key: cord-1029592-cjno2e50
authors: Alsalem, Mohammed Assim; Mohammed, Rawia; Albahri, Osamah Shihab; Zaidan, Aws Alaa; Alamoodi, Abdullah Hussein; Dawood, Kareem; Alnoor, Alhamzah; Albahri, Ahmed Shihab; Zaidan, Bilal Bahaa; Aickelin, Uwe; Alsattar, Hassan; Alazab, Mamoun; Jumaah, Fawaz
title: Rise of multiattribute decision‐making in combating COVID‐19: A systematic review of the state‐of‐the‐art literature
date: 2021-10-04
journal: Int. J. Intell. Syst.
DOI: 10.1002/int.22699
sha: 41a18a552cb5ee947da5090073559e97a0e08b74
doc_id: 1029592
cord_uid: cjno2e50

Considering the coronavirus disease 2019 (COVID‐19) pandemic, the government and health sectors are incapable of making fast and reliable decisions, particularly given the various effects of decisions on different contexts or countries across multiple sectors. Therefore, leaders often seek decision support approaches to assist them in such scenarios. The most common decision support approach used in this regard is multiattribute decision‐making (MADM). MADM can assist in enforcing the most ideal decision in the best way possible when fed with the appropriate evaluation criteria and aspects. MADM also has been of great aid to practitioners during the COVID‐19 pandemic. Moreover, MADM shows resilience in mitigating consequences in health sectors and other fields. Therefore, this study aims to analyse the rise of MADM techniques in combating COVID‐19 by presenting a systematic literature review of the state‐of‐the‐art COVID‐19 applications. Articles on related topics were searched in four major databases, namely, Web of Science, IEEE Xplore, ScienceDirect, and Scopus, from the beginning of the pandemic in 2019 to April 2021. Articles were selected on the basis of the inclusion and exclusion criteria for the identified systematic review protocol, and a total of 51 articles were obtained after screening and filtering. All these articles were formed into a coherent taxonomy to describe the corresponding current standpoints in the literature. This taxonomy was drawn on the basis of four major categories, namely, medical (n = 30), social (n = 4), economic (n = 13) and technological (n = 4). Deep analysis for each category was performed in terms of several aspects, including issues and challenges encountered, contributions, data set, evaluation criteria, MADM techniques, evaluation and validation and bibliography analysis. This study emphasised the current standpoint and opportunities for MADM in the midst of the COVID‐19 pandemic and promoted additional efforts towards understanding and providing new potential future directions to fulfil the needs of this study field.

The coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently transmitting dramatically worldwide, thereby resulting in millions of infections and deaths among the human population. 1 Similar to all global crises in human history, the pandemic is causing unprecedented health and economic disruptions in numerous countries. 2, 3 As a result, centres for disease control and prevention have recommended avoiding any gatherings with more than a specific number of people, including all forms of social activities, schools, and events globally. 4 The majority of countries worldwide have imposed movement restriction orders. 5 is not the first global pandemic to hit the world 6 but is the first one to cause such damage with respect to the economy and loss of human lives. 7 Several different viruses and pandemics, including Ebola, 8 Middle East respiratory syndrome coronavirus and SARS, have emerged in the past. However, all of these socalled pandemics did not have an impact as serious as that of COVID-19. 9 Considering the harm that this virus has caused, the whole scientific community is working continuously towards assisting and helping one another in such times. 10 Medical doctors and frontliners across the globe are dealing with the pandemic and cases at the expense of their health and even lives. 11 Scientists from all domains are proposing solutions to mitigate the effects of this global issue. 12 Accordingly, we were motivated to ask an important question, which is presented as follows.

The new situation is favouring the transition to digital solutions in numerous industries and society as a whole from computer science to artificial intelligence (AI), 13, 14 machine learning (ML), 15 and deep learning 16 to assist with medical decisions and from social science comes solutions to people with behavioural issues from being locked up because of the pandemic. 17 From industrial science, strategies and maintain the economy have also been proposed. 18 Thousands of published studies across all scientific domains are dedicated to COVID-19 and are proposing ideas, solutions, and means to mitigate the effect of the virus. 19 Such widespread participation shows people's solicitude in harnessing their ultimate capabilities to assist with their respected domains. 20 Although those digital solutions have shown considerable significance in mitigating the pandemic, governments and decision makers are sometimes hesitant to make decisions. 21 In this context, another important question is raised as follows.

Closing a country's economy, shutting down operations or even formulating decisions in such times will not only affect the country's economy but might also leave the population vulnerable and endangered. 22 At the same time, governments are not only affected by the difficulty in making such big decisions. At times, even researchers are also incapable of making sound decisions, particularly when several concerns are at stake. 23 Considering this circumstance, certain decisions will have varying effects if they are applied in different settings or even different countries, particularly given the variations in the priorities of people and governments across the globe. Decision-making in itself is no easy task, and every aspect contributing to the action must be carefully measured and considered before the action is applied. Although such consideration can be impossible for each individual to do, this consideration is not impossible for decision support systems once they are fed with the appropriate parameters and aspects to enforce any resolution. 24 However, towards that end, we have to answer another question, which is formulated as follows.

1.3 | How to make a sound decision and what is the suitable technique to achieve that? (MADM) . 26 Such classification considered different purposes and data types. From a practical viewpoint, the MODM is not associated with problems in which the alternatives have been preidentified. 27, 28 In this case, the main concern is to plan/design the most suitable alternative with respect to limited resources. 29, 30 By contrast, MADM is associated with problems in which the alternatives must be identified. In this category, the main concern is to provide a selection/ prioritisation/ranking for a finite number of alternative options. 31, 32 Undoubtedly, the profound capabilities of both groups shine most when needed. Those groups stand on the shoulders of giants during difficult times, and no other time is greater than what we are experiencing right now in the COVID-19 pandemic.

When reviewing the academic literature of MODM, different interesting studies have been published to combat the COVID-19 pandemic. Considering the medical waste management during the pandemic, reference 33 developed a novel model called mixed-integer linear programming for formulating the sustainable multitrip location-routing problem with time windows. Two sequential objectives were achieved by reference. 34 Such objectives are to examine the related literature while systematically focusing on a process approach used in pandemic planning and analyse the pandemic research and prepare a case study for the Czech Republic's pandemic plan using a process approach. Reference 35 presented a deep model approach based on a multilayer long short-term memory network for forecasting medical equipment demand and spread the virus during the COVID-19 outbreak. The authors of [36] explained, formulated and solved the problem of sustainable medical waste management for the COVID-19 pandemic.

From another perspective, several studies declared that the impact of government strategies on fighting the current pandemic has been majorly assisted by MADM methods. Such methods have been proven effective in evaluating and/or selecting various aspects of healthcare datafocused applications. 10 According to the aforementioned significance of MODM and MADM methods in making as much as possible optimum decisions, another question has been raised.

To comprehensively analyse the academic literature studies based on an interconnected and sequential story, the scope of this review exclusively focuses on utilising the MADM methods in different aspects of the COVID-19 pandemic. The reason behind such focus is the significant contributions of research efforts that were performed by scientists by evaluating/benchmarking/selecting different perspectives during the COVID-19 pandemic. In this regard, MADM can be formulated by constructing a decision matrix on the basis of an intersection between the aspects or decisions (i.e., alternatives). That is, a person would like to select their evaluation criteria based on importance, defined as their weights. 37, 38 Their selection can also be represented as the outcome of mental and reasoning processes to determine the most suitable option from several alternatives concerning predefined attributes or criteria. 39 MADM techniques are used in situations that require selecting the most appropriate alternative among several candidates or even ranking or prioritising them on the basis of different evaluation criteria. In its essence, MADM is used in several cases to assist decision makers having difficulty expressing a specific preference for relevant alternatives under several criteria, mainly when relying on unreliable, ambiguous or incomplete information. [40] [41] [42] [43] MADM is unique in finding suitable decision issues with appropriate solutions. This aspect enables MADM to rise across multiple current means for addressing real-life issues, specifically when several factors are influencing the decision-making aspect.

In sum, the role of MADM techniques in solving complex real-world problems controlled by multiattribute to come out with appropriate decisions among available alternatives encouraged researchers to utilise MADM in enormous applications and study it more precisely. The COVID-19 outbreak that affected all life facilities, mainly the health system, led to an unprecedented situation, where decision makers need to make a fast and correct decision. Furthermore, MADM has enormous benefits as support for decision science and has been used in the context of COVID-19 with different applications, such as logistics 44 and health. 10 Identifying all these areas of COVID- 19 where MADM has been used will enable researchers to assess its usage and determine its integration to solve realworld problems in the presence of the pandemic. However, another technical question related to MADM has been raised as follows:

1.5 | How to perform MADM? In other words, what are the methodological steps of MADM?

In terms of how MADM is done, scientists have both agreed and disagreed on the steps needed in this process. Nevertheless, the main flow ( Figure 1 ) of using MADM can be illustrated as follows:

• Decision goals: In this step, a decision maker or a person needs decision assistance by setting goals that suit the decision-making. The goals can be related to selecting and prioritising among alternatives or even weighting a set of criteria. • Identification of alternatives: In this step, a decision maker will make up his/her mind on the items he/she needs to favour or choose from. • Identification of evaluation criteria: In this step, we will determine how to measure the criteria that are to be used in the MADM process. Some criteria are calculated on the basis of a fixed measured value, whereas others are subjectively known. Hence, such a process has to be identified. • Construction of the decision matrix: In this step, a decision matrix will be constructed, which includes all the alternative options to which the authors want to apply MADM and their corresponding influencing criteria. • Selection of MADM methods: In this step, the most suited MADM technique will be selected.

Some MADM methods assign weight (importance) levels to criteria, whereas other techniques deal with the matrix straight away considering that weight is already defined. Some MADM approaches can do both. Thus, selecting the most suitable approach for a specific case is a worthy aspect of consideration. • Normalisation and/or weighting matrices: In this step, the entire matrix is filled with data for both alternatives and their criteria. The data come in various scales and types. Thus, this step aims to normalise the matrix to unify it for the MADM selection process mentioned previously. Moreover, in the criteria weighting performed by MADM weighting methods, the obtained weights for the evaluation criteria can be used and multiplied with the normalised matrix to produce a weighted matrix. • Goal: In this step, the goal of the decision maker is finally resolved by the MADM technique, and the decision maker's desirable outcome is finally apparent across different alternatives/ criteria from which he/she was sceptical to select.

