, 104862. [CrossRef]21. Dijkman, J.G.; van Haeringen, H.; de Lange, S.J. Fuzzy numbers. J. Math. Anal. Appl. 1983, 92, 301–341. [CrossRef]22. Kiker, G.A.; Bridges, T.S.; Varghese, A.; Seager, T.P.; Linkov, I. Application of multicriteria decision anal-ysis in environmental decision making. Integr. Environ. Assess. Manag. Int. J. 2005, 1, 95–108. [CrossRef]23. Singh, H.; Gupta, M.M.; Meitzler, T.; Hou, Z.-G.; Garg, K.K.; Solo, A.M.G.; Zadeh, L.A. Real-Life Applications of Fuzzy Logic. Adv. Fuzzy Syst. 2013, 581879, 1–3. [CrossRef]24. Kahraman, C. (Ed.) Fuzzy Multi-Criteria Decision Making: Theory and Applications with Recent Developments; Springer: Berlin, Germany, 2008; Volume 16.25. Guo, S.; Zhao, H. Fuzzy best-worst multi-criteria decision-making method and its applications. Knowl. Based Syst. 2017, 121, 23–31. [CrossRef]26. Tischler, G.L. Decision-making process in the emergency room. Archives Gen. Psychiatry 1966, 14, 69–78. [CrossRef] [PubMed]27. Sharma, M.K.; Dhiman, N.; Mishra, V.N. Mediative fuzzy logic mathematical model: A contradictory management prediction in COVID-19 pandemic. Appl. Soft Comput. 2021, 105, 107285. [CrossRef]28. Dhiman, N.; Sharma, M.K. Mediative Sugeno’s-TSK fuzzy logic based screening analysis to diagnosis of heart disease. Appl. Math. 2019, 10, 448–467. [CrossRef]29. Shaban, W.M.; Rabie, A.H.; Saleh, A.I.; Abo-Elsoud, M.A. Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network. Appl. Soft Comput. 2021, 99, 106906. [CrossRef]30. Ozturk, T.; Talo, M.; Yildirim, E.A.; Baloglu, U.B.; Yildirim, O.; Rajendra Acharya, U. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput. Biol. Med. 2020, 121, 103792. [CrossRef] [PubMed]31. Pirouz, B.; Shaffiee Haghshenas, S.; Shaffiee Haghshenas, S.; Piro, P. Investigating a serious challenge in the sustainable development process: Analysis of confirmed cases of COVID-19 (new type of Coronavirus) through a binary classification using artificial intelligence and regression analysis. Sustainability 2020, 12, 2427. [CrossRef]32. Sethy, P.K.; Behera, S.K. Detection of Coronavirus Disease (COVID-19) Based on Deep Features. Preprints 2020, 2020030300. [CrossRef]33. Xu, X.; Jiang, X.; Ma, C.; Du, P.; Li, X.; Lv, S.; Li, L. A deep learning system to screen novel Coronavirus disease 2019 pneumonia. Eng. Beijing China 2020, 6, 1122–1129. [CrossRef]34. Wang, J.-J.; Jing, Y.-Y.; Zhang, C.-F.; Zhao, J.-H. Review on multi-criteria decision analysis aid in sustainable energy decisionmaking. Renew. Sustain. Energy Rev. 2009, 13, 2263–2278. [CrossRef]35. Batur Sir, G.D.; Sir, E. Pain Treatment Evaluation in COVID-19 Patients with Hesitant Fuzzy Linguistic Multicriteria DecisionMaking. J. Healthc. Eng. 2021, 8831114, 1–11. [CrossRef] [PubMed]36. Fu, Y.-L.; Liang, K.-C. Fuzzy logic programming and adaptable design of medical products for the COVID-19 anti-epidemic normalization. Comput. Methods Programs Biomed. 2020, 197, 105762. [CrossRef]37. Palouj, M.; Lavaei Adaryani, R.; Alambeigi, A.; Movarej, M.; Safi Sis, Y. Surveying the impact of the coronavirus (COVID-19) on the poultry supply chain: A mixed methods study. Food Control 2021, 126, 108084. [CrossRef] [PubMed]38. Oliveira, J.F.; Jorge, D.C.; Veiga, R.V.; Rodrigues, M.S.; Torquato, M.F.; Silva, N.B.; Andrade, R.F. Mathematical modeling of COVID-19 in 14. 8 million individuals in Bahia, Brazil. Nat. Commun. 2021, 12, 1–13. [CrossRef]39. Caetano, M.A.L. Can Catastrophe Theory Explain Expansion and Contagious of Covid-19? medRxiv 2021. [CrossRef]40. Crítica y Unidades Coronarias; Semicyuc.org Website. Recomendaciones Éticas Para La Toma De Decisiones En La Situación Excepcional De Crisis Por Pandemia Covid-19 En Las Unidades De Cuidados Intensivos. (SEMICYUC). Semicyuc.org Website. Available online: https://semicyuc.org/wp-content/uploads/2020/03/%C3%89tica_SEMICYUC-COVID-19.pdf (accessed on 20 April 2021).41. Madrid’s New COVID-19 Hospital Faces Backlash. Cgtn.com Website. Available online: https://newseu.cgtn.com/news/2020-1 2-03/Madrid-s-new-COVID-19-hospital-faces-backlash-VU85oyZLxe/index.html (accessed on 20 April 2021).42. Alzamora, B.; Barros, R.T.V. Analysis and financial sustainability of MSW management in Belo Horizonte (Brazil). Int. J. Environ. Waste Manag. 