After answering the aforementioned questions, let us close up this section by asking a final important question, which is presented as follows:

1.6 | What are the research novelty and contributions?

To the best of the authors' knowledge, no research attempted to comprehensively review the use of MADM in COVID-19 applications. Thus, the motive behind the work presented in this study is to introduce an intensive analysis of the MADM techniques and demonstrate all the current work directions in the context of COVID-19 to provide an exhaustive analysis for researchers. The contributions of this study are as follows:

1. A comprehensive systematic review and analysis based on the systematic literature review (SLR) protocol are presented to categorise and taxonomise the related literature review into four main categories (i.e., medical, social, economic and technological). 4 . The types of evaluation criteria in the context of MADM based on COVID-19 with their application in each category and subcategory of the proposed taxonomy are mapped. 5. The originality of MADM methods and types with the trend of extending or developing new techniques aligned with the issues and challenges is discussed. The objective is to understand how such methods assist decision makers and researchers across all scientific domains in combating the global issue. 6. Various validation and evaluation approaches are investigated and discussed to prove the reliability of MADM. 7. Bibliography analysis in terms of nationality, journals and MADM environments (i.e., crisp and fuzzy) is presented. 8. Potential future directions and key solutions are presented to solve the uncovered points, particularly in the medical sector.

In this study, the SLR protocol steps were followed. This protocol helps in achieving an exhaustive understanding of the research interest and enriches future studies with further information. Moreover, the well-structured process of SLR compared with traditional review approaches improves the research synthesis in identifying relevant studies based on the identified metrics. The SRL approach is considered an advanced method because of its wide impact on various research fields and scientific disciplines. This approach is mainly composed of a set of processes, including research scope identification, searching mechanism, study selection and extraction and synthesis of information.

The collected information for this study was based on the strategical search method followed by the SLR and meta-analysis (PRISMA) phases, as shown in Figure 2 . Four reliable database search engines were used for searching, filtering, extracting and drafting this survey: (1) 'Scopus', which contains numerous research publications on different scientific domains; (2) 'IEEE Xplore', which includes publication work on multidisciplinary technologies from different domains; (3) 'ScienceDirect', which provides wide access to research works from various academic fields; (4) 'Web of Science' which contains a wide spectrum of works in many fields, such as social sciences, arts, and humanities. The selected databases have widely appeared in numerous published SLRs in high-impact scientific journals, which have academic resilience and scientific soundness. Thus, these databases were considered adequate and most suitable for this review.

The search was carried out on 30 November 2020, followed by two other iterative search rounds on 27 December 2020 and 16 April 2021 to ensure that more updated and recent literature was included. The search was implemented using the advanced search boxes of the search engine databases mentioned above. Boolean operators were used for the search (i.e., AND and OR), and two groups of keywords (i.e., queries) were used in the process, as shown in Figure 2 . The previous process was performed to retrieve the most relevant articles. In searching and filtration, the content based on various types of publications, such as journal articles, conference papers, reviews and research articles, was selected. This option was considered efficient for covering the most recent and related publications in the designated topic of this review.

The research procedure was composed of three succeeding steps, namely, collection of articles, scanning the title and abstract and full-text reading. First, the initial number of collected articles from the nominated databases was 879, and duplicate articles (n = 26) were scanned across all the databases. Second, the abstracts and titles of all extracted articles were scanned on the basis of the inclusion criteria discussed in Section 2.4 to identify the relevant articles and verify their relevance. All articles that matched the inclusion criteria would be included in the final round. Lastly, full-text reading was performed. Any study that did not meet the identified criteria was excluded from this review. Among the collected articles, 720 were irrelevant papers and thus were excluded. The final set of articles that complied with the identified criteria of this review was 51. Full-text reading was performed to extract useful and valuable information (i.e., data extracted) to be analysed for this review.

The defined inclusion and exclusion criteria were imposed while attempting to identify the most related articles during the study selection process. The date of publication was set from the time that COVID-19 made its presence in 2019 until April 2021. According to the additional criteria, all papers, including reviews and research papers, were limited to those in the English language across all the selected databases. The remaining criteria were concerned with the inclusion of all papers on COVID-19 based on MADM techniques, which focused on discussing how MADM was integrated into different applications during the COVID-19 pandemic.

In this stage, 51 articles were classified into four major categories: (1) medical (n = 30), (2) social (n = 4), (3) economic (n = 13), and (4) technological (n = 4). These major categories were linked to their corresponding subcategories while considering the nature of their contribution, where MADM was applied in the presence of COVID-19, as shown in Figure 3 . Furthermore, several sequential analyses were performed to the MADM-based COVID-19 taxonomy. These analyses included issues and challenges (Section 3.1), contribution and description for each included study in such taxonomy (Section 3.2), the data set used in the literature (Section 3.3), evaluation criteria used (Section 3.4), MADM technique used (Section 3.5), validation and evaluation approaches used (Section 3.6) and bibliography analysis for such literature review (Section 3.7).

In this section, the limitations and issues of decision science in the literature review should be identified to understand the role of MADM with respect to the presence of COVID-19 application. By knowing its issues, researchers can either take one of the two different approaches while using MADM. (1) They can begin by working on MADM at the theoretical level and attempt to mitigate the issues of the techniques. Alternatively, they can develop new ones that aim to solve the existing issues in previous approaches. 45, 46 (2) The second group of scientists can opt to work with some of the issues arising while attempting to apply MADM in different applications. 47, 48 In general, most of the general issues and problems of MADM can be identified from the following:

• Multiattribute: a complex decision issue, where a decision is influenced by several factors that cannot be placed on a single scale (e.g., all good or all bad). A conflict among these criteria causes difficulty for humans when making decisions. 49 • Inconsistency: an MADM challenge that mostly occurs in pairwise comparison approaches in practice, wherein a decision maker gives inconsistent comparisons when determining the weight. 49 • Time consumption in a pairwise comparison: an MADM issue, which shows that conducting a comparison is lengthy and at times requires proper explanation to the person making the comparison. 50 • Unnatural comparison: comparing two different quantities is not a natural process. In such a case, a decision-making expert has difficulty providing his/her judgment. Hence, the comparison must be a natural process based on similar quantities to allow experts to have a more instinctive and easier decision-making process. 10 • Vagueness: an MADM issue also known as fuzziness, which is an inherent property of the decision-making process. 'It arises as a result of the psychological perspective of humans or due to the impreciseness in the physical nature of a problem. Along with this property, another psychological aspect, namely, the nondeterministic nature of the decision maker measured by priority index is also closely associated with the process of decision-making'. 51 • Normalisation: using different techniques for the normalisation process might provide different ranking/weighting results. Thus, different evaluation scales must be unified, and their values must be converted into unitless numbers. 10 • Distance measurement: this issue is related to the distance measurement between the ideal solution and the alternatives established. Some MADM methods identify the costs and benefits of evaluation attributes using the Euclidean distance between separate attributes and F I G U R E 3 Taxonomy of multiattribute decision-making-based COVID-19 studies [Color figure can be viewed at wileyonlinelibrary.com] the ideal solution. Conducting two different references for comparing alternatives cause this issue. 10 • Outranking: the issue of rank preservation and reversal is the basis to ensure the effectiveness of MADM methods, which is often a critical issue to make life more organised and orderly. In this issue, prioritising the alternatives is inverted when an alternative is added or eliminated from the list of the established alternatives. 51 • Trade-off: a situational decision that causes reducing or eliminating one quality, quantity or trait of a set or design in consideration of improvements in other aspects. In other words, a trade-off is when one evaluation criterion increases, the other must decrease without any interconnected relation between them. 52 • Conflict criteria: this issue is most similar to the trade-off issue. However, the difference is that the relationship among the evaluation criteria exists, and one criterion might be affected by others. 52 • Importance of criteria: as mentioned before, the decision in MADM shall be made on the basis of different evaluation criteria. However, the importance of such criteria varies from one to another according to the expert preferences as a subjective concept or even on the basis of the nature of the data of each criterion as an objective principle. 52 • Data variation: The data variation issue in MADM occurs when some alternatives have defeated others and could be selected as the best ones according to some criteria, whereas other alternatives can be prioritised over the previous alternatives according to different criteria. 49 Across all the screened and analysed studies, most of the abovementioned issues were apparent in the literature and identified, as presented in Table 1 .

For the issues presented in all previous sections, multiattribute and importance criteria appeared in 100% of all articles within the medical category. As for data variation, four studies had the issue with a 13.33% occurrence rate, followed by a trade-off with nine studies with a 30% occurrence rate. Meanwhile, conflicting criteria and vagueness were present in 15 and 19 studies with a 50% and 63.33% occurrence rate, respectively. For unnatural comparison, only one study reported the issue with a 3.33% occurrence rate. However, time consumption was present in two studies with a 6.67% occurrence rate, and the same outcome occurred for distance measurement and normalisation. The last group was reverse ranking, with three studies and a 10% occurrence rate. For the social science with respect to MADM, the multiattribute issue was apparent in all four studies with a 100% occurrence rate. However, three studies accounted for criteria importance with a 75% occurrence rate, and three studies accounted for vagueness issues. Only one study tackled trade-offs with a 25% occurrence rate. For the economic part of the literature of MADM, multiattribute was again apparent in all studies with a 100% occurrence rate, whereas the importance of criteria dominated in all studies except one (n = 12) with a 92.30% occurrence rate, followed by conflicting criteria with nine studies and a 69.23% occurrence rate. Vagueness occurred in six studies with a 46.15% occurrence rate, followed by data variation and trade-off with two studies with a 15.38% occurrence rate. The last category was technological with a 100% occurrence rate for the multiattribute issue and importance of criteria, followed by conflict and vagueness, which occurred in two studies with a 50% occurrence rate. Furthermore, data variation and trade-off were observed in each study.

All of these MADM issues emerged in numerous studies, and each study in this review dealt with these topics from various perspectives. Some works opted to develop MADM and made it more advanced mathematically, whereas some dealt with these issues on the basis of the nature ALSALEM ET AL. of the criteria and attributes taken into the MADM decision. Others were keen on extending MADM approaches or even combining them to address their respective issues and problems regardless of their occurrence. However, how the studies dealt with the issues represented the contributions that they carried in their respected research. As much as this section addressed these main issues, the following portion highlights how research contributions were made in various fields after addressing the respective MADM issues.

As shown in Figure 3 , the final set of articles was categorised into four major categories, namely, medical (Section 3.2.1), social (Section 3.2.2), economic (Section 3.2.3), and technological (Section 3.2.4). These categories were classified on the basis of a common theme inspired by the reference and agreed upon during the authors' discussions.

This category discussed studies that in one way or another touched on a medical aspect in applying MADM to COVID-19. The section contains a total of 30 studies. The subcategory contains major topics, where MADM was applied with regard to (1) strategies, (2) policies, (3) tools, and (4) services.

The first set of studies discussing MADM with strategies included four studies. In the first study, 49 an MADM approach was used for the selection of sustainable strategic guidelines for the reorganisation of a healthcare system under the conditions of the COVID-19 pandemic. The authors claimed that such an MADM approach would assist in crises, such as the current pandemic, which made the integration of the method necessary for establishing adequate medical care to patients and reducing dangers for the population caused by the COVID-19 pandemic to a minimum. The researchers used four strategic guidelines, which were defined along with five criteria for the evaluation in their study. In the following study, 51 the authors discussed the hospitals' confrontation with a wide range of alternatives represented in preparing preventive measures to confront COVID-19. However, the study did not determine which alternatives were better. The authors used MADM to identify these alternatives, arrange them according to their importance and make a comparison of various preventive strategies between countries globally. In another study, 54 the authors discussed that many health organisations, such as the World Health Organization (WHO), and many governmental agencies were struggling with proper guidelines and remedial activities to reduce the spread of COVID-19. In their pursuit, they indicated that applying all recommendations at the same level by people is an impractical expectation. The reason is the existence of numerous social and physical measures, such as social and physical distancing, wearing antiviral masks, avoiding any unnecessary travel, maintaining hygiene, eating healthy food, and monitoring health conditions. Thus, measures that are more effective in curtailing the spread of COVID-19 should be prioritised compared with those that have less effect. Consequently, an MADM analysis is used to analyse and prioritise the precautionary measures of COVID-19. The fourth study 53 discussed an important issue associated with the presence of COVID-19. The authors claimed that the mental and physical well-being of people under lockdowns has become an emerging concern. Moreover, governments have been continuously pursuing lockdown relaxation efforts for public health and economic restart. Moreover, they have attempted to maintain existing strategies while avoiding succeeding waves of cases. Such serious issue would be considered a conflicting criterion, particularly when the government relies on a trial-and-error approach. Therefore, MADM emerges in scenarios with intertwined causal relationships among guideline protocols for the relaxation strategy.

The second set of studies discussing MADM with policies included three studies. In the first work, 55 the authors claimed that governments are challenged to come up with policies to help reduce the COVID-19 burden on healthcare systems, which have exceeded their capacity. The authors called it the 'flattening the curve' concept. Moreover, the authors looked at the issue and recognised that traditional means are impossible to implement, particularly given the fast spread of COVID-19 and community attributes that influence the spread at the municipal level. Nevertheless, such a position always remains a tough one to make given all these issues, and finding a solution in a short amount of time without causalities is nearly impossible. Once again, MADM is pioneered in scenarios regarding infected patients and bed capacity to provide support for effective public health policies for national, regional and local intervention. In the following study, 56 the authors asserted that their ultimate and harmonic integration is needed considering the importance of emergency medical services during the COVID-19 pandemic. They highlighted that the response to the pandemic affects the rate of mitigating its problems. They also discussed methods to respond effectively to the urban epidemic situation during a complex environment, which has become a global challenge. Moreover, emergency decision-making can be considered an MADM problem, which involves multiple criteria or attributes with qualitative and quantitative aspects. Therefore, by using MADM, we can evaluate the severity of the urban COVID-19 epidemic situation within a complex humanitarian crisis environment. The last study in the policies subsection by Ashraf et al. 57 discussed the importance of controlling the spread of COVID-19 in emergencies worldwide. The authors claimed that emergency events are known to have aspects of short duration and data, harmfulness and ambiguity. Policy makers are often rationally bounded under the uncertainty, complexity and vagueness of the information and issues. Decision issues become complex, making them priorities that need to be addressed. Therefore, these authors posited that MADM for controlling the transmission and spread of COVID-19 is a very effective approach that can also be used for other complicated problems, such as risk evaluation, emerging technology, uncertain decision-making, project installation and site selection.

The third set of studies discussing MADM with tools included nine studies. The first two studies discussed masks. In the study of Yang et al., 58 the issue of mask selection during the COVID-19 pandemic was discussed. The authors argued that antivirus mask selection given the shortage of these tools during the pandemic is a serious concern. People often believe that the best masks are either expensive or only those used by frontliners. This misconception leads to the inappropriate and excessive purchase and the use of personal protective equipment, which has exacerbated the mask shortage. In reality, masks should be considered with factors, such as reusability, quality of raw materials and the situations of people wearing them to optimise their use and allocate medical resources in the best way possible. When such an issue arises, MADM assists in selecting a reasonable antivirus mask on the basis of the needs and situations of different people, thereby ensuring practical significance. Another work 59 expressed that considering the COVID-19 pandemic, the task of choosing the right face mask has become a problem that numerous people face because of the lack of specific standards. This problem has been addressed using MADM. Another mask-related work was presented by Yang et al., 64 wherein the authors discussed the importance of mask selection during the COVID-19 pandemic. They utilised MADM techniques to select among the six most common and available masks. They also analysed the advantages and disadvantages and also made a comparative analysis with graphical interpretation. Aside from masks, other authors discussed the diagnosis tests for COVID-19. The authors in Sayan et al. 60 stated that diagnosing patients quickly and accurately is important in their treatment. Moreover, the existence of several diagnosis tests creates confusion in selecting the most appropriate method. Prioritising one over the other is a difficult decision, particularly given various parameters, such as high sensitivity, high specificity, low false positivity, low false negativity, high usability and low cost. Therefore, the authors considered the MADM solution in the evaluation of the effectiveness of seven different diagnostic tests for COVID-19. They concluded that the effectiveness of diagnostic tests varies according to the patient's specific condition and each country's own resources. In Mohammed et al., 61 the authors shed light on the fact that COVID-19 diagnosis has been widely applied with the use of AI models. These intelligent methodologies are highly desirable in helping health organisations in the selection of a COVID-19 diagnosis system. However, similar to any other technology, they are ambiguous and have drawbacks, hindering their ultimate usage. The authors claimed that owing to the availability of several ML models, selecting one over the other method is no easy thing to do, and evaluating and benchmarking COVID-19 ML models are considered their main challenges. They argued that multiple conflicting criteria exist in the evaluation and benchmarking process, and in this regard, MADM comes as an intermediary in addressing such decision issues. Another study in this subcategory 62 argued that accurately diagnosing COVID-19 using CT is sometimes difficult and uncertain as the symptoms for the disease have similarities with other viral lung diseases, namely, H1N1, H5N1, SARS, and hantavirus. Therefore, the authors recognised the challenge of such a decision problem and agreed that CT imaging results would be identified as a decision issue with multiple conflicting criteria. Hence, they pioneered the use of MADM in the context of environmental uncertainty, wherein the symptoms and CT imaging results are the criteria and the alternatives are the viral lung diseases, including COVID-19. The authors believed that such work can assist medical staff in monitoring the spread of COVID-19 with accurate results of the effectiveness of the evaluation. Another interesting work 52 discussed the potential of using MADM in the detection of COVID-19. The authors proposed a methodology with three phases for evaluating and benchmarking the COVID-19 AI classification techniques, which were applied for diagnosing the disease. 'The researchers discussed the phase of identification, which illustrates the data sets and requires preprocessing, and identified the evaluation criteria used in the evaluation and benchmarking of COVID-19 AI classification techniques and the number and type of techniques.' The output of the phase had four decision matrices, which were followed by the integration of MCDA methods to deal with the issue. The final phase in their work included the objective and subjective validations for ranking COVID-19 AI classification techniques. Another diagnosis study 63 presented a strategy based on the operators to tackle MADM issues. The authors used COVID-19 as a practical example for the selection of a suitable laboratory for testing to demonstrate the application of their proposed strategy. The last study in this category 65 presented that the emergence of COVID-19 led to many casualties globally. The authors discussed the importance of detecting COVID-19 in patients and proper treatment along with awareness because it can assist to control COVID-19. Therefore, the authors proposed the fuzzy cloud-based COVID-19 diagnosis assistant, which aims to distinguish patients as confirmed, suspected or probable COVID-19 cases. The diagnosis tool categorised the patients into four categories as mild, moderate, severe, or critical. The tool was proven in successfully monitoring the COVID-19 pandemic and reducing its transmission rate among society.