2022, in press. [CrossRef]43. Depuydt, P.; Guidet, B. Triage policy of severe Covid-19 patients: What to do now? Ann. Intensive Care 2021, 11, 18. [CrossRef]44. Vujanovic, A.A.; Lebeaut, A.; Leonard, S. Exploring the impact of the COVID-19 pandemic on the mental health of first responders. Cogn. Behav. Ther. 2021, 1–16. [CrossRef] [PubMed]45. Zolnikov, T.R.; Furio, F. Stigma on first responders during COVID-19. Stigma Health 2020, 5, 375–379. [CrossRef]46. De Kock, J.H.; Latham, H.A.; Leslie, S.J.; Grindle, M.; Munoz, S.-A.; Ellis, L.; O’Malley, C.M. A rapid review of the impact of COVID-19 on the mental health of healthcare workers: Implications for supporting psychological well-being. BMC Public Health 2021, 21, 104. [CrossRef]47. Xiong, J.; Lipsitz, O.; Nasri, F.; Lui, L.M.W.; Gill, H.; Phan, L.; McIntyre, R.S. Impact of COVID-19 pandemic on mental health in the general population: A systematic review. J. Affect. Disord. 2020, 277, 55–64. [CrossRef]48. Lebrasseur, A.; Fortin-Bédard, N.; Lettre, J.; Bussières, E.-L.; Best, K.; Boucher, N.; Routhier, F. Impact of COVID-19 on people with physical disabilities: A rapid review. Disabil. Health J. 2021, 14, 101014. [CrossRef]49. Li, W.; Yang, Y.; Liu, Z.-H.; Zhao, Y.-J.; Zhang, Q.; Zhang, L.; Xiang, Y.-T. Progression of mental health services during the COVID-19 outbreak in China. Int. J. Biol. Sci. 2020, 16, 1732–1738. [CrossRef]50. Taquet, M.; Luciano, S.; Geddes, J.R.; Harrison, P.J. Bidirectional associations between COVID-19 and psychiatric disorder: Retrospective cohort studies of 62 354 COVID-19 cases in the USA. Lancet Psychiatry 2021, 8, 130–140. [CrossRef]51. Giorgi, G.; Lecca, L.I.; Alessio, F.; Finstad, G.L.; Bondanini, G.; Lulli, L.G.; Mucci, N. COVID-19-related mental health effects in the workplace: A narrative review. Int. J. Environ. Res. Public Health 2020, 17, 7857. [CrossRef]52. McKnight-Eily, L.R.; Okoro, C.A.; Strine, T.W.; Verlenden, J.; Hollis, N.D.; Njai, R.; Thomas, C. Racial and ethnic disparities in the prevalence of stress and worry, mental health conditions, and increased substance use among adults during the COVID-19 pandemic—United States, April and May 2020. Mmwr. Morb. Mortal. Wkly. Rep. 2021, 70, 162–166. [CrossRef]53. Alcover, C.-M.; Salgado, S.; Nazar, G.; Ramírez-Vielma, R.; González-Suhr, C. Job Insecurity, Financial Threat and Mental Health in the COVID-19 Context: The Buffer Role of Perceived Social Support. MedRxiv 2020. [CrossRef]54. Cengiz, K.; Onar, S.C.; Oztaysi, B. Fuzzy multicriteria decision-making: A literature review. Int. J. Comput. Intell. Syst. 2015, 8, 637–666.55. Matarazzo, G.; Fernandes, A.; Alcadipani, R. Police institutions in the face of the pandemic: Sensemaking, leadership, and discretion. Rev. Adm. Pública 2020, 54, 898–908.56. Kofman, Y.B.; Garfin, D.R. Home is not always a haven: The domestic violence crisis amid the COVID-19 pandemic. Psychol. Trauma Theory Res. Pract. Policy 2020, 12, S199–S201. [CrossRef]57. Jennings, W.G.; Perez, N.M. The immediate impact of COVID-19 on law enforcement in the United States. Am. J. Crim. Justice Ajcj 2020, 45, 1–12. [CrossRef]58. Bonkiewicz, L.; Ruback, R.B. The role of the police in evacuations: Responding to the social impact of a disaster. Police Q. 2012, 15, 137–156. [CrossRef]59. Shortland, N.; Thompson, L.; Alison, L. Police perfection: Examining the effect of trait maximization on police decision-making. Front. Psychol. 