The fourth set of studies discussing MADM with services included 14 studies. The first three studies discussed patients. The authors in Shirazi et al. 50 discussed patients' dissatisfaction with hospital services in the presence of COVID-19. They indicated that despite having resources and existing medical capacities, dissatisfaction with improper allocation hinders patients from having the ultimate health experience with respect to their needs, desires and expectations. They argued that service factors from the patients' viewpoints varied and that prioritising them would maintain the quality of the service. Therefore, MADM was introduced in such a scenario to mitigate the decision issue. The approach helped to identify which satisfaction factors were to be prioritised under normal conditions and during the COVID-19 pandemic to succeed and survive in today's competitive environment. Another study 37 argued that COVID-19 causes variations in the laboratory examinations of patients. The authors shed light on the speed of geographical expansion and the sudden increase in the numbers of infected patients. This rise should quickly emphasise the growing number of patients, thereby leaving individuals at medical institutions with tough decisions. Therefore, medical professionals have opted to use MADM to prioritise patients while considering the urgency for solutions to minimise risks for deteriorating conditions. The latter was proposed to overcome the challenges and complications of patients with severe COVID-19 on the basis of laboratory examination criteria, which assist in differentiating among mild, serious, and critical COVID-19 conditions. Such an approach can be used later to assign appropriate care with prompt and effective treatment. The last patient study 66 revealed that given the rapid spread of the global pandemic, numerous scientists are doing their best to tackle this crisis. The authors claimed that new challenges are outlined from various medical perspectives, thereby causing several decision issues and challenges that require a novel design solution, particularly for asymptomatic patients because of multilaboratory criteria, criterion importance and trade-off among these criteria, which are challenging to differentiate. The researchers used integrated MADM to handle the prioritisation of patients with COVID-19 and detect the health conditions of asymptomatic carriers. Other studies discussed treatment, particularly the study of Albahri et al. 10 who presented interesting ideas. The authors claimed that people who have recently recovered from COVID-19 have antibodies against the virus circulating in their blood. Transfusing these antibodies to deteriorating patients could theoretically help boost their immune systems. At the same time, decision issues arise as to whom the antibodies should be administered first given all the biologically different and conflicted criteria and the compliance with national health requirements and known standard routine procedures. The authors proposed a rescue framework for the transfusion of the best convalescent plasma (CP) to the most critical patients while considering ML and MADM, which can greatly assist the health sector when such decisions are to be made. Another study 67 discussed exploring the most efficient method for surviving COVID-19 in addition to medical treatments. For this purpose, the authors introduced a novel concept of the MADM technique and explored the most effective alternative cure for COVID-19, which is undoubtedly based on the theory and work they applied. Another treatment study 68 stated that 'as COVID-19 spreads across the world, the transfusion of efficient CP to the most critical patients can be the primary approach to preventing the spread of the virus and treating the disease. This strategy is considered an intelligent computing concern where several criteria affect the decision. Therefore, the authors proposed a novel CP-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on the matching component process to provide CP efficiently from eligible donors to the most critical patients using MADM methods.' In this category, another study 69 discussed that given the presence of COVID-19 vaccines and with the need to provide all the people of a country with vaccines, governments must identify priority groups for allocating COVID-19 vaccine doses. Therefore, the authors formulated strategies with the assistance of MADM methods to classify and rank the most deserving groups in society so that they receive the vaccine first. Similar to Mishra et al., 76 more treatment work discussed that choosing the best antiviral therapy to treat the mild symptoms of COVID-19 was quite a complex, challenging and uncertain decision. The reason is the inexistence of ultimate medication for COVID-19 at that time and the emergence of other forms of treatment including several antiviral therapies, which were used to treat the mild symptoms of COVID-19 disease. In that respect, the authors used hesitant fuzzy sets (HFSs) as part of hesitant fuzzy decision-making, which have been proven effective and valuable to express uncertain information in real-world issues, to choose five methods or medicines to treat the mild symptoms of COVID-19 and demonstrate the practicability and efficacy of the developed idea in real-life applications. Another treatment study 74 discussed that owing to the impact of COVID-19, treating patients with possible medicines is warranted, but at the same time, people who have COVID-19 infection will suffer from fever, cough, shortness of breath, dyspnoea, and other symptoms. Unfortunately, no specific treatment is available for diseases caused by COVID-19. In that regard, MADM was utilised for the medicine selection of COVID-19. In the following topic for resource utilisation, the authors in De Nardo et al. 70 raised the issue of hospital admission during the COVID-19 pandemic with a hospital-bed shortage. The researchers claimed that for critically ill patients, this shortage has been a major challenge because even countries with robust healthcare systems and strong economies can be rapidly overwhelmed by such emergency, let alone those in low-and middle-income settings. Prioritising access to care in these situations is considered a serious issue with various criteria influencing the decision. To address the latter, MADM has been proven useful in such context and used in identifying noncritical COVID-19 patients who should be admitted to hospitals because of the risk of rapid clinical deterioration and in improving the response of health systems. The next study regarding hospitals and their resources 71 suggested that the unexpected emergence of natural and man-made disasters globally is worth discussing, particularly the importance of hospital preparedness, which is the first reference point for people to obtain healthcare services. At the same time, numerous uncertainties and decision issues are emerging, thereby hindering the ability to grasp what these hospitals can truly do. Therefore, identifying hospitals with low levels of preparedness will be crucial for disaster preparedness planning. The authors argued that the healthcare sector is currently facing a great struggle because of the COVID-19 pandemic. Evidently, preparing hospitals for such disasters should be made quickly and reliably. Therefore, MADM has been used to evaluate hospital disaster preparedness and has been proven to be a suitable approach considering that methods measuring hospital disaster preparedness levels are lacking. Another study 72 discussed the importance of hospital selection in times of COVID-19. The authors highlighted the hospital selection problem for COVID-19 when the pandemic had broken out in Wuhan, China. At that time, the government stressed to 'leave no one unattended'. Therefore, the Chinese government ordered the design and conversion of makeshift hospitals in Wuhan. Undoubtedly, this effort raised serious decision problems on hospital selection to efficiently provide treatment for COVID-19 patients with mild symptoms. In that regard, MADM was used to select the best among many available options. More resource works were concerned with different topics, including the disposal of medical waste. The authors 73 discussed that 'many authorities especially in the developing nations are battling to select the best health care waste (HCW) disposal technique for the effective treatment of the medical wastes during and post COVID-19 era'. In that regard, the selection of the best technique requires considering various tangible and intangible criteria, which can be framed as an MADM problem. Therefore, the authors proposed a framework for the selection of the best HCW disposal technique on the basis of socio-technical and triple bottom line perspectives. Another work 75 discussed the importance of recognising various dimensions of seven basic types of waste in healthcare processes. In that regard, MADM was used to prioritise the identified wastes and their dimensions. The authors also developed a framework of leanness assessment and validation for the healthcare system.

This category discussed studies that one way or another touched on a social aspect related to applying MADM with COVID-19. The category contains four studies. In the first work, 77 the impact of COVID-19 on numerous vital centres worldwide must be examined by determining the social factors that countries are facing. The most critical issue among these social aspects is still unclear. Therefore, MADM has been applied to reach a logical arrangement of these factors and address the corresponding problem. As a result, several preventive measures have been classified to confront COVID-19. The cases have several differences, and the best procedure has not been identified. Hence, MADM has been used to explore the best preventive method. The next study 78 indicated that given the emergency factors that increased during the COVID-19 pandemic, several organisations have suffered from decision-making problems for their emergency operations associated with these times. Therefore, to address these issues, the authors used the MADM technique to discuss the decision-making process under uncertainty, which can assist them in decision-making problems in emergencies. Another work 80 discussed COVID-19 risk factors. The authors indicated that the COVID-19 outbreak in December 2019 severely affected the world. The epidemic is spreading fast through various manners as the virus is very contagious. The authors discussed that one method of containing the virus is to sort out the risk factors and rank them in terms of contagion. Therefore, they evaluated risk factors involved in the spread of COVID-19 and ranked them using MADM methods. The next work 79 discussed the risk level for musculoskeletal disorders among handheld device users. The authors indicated that owing to COVID-19, 'The work-from-home (WFH) situation increased the handheld device (HHD) users to work in uncomfortable postures for longer times because of the absence of proper designed workstations. This issue leads to different type of musculoskeletal disorders (MSDs) among the HHD users. For that, the authors proposed multicriteria decision-making approach for identifying the risk level of MSDs among HHD users'.

This category discussed studies that in one way or another touched on an economic aspect related to the application of MADM on COVID-19. A total of 13 studies were obtained. The subcategory contains major topics, where MADM was applied with regard to (1) supply chain, (2) green economy, (3) transportation, (4) environment, and (5) business and corporations.

The first set of studies discussing MADM with the supply chain included five studies. Grida et al. 44 shed light on the disruption caused by COVID-19 and its economic impact. They argued that the COVID-19 crisis in supply chains is derived from three main aspects: (1) supply, (2) demand, and (3) logistics. Several works in the literature have investigated the effect of preventive strategies on supply chain policies in light of COVID-19. In this regard, MADM was used to generate sufficient information for decision makers in industrial firms to address uncertainty issues, which are considered high-decision issues associated with the three supply chain policies mentioned previously. The next study 81 explored the issues that disrupted supply chain agility for pharmaceutical companies during the COVID-19 crisis and the vital problems related to the priority of factors that affect supply chain agility for pharmaceutical companies, specifically because of the slow financial turnover in pharmaceutical companies and their need for urgent financial resources. Thus, MADM was used to increase forecast accuracy, lower production costs, optimise the use of resources and set the criteria for selecting the perfect suppliers to increase speed and flexibility in production. Next, the authors in Sharma et al. 82 explained that the COVID-19 pandemic has created problems for supply companies regarding flexibility and response during and after COVID-19 in terms of building systems that are more responsive and resistant to future crises of industrial companies. Therefore, MADM was used to identify the most important factors that help rebuild industries and societies to resist crises and be more flexible. In the last two supply chain studies, the authors in Khurana et al. 83 discussed numerous unprecedented risks during the COVID-19 pandemic resulting from disruptions in agricultural supply chains. In addition, the critical risks during this global challenge remain unclear. Using MADM methods, the risks of agricultural supply chains were determined by the order of importance, namely, demand risks, financial risks, logistics and infrastructure risks, management and operational risks, policy and regulation risks and biological and environmental risks. Another supply chain disruption work 84 discussed the importance of electronic products to society, but the COVID-19 situation pushed almost all human direct communication to become virtual (i.e., online). This event indicated that the supply of critical raw materials for electronics manufacturing is becoming increasingly precarious to social, geopolitical, and technical disruptions, which are more likely to be increased in the future because of the global health crises and their impact on climate change. In that regard, the authors 'investigated supply chain disruption risks in the electronics sector by applying metrics that capture supply, demand, socio-political, and environmental risks in a multicriteria framework using TOPSIS to almost 40 metals and minerals that provide critical functionality to electronic products'.

Aside from the supply chain, another topic for the green economy was also discussed in Shah et al. 85 The authors claimed that the COVID-19 situation constitutes an opportunity to shift towards a green economy because of the curfew issues that have affected various companies, constituting a favourable opportunity to reduce carbon production. However, the problem that companies face lies in setting priorities for waste-to-energy conversion. Thus, MADM with fuzzy priorities for converting waste to energy was formed to reduce uncertainties regarding the most appropriate technology for the conversion. Another green energy work was discussed by Yao. 86 The authors argued that COVID-19 causes serious threats not only to global health but also to the worldwide development regime. 'The experts, economists, policymakers, and the governments expressed their pledges and determinations to adapt and mitigate climate change, making them start adopting green growth and development strategies, and one of the major challenges has been promulgating and strictly implementing environmental regulations and policies for green growth and development'. In that regard, they utilised MADM to analyse environmental regulation. They identified alternative strategies to achieve while considering different main and subcriteria in the context of environmental regulations.

Another economy-related topic associated with transportation was discussed in Moslem et al. 87 The authors believed that countries suffered because of the transport sector's sustainability and faced numerous environmental challenges, which were caused by the challenge of identifying transportation alternatives. This problem was observed after imposing social distancing measures owing to COVID-19. To determine transportation alternatives, MADM was applied to identify options to provide effective solutions for selecting a sustainable situation and improving the urban context. The next work 88 comprehensively studied the changes in environmental conditions caused by the imposition of lockdowns because of COVID-19. They had different alternatives in their study. By relying on an MADM method, the authors were able to derive the weight of indicators to study them properly. They also gained an understanding of the environmental response to the COVID-19-pandemic-imposed lockdown situation and assessment after the lockdown situation.

Furthermore, four studies discussed business and corporations. In Altuntas and Gok, 91 the authors discussed another important use of MADM given the gap in the scientific literature with respect to making the right quarantine decisions to decrease the negative effect of a pandemic on the hospitality industry. The authors claimed that quarantine decisions during a pandemic should be taken by using the systematic method, and no universal guideline regarding such thing exists, specifically during the COVID-19 pandemic. Towards this end, the authors used the decision-making trial and evaluation laboratory (DEMATEL) method to help countries reduce the negative impact of quarantine decisions on the hospitality industry because of the COVID-19 pandemic. Another work 89 discussed that owing to the emergence and impact of COVID-19, each country's economic development was affected, and all governmental agencies are encouraging the construction industry to develop an advanced infrastructure related to health, transport, education, and housing, particularly with regard to the operations and financial performance of construction sector companies. Therefore, the MADM process was utilised because no comprehensive study was conducted on the evaluation of the financial performance of construction companies. The authors determined the priority of financial ratios and the ranking of construction companies with the proposed techniques. The next work 92 discussed that the medical sector has been affected by COVID-19-related decisions and issues that were also reflected in business and the economy. They discussed the insurance business and elaborated that assessing and ranking private health insurance companies provide insurance agencies, insurance customers, and authorities with a reliable instrument for the insurance decision-making process. In that respect, insurance companies were ranked on the basis of their healthcare services provided in Turkey during the COVID-19 outbreak through an MADM. The authors concluded that the introduced approach met the insurance assessment problem during the COVID-19 pandemic. In Duan et al., 90 the authors discussed the impact of COVID-19 on electric power grid investment. 'They discussed that owing to COVID-19 socioeconomic development is undergoing changes in China, and it is in that regard urgent to evaluate the risk of electric power grid investment in China under new socio-economic development situation, which can help the investors manage risk and reduce risk loss. In their work, the MADM method was proposed for risk evaluation of electric power grid investment in China under new socio-economic development situation'.

This category discussed studies that in one way or another touched on a technological aspect related to the application of MADM. A total of four studies were obtained. In the first work, 93 the authors discussed that wastes pose a great risk not only to the environment but also to public health. Particularly with the COVID-19 outbreak, the proper disposal of waste has become a vital risk to people's lives. Hence, one problem is related to defining the criteria for a smart system for the disposal of medical wastes of industrial companies. By using MADM to determine the relationship between healthcare centres and waste disposal companies, the researchers could mitigate the issues and resolve them in the best way possible. The next study 94 indicated that telecom service providers have been facing an unprecedented challenge to meet customer demands during the COVID-19 period because of the increase in employees working from home. Users face challenges related to the price and quality of these services. Therefore, the use of MADM was discussed to develop methods to support end-users. The third work 95 explained that digital technologies are considered one of the essential aspects of daily life at present. However, the emergence of COVID-19 has created numerous challenges in terms of strengths, weaknesses, opportunities, and threats. Thus, the application of MADM was discussed to determine the importance of these factors in the health sector in relation to digital technologies. The researchers concluded that the first factor is health information systems, followed by the lack of digital knowledge, digital stratification, and economic interventions. Another technological aspect was more related to using online sources and technologies for educational purposes. The authors in Gong et al. 96 discussed that 'network teaching has been widely developed under the influence of COVID-19 pandemic to guarantee the implementation of teaching plans and protect the learning rights of students'. They also shed light on the existence of various online sources in that regard. Moreover, selecting a particular website for online teaching can directly affect the performance of educators and students and promote the quality of online teaching. Consequently, e-learning website selection can be considered an intricate MADM problem, and experts' evaluations of the performance of e-learning websites are often imprecise and vague because of the subjectivity in human thinking.

In this section, studies on various COVID-19 applications based on MADM approaches are discussed and analysed in terms of whether the data set used or suggested to use multiple factors represented by the primary data set, secondary data set, sample size and number of experts. The primary data represent the data set collected during the research and approved by the ethics committee. Meanwhile, the secondary data are those adopted and obtained by other researchers to help authors address multiple problems and test their approach. Table 2 presents a summary of the data sets used in the literature. 52 Table 2 presents various employed data sets based on each category and subcategory of the proposed taxonomy. In the literature, the experimental test was implemented either using a primary or secondary data set or both. 97 Out of 51 studies, two studies 10,52 did not include the primary and secondary data sets in their study design. The authors in Albahri et al. 52 proposed a theoretical framework based on MADM methods as a future direction to evaluate and benchmark AI diagnosis techniques used in classifying COVID-19 medical images. Meanwhile, the authors in Albahri et al. 10 stated that the developed MADM framework has not been tested using a data set of infected cases because of the lockdown and global pandemic outbreak. The majority of the 49 remaining studies (36; 73.5%) used only the primary data set, nine studies (18%) combined primary and secondary data sets and four studies (8%) used only the secondary data set in the experimental test. Furthermore, most researchers were keen to publish the data ALSALEM ET AL. set and present it within the study work. However, only eight studies (16%) presented the data sets and the results partially. In general, the availability of the data set is either fully presented, partially presented or not shared, which mainly followed the journal policy of the availability statement. However, the researcher can access the data set that was not directly published either through contacting the authors, referring to the supplementary file or contacting the journal itself. Most of the time, the data set would be shared if requested, unless if it includes sensitive data. 98 

This section analyses previous studies in the literature that focused on using decision-making with COVID-19 in terms of the evaluation criteria used in each case study. The extracted criteria were classified within the two main types of evaluation criteria: subjective and objective criteria. The researchers used either the first or the second type or the combination of the two types depending on the research in question. Table 3 illustrates the evaluation criteria used in each of the taxonomic categories shown in Figure 3 . Furthermore, subjective criteria are defined on the basis of human judgment or perception, which rely on individuals' tastes and preferences. These factors reflect some desirable properties of the object that need to be evaluated. By contrast, objective criteria are defined as factual, measurable information. Their characteristics are evident and do not rely on human judgment or perception. Table 3 shows the distribution of objective, subjective and hybrid criteria (a combination of subjective and objective criteria) that were used by the studies included in this review (Table A1 in the appendix A illustrates the description of the main and subcriteria of each reviewed study). Of the 51 studies, 14 studies representing 27.45% of the exhaustive studies used objective criteria. Then, 22 studies comprising 43.13% used subjective criteria. Meanwhile, 15 studies constituting 29.41% used hybrid criteria. Figure 4 depicts the visualisation of the overall distribution of objective, subjective and hybrid criteria used in the literature. In the medical category, which represented 58.82% of the total studies, 8 studies representing 26.67% used objective criteria, 11 studies representing 36.67% used subjective criteria and 11 studies representing 36.67% used hybrid criteria. The distribution of objective criteria in terms of numbers and percentages among the medical subcategory revealed that the objective criteria were most frequently used in the services subcategory with 17% of the total studies in the medical category. This group was followed by the tool subcategory with 7%, whereas the policies subcategory comprised 3% of the total studies in the medical category. By contrast, subjective criteria were most frequently used in the tools subcategory with 17%, followed by strategies with 13% and services with 7%. Most hybrid criteria were used in services with 23%, followed by policies and tools with 7% each in the total studies of the medical category.

Furthermore, Figure 5 shows the distribution of the mentioned criteria for each category and its subcategory individually. In the economic category, which represented 25.49% of the total studies, nine studies representing 69.23% used subjective criteria, two studies representing 15.38% used objective criteria and two studies representing 15.38% used hybrid criteria. The distribution of subjective criteria in terms of numbers and percentages in the economic subcategory demonstrated that subjective criteria were most frequently used in the supply chain subcategory with 31% of the total studies in the economic category. This group was followed by the green economy, environmental, transportation and business and corporations subcategories with 8% each, as shown in Figure 5 . In the social category, which represented 7.84% of the total 

This section discusses the application of different MADM techniques in solving issues related to COVID-19 in the medical, social, economic, or technological sectors, which were considered in the proposed taxonomy of this study. In MADM, all possible options are evaluated against each criterion. These assessments help to make the right decision and select the most suitable option(s). Theoretically, MADM techniques have three different directions: ranking, weighting, and hybrid methods. Ranking methods work on prioritising the alternatives or distinguish the most suitable alternative among others, whereas weighting methods compute the weight of each criterion on the basis of the related importance against other criteria. Hybrid methods take advantage of integrating ranking and weighting methods together. 78 Notably, some MADM methods, such as the analytic hierarchy process (AHP), 69 subjective and objective decision by opinion score method (SODOSM), 10 and weighted aggregated sum product assessment (WASPAS) 95 were developed to accomplish both tasks, compute criteria weights and rank alternative priorities. Moreover, weighting methods can be categorised into three different types: subjective, objective and hybrid weighting methods (both subjective and objective weighting). Objective weighting methods use the data values for each criterion to measure the weights of the criteria. 61 For subjective weighting methods, the accumulated expert consensus is used to assign and calculate the subjective agreements for each criterion. The hybrid weighting methods use the characteristics of both previous methods. That is, subjective and objective processes are combined in a single method as in SODOSM, 10 Alternately, subjective and objective methods are integrated as in the level-based weight assessment (LBWA) model and an objective method called measuring attractiveness by a categorical-based evaluation technique (MACBETH) method 49 and AHP with spherical fuzzy entropy. 78 As mentioned in Table 1 , assessing the importance of related criteria is one of the main issues in solving COVID-19-related problems in the medical, social, economic or technological sectors. Thus, the distribution analysis of MADM techniques under each category of our taxonomy is unnecessary because all 51 studies computed the weight of related criteria and ranked its studied alternatives in one way or another. Moreover, the applied weighting methods were a combination of subjective, objective and hybrid weighting methods (Table 4) .