2020, 11, 1817. [CrossRef]60. Sánchez-Lozano, J.M.; Serna, J.; Dolón-Payán, A. Evaluating military training aircrafts through the combination of multi-criteria decision-making processes with fuzzy logic. A case study in the Spanish Air Force Academy. Aerosp. Sci. Technol. 2015, 42, 58–65. [CrossRef]61. Yilmaz, B.Ö.; Tozan, H.; Karadayi, M.A. Multi-Criteria Decision Making (MCDM) Applications in Military Healthcare Field. J. Health Syst. Policies 2020, 2, 149–181.62. Karadayi, M.A.; Ekinci, Y.; Tozan, H. A fuzzy MCDM framework for weapon systems selection. In Operations Research for Military Organizations; IGI Global: Hershey, PA, USA, 2019; pp. 185–204.63. Pearce, A.P.; Naumann, D.N.; O’Reilly, D. Mission command: Applying principles of military leadership to the SARSCov-2 (covid-19) crisis. BMJ Mil Health 2021, 167, 3–4. [CrossRef]64. Karsak, E.E.; Ethem Tolga, E. Fuzzy multi-criteria decision-making procedure for evaluating advanced manufacturing system investments. Int. J. Prod. Econ. 2001, 69, 49–64. [CrossRef]65. Dalalah, D.; Hayajneh, M.; Batieha, F. A fuzzy multi-criteria decision making model for supplier selection. Expert Syst. Appl. 2011, 38, 8384–8391. [CrossRef]66. Chang, T.; Wang, T. Using the fuzzy multi-criteria decision making approach for measuring the possibility of successful knowledge management. Inf. Sci. 2009, 179, 355–370. [CrossRef]67. Chou, T.-Y.; Chou, S.-C.T.; Tzeng, G.-H. Evaluating IT/IS investments: A fuzzy multi-criteria decision model approach. Eur. J. Oper. Res. 2006, 173, 1026–1046. [CrossRef]68. Wang, C.-N.; Yang, C.-Y.; Cheng, H.-C. A fuzzy multicriteria decision-making (MCDM) model for sustainable supplier evaluation and selection based on triple bottom line approaches in the garment industry. Processes 2019, 7, 400. [CrossRef]69. Kaya, ˙I.; Çolak, M.; Terzi, F. A comprehensive review of fuzzy multi criteria decision making methodologies for energy policy making. Energy Strategy Rev. 2019, 24, 207–228. [CrossRef]70. Khemiri, R.; Elbedoui-Maktouf, K.; Grabot, B.; Zouari, B. A fuzzy multi-criteria decision-making approach for managing performance and risk in integrated procurement–production planning. Int. J. Prod. Res. 2017, 55, 5305–5329. [CrossRef]71. Soto-Baño, M.A.; Clemente-Suárez, V.J. Psicología de emergencias en España: Delimitación conceptual, ámbitos de actuación y propuesta de un sistema asistencial. Papeles del Psicól 2021, 42, 56–66.72. Soto-Baño, M.A.; Clemente-Suárez, V.J. Psicología de emergencias en España: Análisis actual, normativa y proposición reguladora. Papeles del Psicól 2021, 42, 46–55.73. Yao, S. Fuzzy-based multi-criteria decision analysis of environmental regulation and green economic efficiency in a post-COVID-19 scenario: The case of China. Environ. Sci. Pollut. Res. Int. 2021, 1–27. [CrossRef]74. Majumder, P.; Biswas, P.; Majumder, S. Application of new TOPSIS approach to identify the most significant risk factor and continuous monitoring of death of COVID-19. Electron. J. Gen. Med. 2020, 17, em234. [CrossRef]75. Clemente-Suárez, V.J.; Navarro-Jiménez, E.; Jimenez, M.; Hormeño-Holgado, A.; Martinez-Gonzalez, M.B.; Benitez-Agudelo, J.C.; Perez-Palencia, N.; Laborde-Cárdenas, C.C.; Tornero-Aguilera, J.F. Impact of COVID-19 Pandemic in Public Mental Health: An Extensive Narrative Review. Sustainability 2021, 13, 3221. [CrossRef]76. Rodriguez-Besteiro, S.; Tornero-Aguilera, J.F.; Fernández-Lucas, J.; Clemente-Suárez, V.J. Gender Differences in the COVID-19 Pandemic Risk Perception, Psychology, and Behaviors of Spanish University Students. Int. J. Environ. Res. Public Health 2021, 18, 3908. [CrossRef]PublicationORIGINALPerformance of fuzzy multi-criteria decision analysis of emergency system in covid-19 pandemic. An extensive narrative review.pdfPerformance of fuzzy multi-criteria decision analysis of emergency system in covid-19 pandemic. 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