Of the 51 studies, 41 representing 80.39% of the exhaustive studies used subjective weighting methods. Six studies representing 11.76% used objective weighting methods, and four studies representing 7.84% used hybrid weighting. The distribution of subjective methods in terms of numbers and percentages showed that the direct weighting method (also called the direct rating method) 99 was the most frequently used among the studies with 34.15%. In this method, the rate/importance of each criterion was directly assigned by the decision maker(s). This criterion was followed by the fuzzy-AHP (F-AHP) with 17.07% of the total studies; AHP with 14.63%; the best-worst method (BWM) with 9.76%; and fuzzy analytic network process, DEMATEL, stepwise weight assessment ratio analysis, neutrosophic AHP, linguistic HFSs BWM (LHF-BWM), Bayesian BWM (BBWM), trapezoidal interval type-2 fuzzy BWM (TrIT2F-BWM), fuzzy assessment (FA), HF-divergence measure and IF-MARCOS with 2.44% for each method. The distribution of objective methods demonstrated that the entropy method was the most frequently used among the studies at 66.67%. Potentially, all pairwise rankings of all possible alternatives (PAPRIKA) and fuzzy linguistic quantifier order weighted aggregation (FLQOWA) followed the entropy method with 16.67% each. In the same line, the distribution of hybrid methods including SODOSM, LBWA with MACBETH, AHP with spherical fuzzy entropy and cross-entropy of probabilistic linguistic z-number with unbalanced semantics (UPLZs) was equal to 33.33% frequency each. In light of the hybridisation definition mentioned before, SODOSM is a hybrid method that combines two assessment processes (subjective and objective) in a single method. In contrast to LBWA with MACBETH and AHP with spherical fuzzy entropy, the subjective and objective methods are integrated to perform the required assessments.

For the distribution of MADM ranking methods used by studies included in this review, 10 out of 51 studies representing 19.61% from the exhaustive studies used the TOPSIS ranking method two times separately and were integrated eight times with the weighting method. Four studies representing 7.84% used the F-AHP method for ranking, four studies representing 7.84% used VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and three studies representing 5.88% used AHP. The rest of the ranking methods include the following: TODIM, compressed proportional assessment (COPRAS), spherical normal fuzzy, intuitionistic fuzzy set (IFS), fuzzy preference ranking organisation method for enrichment of evaluations, fuzzy Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval (fuzzy RAFSI), IF-DEMATEL, Fermatean fuzzy sets (FFNs), preference ranking organisation method for enrichment of evaluations, SODOSM, PmFT-TOPSIS, PAPRIKA, F-TOPSIS, FLQOWA, BWM, F-VIKOR, DEMATEL, WASPAS, LHFSs-TODIM, IMEEM, HFS-TOPSIS, complex q-rung orthopair uncertain linguistic weighted partitioned Bonferroni mean (CQROULWPBM), multiobjective optimisation on the basis of a ratio analysis plus the full Noticeably, integrating TOPSIS as a ranking method with weighting methods was the most common practice in this review compared with other MADM methods with 42.30%.

On the other hand, the distribution of originality of MADM methods showed that 63% of the overall studies used an existing method, 25% of the studies extended an existing method and 12% proposed new MADM methods. The devastating effect of the COVID-19 pandemic has created an unprecedented situation that needs a professional decision support system that can effectively handle the related issues mentioned in Table 1 . The result of this review revealed 13 extensions of existing methods, namely, LBWA-MACBETH-RAFSI, 49 PmF-TOPSIS, 67 WASPAS, 95 AHP-Entropy-TOPSIS-GRA, 78 distance measure for IFSs, 59 LHF-BWM-TODIM, 96 BBWM with improved matter-element extension model (BBWM-IMEEM), 90 cross-entropy of UPLZs-MULTIMOORA, 74 Entropy-F-VIKOR, 89 FAHP-HFS-TOPSIS, 80 HF-divergence measure-HF-ARAS, 76 TrIT2F-BWM-VIKOR, 72 and IF-MARCOS. 92 These methods mainly focus on handling the inaccuracies and uncertainties (vagueness issue) mentioned in Table 1 . 

Among the six new proposed MADM methods that were developed to overcome the limitation of the existing methods, five were under the medical category, namely, SODOSM, 10 FFNs, 63 spherical normal fuzzy weighted Bonferroni mean (SpNoFWBM), 58 FA, 65 and CQROULWPBM, 64 whereas FLQOWA 83 was under the economic category. SODOSM 10 was designed to provide better services in the medical sector, which can compute the importance of various criteria with their values within CP and patient decision matrices. In addition, SO-DOSM can calculate the weight of these criteria subjectively and objectively without increasing the inconsistency in the reliability of the output of decision-making in ranking the priority of alternative CPs/patients. FFNs 63 use the powerful FFSs in handling uncertain information to provide a better decision in selecting an authentic laboratory for the COVID-19 test. The SpNoFWBM 58 uses the Bonferroni mean operator to improve the process of selecting better antivirus masks similarly to CQROULWPBM. 64 FA 65 is used to improve the identification process and provide fast remote diagnosis assistance on the cloud. Lastly, FLQOWA 83 is used to solve economic problems where the information is imprecise and under uncertain conditions. FLQOWA combines two operators (fuzzy ontologies and aggregation) to provide a very powerful solution to decision problems and improve the reliability of the results.

In general, the validity of any proposed work (e.g., method, technique, model and framework) is a crucial step; this vital process becomes indispensable with regard to human life. MADM methods tackle numerous COVID-19 cases in different categories related to multiattribute complex problems where decision-making is a challenge. However, the development of a new decision support approach cannot be applicable unless the evaluation and/or validation is achieved. In the literature included in this study, several approaches were used to evaluate and/or validate the proposed decision support approaches. Notably, the evaluation and/or validation in MADM coincides with the principle of testing the reliability and stability of the decision results. 56 Six common approaches are presented to evaluate and/or validate the MADM results in the related literature as follows:

• Comparative analysis: One of the most common approaches to verify the strength, steadiness and validity of a proposed model, method or technique in MADM is comparative analysis. In this approach, the superiority of the proposed decision support approach is tested compared with the existing one. The proposed and existing competitors are exposed to the same test situation and circumstances and applied to the same data set to guarantee the efficiency and reliability of the suggested one. 57 • Objective validation (mean ± SD): This statistical analysis is applied to validate the ranking results of the proposed MADM framework. In this approach, the rank results of the alternatives are divided into (n) groups. Each group consists of a set of the studied alternatives. The mean ± SD is used to compute each group accordingly using Equations (1) and (2) .

For example, suppose we divided the rank results of the alternatives into three groups, according to this division, the best value should be for the first group, which must be supported by the mean ± SD result. The statistical measurement of all following groups should be poorer than the first and preceding groups, whereas such measurement should be better or equal to the posterior groups to ensure that the alternative prioritisation undergoes systematic ranking. 52 • Subjective validation (feedback and evaluation from specialists and experts of the study field): This approach depends on the subjective judgment of the specialised experts who are responsible for evaluating and/or validating the outcome of the proposed method. The central and crucial part of this approach is the characteristic and standardisation method applied to determine the experts of the studied field. On this basis, the selected experts will be involved in the evaluation process to provide their feedback and judgment subjectively. 52 • Sensitivity analysis (tweaking the parameters): This analysis tests the influence of changing parameters on the decision-making results. The values of the operator's parameters (p q , ) within a specific interval are tweaked accordingly to observe their impact. This sensitivity can indicate and evaluate the validity and stability of the ranking results in the proposed methodology compared with other MADM methodologies by competitors. 56 • Experimental evaluation (case study): In some studies, the experimental assessment by deploying a case study of a real or laboratory data set is used to evaluate the effectiveness of the proposed decision support systems, particularly if the study tests community behaviours or examines the accuracy of diagnosing diseases, as in Pamučar et al. 49 • Spearman's rank correlation test: This correlation test is used to examine the difference in the ranking results of the proposed MADM method in competing with existing methods. Spearman's rank correlation test is formulated in Equations (3) and (4). (3)

where r Z and s are two indicators to assess the relationships between two ranking results. n is the number of alternatives, d i is the degree of the difference between two comparative ranking results for each alternative and ∈ r [−1,1] s . The strength of this relationship increases whenever the value of r | | s is closer to 1. The value of Z if equal or greater than 1.645 indicates a positive relationship; otherwise, the rankings fluctuate. 58 As shown in Table 5 , the studies included in this review applied different approaches to evaluate and/or validate the decision results. Of the 51 studies, 28 included the evaluation and validation procedures in their process for conformity. In the taxonomy categories, 16 out of the 28 studies with a 57.14% occurrence rate that applied the verification process to their results were under the medical category; six with 21.42% occurrence rate were under the economic category, whereas three studies in each social and technological category with 10.71% occurrence rate used the evaluation and validation process. Unsurprisingly, the comparative study was the most common and dominant approach for testing the reliability and efficiency of the MADM results. This approach was used in 19 works (67.86%) to examine the validity of the decision outcomes. Some studies used only comparative analysis as in [57, 67, 72, 89] , whereas others combined this analysis with other approaches to increase the accuracy of the evaluation results as in [90, 94, 96] . This process was followed by sensitivity analysis, which was employed in 10 studies with a 37.71% occurrence rate. Meanwhile, experimental evaluation, objective validation (mean ± SD), subjective validation and Spearman's rank correlation test were the least applied methods with occurrence rates of 14.29%, 10.71%, 10.71%, and 7.14%, respectively. Thus, regardless of the applied approach, evaluation and/or validation is essential for checking the reliability of the proposed MADM method's results.

In this section, the country in which the study was performed was statistically analysed, as shown in Figure 6 . The journals that were targeted to publish this type of research and studies were presented. Moreover, the statistical information about the amount of resources used in previous studies in terms of their nature (i.e., whether the values were fuzzy numbers or crisp numbers) is presented in this section. Figure 6 shows the 16 countries in which the studies included in this review were conducted. The selected articles' geographical distribution in terms of numbers and percentages shows that the most prolific contributors were from India 59, 63, 65, 73, 75, 76, 79, 80, 82, 83, 88, 93 49 Philippines, 53 Brazil, 55 Vietnam, 95 Taiwan, 64 and the USA 84 with one study each.

Additionally, the total number of journals that included articles in different categories was 39. The distribution of journals in terms of the numbers and percentages of published articles in this review showed that the most prolific journals were Applied Soft Computing Journal 76, 92, 95, 96 and Journal of Cleaner Production, 73, 83, 85, 93 with four papers each. These journals were followed by the Sustainability (MDPI) 49, 87, 88 with three published articles and IEEE Access, 61, 74 International Journal of Intelligent Systems, 59, 78 Results in Physics, 69, 80 and Soft Computing 57,79 with two articles each. Meanwhile, one article was published in each of the following journals: Journal of Healthcare Engineering-Hindawi, 51 Socioeconomic Planning Sciences, 53 Science of the Total Environment, 55 Complexity, 56 Environmental Research and Public Health, 58 Computational and Mathematical Methods in Medicine, 60 Health Informatics Journal, 62 Journal of Infection and Public Health, 52 Mathematical Problems in Engineering, 63 International Journal of Healthcare Management, 50 International Journal of Information Technology and Decision Making, 37 Artificial Intelligence in Medicine, 66 Computer Methods and Programs in Biomedicine, 10 International Journal of Biomathematics, 67 International Journal of Infectious Diseases, 70 International Journal of Disaster Risk Reduction, 71 International Journal of Intelligent Engineering and Systems, 77 Transportation Research Interdisciplinary Perspectives, 44 Systematic Reviews in Pharmacy, 81 International Journal of Logistics Research and Applications, 82 Journal of Latex Class Files, 94 Applied Intelligence, 68 Entropy, 89 Environmental Science and Pollution Research, 86 International Journal of Hospitality Management, 91 Journal of Healthcare Leadership, 54 Knowledge-Based Systems, 72 Materials Today: Proceedings, 65 Mathematics, 90 Operational Research Society of India (OPSEARCH), 75 Resources, Conservation & Recycling, 84 and Symmetry. 64 In the application of MADM methods, the criteria can be crisply or fuzzily defined. In this review, 19 studies used a crisp environment, representing 37.25% of the total studies. Meanwhile, 32 works used a fuzzy environment, representing 62.75% of the total studies ( Figure 7) . The distribution in terms of numbers and percentages showed that the fuzziest method was triangular fuzzy numbers (TFN), [49] [50] [51] 60, 65, 71, 73, 75, 77, 80, 85, 86, 89 88 linguistic HFSs (LHFSs), 96 trapezoidal interval type-2 fuzzy (TrIT2F), 72 complex q-rung orthopair uncertain linguistic sets (CQROULSs), 64 and Probabilistic Linguistic Z-number with Unbalanced semantics (UPLZ) 74 were used by each study.

For future research, a further examination should focus on the role of MADM in different unused contexts to mitigate any issues that might arise when a similar incident recurs. As discussed in Section 3.2, the related studies were analysed and taxonomised in four main categories: medical, social, economic, and technological. Furthermore, several studies F I G U R E 7 Distribution by fuzzy sets used [Color figure can be viewed at wileyonlinelibrary.com] determined customers' intentions and trust in social commerce during COVID-19. [100] [101] [102] However, the previous studies did not consider and assess the multigroup modelling for customers and firms during COVID-19, which is considered a research gap. Such a research gap is related to economic or/and social categories of the proposed taxonomy and might be addressed in future research.

In this context, the evaluation and ranking of the customers of each firm would determine the most intention of customers to use social commerce. The social commerce firms would give high support, such as discounts, coupons, points of purchase and gifts, to such customers to reduce the costs of advertising and increase marketing promotion. 100, 103 From another perspective, the evaluation and ranking of the firms can provide valuable insights into the best firm on the basis of using social commerce. 101 Furthermore, the social commerce firms would improve social commerce construct. The firms listed at the last of the ranking can adhere to firms with the highest-ranking models. Firms with the highest ranking may develop their social commerce construct to respond to commerce challenges for such a pandemic. 104 However, the evaluation and ranking of firms and customers of each firm are considered a challenge because of the following four main issues: (1) the supporting criteria of trust in social commerce during COVID-19 for firms and customers within each firm should be identified and determined, (2) weight is assigned for each supported criterion of firms and customers, (3) the supported multievaluation criteria must be considered in the evaluation of firms and customers, 104 and (4) the maximisation and minimisation goals for social commerce criteria of firms and customers increase the variation complexity, resulting in the fourth issue of data variation. 105 Accordingly, four sequential methodology stages will be proposed as follows (Figure 8 ). First, based on literature review analysis, identifying the related criteria of firms and customers for trust in social commerce during COVID-19 must be achieved. Second, based on the identified criteria, a multigroup structural equation modelling approach must be adopted to assess and determine the supporting criteria of firms and customers for trust in social commerce F I G U R E 8 Potential future directions of social commerce based on MADM methods [Color figure can be viewed at wileyonlinelibrary.com] during COVID-19. Third, an artificial neural network technique must be developed to assign weight in supported criteria of firms and customers. Fourth, two decision matrices should be formulated. (I) firms' decision matrix will be presented on the basis of the intersection of the 'supported and weighted firms' criteria' and 'firms list'. (ii) Customers' decision matrices within each firm will be presented based on the intersection of the 'supported and weighted customers criteria' and 'customers list'. Then, based on the formulated decision matrices, MADM methods must be used for ranking the firms and customers thoroughly. 106, 107 Moreover, the multiexpert decision-making might be addressed to improve the consistency of the individual opinion of experts involved in the evaluation and ranking of firms and customers. 108 Such future work is vital and has two main significances. First, the social commerce firms will improve when promoting the social commerce of people during COVID-19 by recognising the most vital customers to provide them with effective services. Second, the social commerce firms will improve the social commerce construct emerging from the COVID-19 crisis worldwide.

Since the outbreak of the COVID-19 pandemic, the number of studies that reflect researchers' efforts is continually increasing. Studying the influencing criteria and finding the best substitute in the various sectors and aspects that are crucially affected by COVID-19 to make a proper decision are necessary. In this study, a comprehensive review of how MADM approaches assisted with different applications in the midst of COVID-19 were systematically conducted. Following the SLR protocol presented in Figure 2 , four reliable scientific database engines have been searched three different times to extract the relevant publications of COVID-19 based on MADM techniques. In total, out of the initial publication numbers (i.e., 879 studies), 51 studies were classified into four main categories including medical (n = 30), social (n = 4), economic (n = 13), and technological (n = 4). Those categories were associated with their specific subcategories, as shown in Figure 3 . Moreover, the medical studies were distributed into four subcategories, namely, strategies, policies, tools, and services. Regarding the social category, the studies focused on different social aspects including social distancing factors, emergency factors, risk factors, and risk level. The economic studies discussed interesting topics, such as supply chain, green economy, transportation, environment and business and corporations. Moreover, the technological studies discussed the topics of smart systems, telecom service, digital technology, and E-Learning.

Furthermore, a comprehensive analysis was conducted for each category to address the issues and challenges, the literature contributions, the employed data set, evaluation criteria types, MADM techniques types, validation and evaluation approach and different bibliography analysis. Several issues and challenges were analysed and discussed including multiattribute, inconsistency, time consumption, unnatural comparison, vagueness, normalisation, distance measurement, outranking, trade-off, conflict criteria, the importance of criteria, and data variation. In this regard, multiattribute and importance criteria issues were clearly discussed in all articles within all categories except for the economic category, which showed that the issue of the importance of criteria dominated in all studies except for one study only. The main analysis of the data set was discussed and presented in terms of the primary and secondary data sets, sample size and number of experts. This analysis showed that the majority of studies used the primary data set, combined primary and secondary data sets and secondary data sets. Going through the conducted analysis of the evaluation criteria types, two types of evaluation criteria (i.e., subjective and objective criteria) were discussed. Previous studies used either only one type or combined the two types depending on the identified criteria in their case. The analysis showed that most studies used the objective criteria type, subjective criteria type and both. Furthermore, the academic literature showed that three different directions in using MADM techniques exist, namely, ranking, weighting and hybrid methods. The main highlight of such analysis showed that the existing MADM methods were most frequently used followed by the extended existing MADM method and proposed new MADM methods. In the analysis of validation and evaluation approaches, six common approaches were found and discussed, namely, comparative analysis, objective validation, subjective validation, sensitivity analysis, experimental evaluation and Spearman's rank correlation test. In this regard, comparative study was most frequently used for testing the reliability and efficiency of the results followed by sensitivity analysis, experimental evaluation, objective validation, subjective validation and Spearman's rank correlation test. In addition, the final set of articles were distributed based on different bibliography analyses including the country of study, journals and fuzzy set used. This analysis has shown that the most prolific contributors were from India, the most prolific journal was Applied Soft Computing Journal and the fuzziest method was TFN. As a future recommendation, the authors of this study intend to give insight into the second scope of MCDM methods (i.e., MODM), which was also used to combat the COVID-19 pandemic. 

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and corona virus disease-2019 (COVID-19): the epidemic and the challenges

The unprecedented stock market reaction to COVID-19

The 2019 novel coronavirus outbreak: current crises, controversies and global strategies to prevent a pandemic

Using technology to maintain the education of residents during the COVID-19 pandemic

Determinants of the community mobility during the covid-19 epidemic: the role of government regulations and information

Impact of COVID-19 pandemic on health system & sustainable development goal 3

The discounted value of human lives lost due to COVID-19 in France

Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: a systematic review

Optimizing pandemic preparedness and response through health information systems: lessons learned from Ebola to COVID-19

Helping doctors hasten COVID-19 treatment: towards a rescue framework for the transfusion of best convalescent plasma to the most critical patients based on biological requirements via ml and novel MCDM methods

Combating COVID-19 pandemic: the best management practices of a designated hospital in Southern Philippines

Investigating the emerging COVID-19 research trends in the field of business and management: a bibliometric analysis approach

Artificial intelligence in COVID-19 drug repurposing

Artificial intelligence against COVID-19: A meta-analysis of current research

New machine learning method for imagebased diagnosis of COVID-19

Weakly supervised deep learning for covid-19 infection detection and classification from ct images

The role of social media to generate social proof as engaged society for stockpiling behaviour of customers during Covid-19 pandemic

Tourism recovery strategy against COVID-19 pandemic

Retracted COVID-19 articles: a side-effect of the hot race to publication

Emotion-aware and intelligent internet of medical things towards emotion recognition during COVID-19 Pandemic

The UK government's COVID-19 policy: assessing evidence-informed policy analysis in real time

Effects of COVID-19 on the export operations of smaller manufacturing enterprises

Impact of COVID-19 pandemic on information management research and practice: transforming education, work and life

A foundation for the study of group decision support systems

Multi-criteria decision making in Pythagorean fuzzy environment

New Methods And Applications In Multiple Attribute Decision Making (MADM)

An augmented Tabu search algorithm for the green inventory-routing problem with time windows

Fuzzy mathematical programming and self-adaptive artificial fish swarm algorithm for just-in-time energy-aware flow shop scheduling problem with outsourcing option

Multi-objective optimization for healthcare waste management network design with sustainability perspective

Multi-objective optimization for the reliable pollution-routing problem with cross-dock selection using Pareto-based algorithms

An integrated decisionmaking approach for green supplier selection in an agri-food supply chain: threshold of robustness worthiness

A novel approach for group decision making based on the best-worst method (G-BWM): application to supply chain management

Sustainable fuzzy multi-trip location-routing problem for medical waste management during the COVID-19 outbreak

Using a process approach to pandemic planning: a case study

Forecasting of medical equipment demand and outbreak spreading based on deep long short-term memory network: the COVID-19 pandemic in Turkey. Signal, Image Video Process

A sustainable medical waste collection and transportation model for pandemics

Multi-biological laboratory examination framework for the prioritization of patients with COVID-19 based on integrated AHP and group VIKOR methods

Determining importance of many-objective optimisation competitive algorithms evaluation criteria based on a novel fuzzy-weighted zero-inconsistency method

A material selection model based on the concept of multiple attribute decision making

An extension of ELECTRE to multi-criteria decisionmaking problems with multi-hesitant fuzzy sets

Fuzzy decision by opinion score method

Interval type 2 trapezoidal-fuzzy weighted with zero inconsistency combined with VIKOR for evaluating smart e-tourism applications

Multidimensional benchmarking of the active queue management methods of network congestion control based on extension of fuzzy decision by opinion score method

Evaluate the impact of COVID-19 prevention policies on supply chain aspects under uncertainty

Novel technique for reorganisation of opinion order to interval levels for solving several instances representing prioritisation in patients with multiple chronic diseases

A uniform intelligent prioritisation for solving diverse and big data generated from multiple chronic diseases patients based on hybrid decision-making and voting method

Dempster-Shafer theory for classification and hybridised models of multi-criteria decision analysis for prioritisation: a telemedicine framework for patients with heart diseases

Medical emergency triage and patient prioritisation in a telemedicine environment: a systematic review

Development of a multicriteria model for sustainable reorganization of a healthcare system in an emergency situation caused by the COVID-19 pandemic

Ranking of hospitals in the case of COVID-19 outbreak: a new integrated approach using patient satisfaction criteria

Evaluation of the COVID-19 pandemic intervention strategies with hesitant F-AHP

Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects

Modeling the lockdown relaxation protocols of the Philippine government in response to the COVID-19 pandemic: an intuitionistic fuzzy DEMATEL analysis

Prioritizing infection prevention and control activities for SARS-CoV-2 (COVID-19): a multi-criteria decision-analysis method

Risk of the Brazilian health care system over 5572 municipalities to exceed health care capacity due to the 2019 novel coronavirus (COVID-19)

Hybrid TODIM method with crisp number and probability linguistic term set for urban epidemic situation evaluation

A new emergency response of spherical intelligent fuzzy decision process to diagnose of COVID19

Decision support algorithm for selecting an antivirus mask over COVID-19 pandemic under spherical normal fuzzy environment

A novel distance measure for intuitionistic fuzzy sets with diverse applications

Capacity evaluation of diagnostic tests for COVID-19 using multicriteria decision-making techniques

Benchmarking methodology for selection of optimal COVID-19 diagnostic model based on entropy and TOPSIS methods

A model for the effective COVID-19 identification in uncertainty environment using primary symptoms and CT scans

Decision-making analysis based on Fermatean fuzzy Yager aggregation operators with application in COVID-19 testing facility

Complex q-rung orthopair uncertain linguistic partitioned Bonferroni mean operators with application in antivirus mask selection

Fuzzy cloud based COVID-19 diagnosis assistant for identifying affected cases globally using MCDM

Detection-based prioritisation: framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated entropy-TOPSIS methods

Pythagorean m-polar fuzzy topology with TOPSIS approach in exploring most effectual method for curing from COVID-19

Convalescent-plasma-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on AHP-group TOPSIS and matching component

COVID-19 vaccine: a neutrosophic MCDM approach for determining the priority groups

Multi-criteria decision analysis to prioritize hospital admission of patients affected by COVID-19 in low-resource settings with hospital-bed shortage

Evaluation of hospital disaster preparedness by a multi-criteria decision making approach: The case of Turkish hospitals

An integrated interval type-2 fuzzy technique for democratic-autocratic multi-criteria decision making. Knowledge-Based Systems

Selection of the best healthcare waste disposal techniques during and post COVID-19 pandemic era

A new computational method based on probabilistic linguistic Z-number with unbalanced semantics and its application to multi-criteria group decision making

A lean approach to healthcare management using multi criteria decision making

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)

A framework of computational model for predicting the spread of COVID-19 pandemic in Saudi Arabia

Emergency decision support modeling for COVID-19 based on spherical fuzzy information

An integrated multi-criteria decision-making approach for identifying the risk level of musculoskeletal disorders among handheld device users

Identification of dominant risk factor involved in spread of COVID-19 using hesitant fuzzy MCDM methodology

Identifying and Prioritizing the factors affecting the agility of the supply chain of pharmaceutical company using multi-criteria decision-making methods in COVID-19 Pandemic

Agriculture supply chain risks and COVID-19: mitigation strategies and implications for the practitioners

Now is the time to press the reset button: Helping India's companies to become more resilient and effective in overcoming the impacts of COVID-19, climate changes and other crises

Disruption risks to material supply chains in the electronics sector

Energy trilemma based prioritization of waste-to-energy technologies: implications for post-COVID-19 green economic recovery in Pakistan

Fuzzy-based multi-criteria decision analysis of environmental regulation and green economic efficiency in a post-COVID-19 scenario: the case of China

Best-worst method for modelling mobility choice after COVID-19: evidence from Italy

Impact of COVID-19 induced lockdown on environmental quality in four indian megacities using landsat 8 OLI and TIRS-derived data and mamdani fuzzy logic modelling approach

Performance Evaluation of Construction Companies Using Integrated. Entropy-Fuzzy VIKOR ModelEntropy

Risk evaluation of electric power grid investment in China employing a hybrid novel MCDM method

The effect of COVID-19 pandemic on domestic tourism: A DEMATEL method analysis on quarantine decisions

MARCOS technique under intuitionistic fuzzy environment for determining the COVID-19 pandemic performance of insurance companies in terms of healthcare services

The interplay of circular economy with industry 4.0 enabled smart city drivers of healthcare waste disposal

Engineering. COVID-19 networking demand: an auction-based mechanism for automated selection of edge computing services

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

An integrated multi-criteria decision making approach with linguistic hesitant fuzzy sets for E-learning website evaluation and selection

Encyclopedia of social measurement: data collection, primary versus secondary

Research data sharing: developing a stakeholder-driven model for journal policies

Weighting Methods and their Effects on Multi-criteria Decision Making Model Outcomes In Water Resources Management

Predicting the antecedents of trust in social commerce-a hybrid structural equation modeling with neural network approach

The age of mobile social commerce: an artificial neural Network analysis on its resistances

Neural network modeling of consumer satisfaction in mobile commerce: an empirical analysis

Mobile technology acceptance model: an investigation using mobile users to explore smartphone credit card

Predicting trust in online advertising with an SEM-artificial neural network approach

Risk, trust, and the interaction of perceived ease of use and behavioral control in predicting consumers' use of social media for transactions

Score function based on concentration degree for probabilistic linguistic term sets: an application to TOPSIS and VIKOR

Probabilistic double hierarchy linguistic term set and its use in designing an improved VIKOR method: the application in smart healthcare

The risk assessment of construction project investment based on prospect theory with linguistic preference orderings

Rise of multiattribute decision-making in combating COVID-19: A systematic review of the state-of-the-art literature

The authors are grateful to the Universiti Pendidikan Sultan Idris, Malaysia for funding this study under UPSI SIG Grant No. 2020-0150-109-01.

The authors declare that there are no conflict of interests.