Modelling the interdependent relationships among epidemic antecedents using fuzzy multiple attribute decision making (F-MADM) approaches

Abellana, Dharyll Prince M.

doi:10.1515/comp-2020-0213

Cited by 4 publications

(9 citation statements)

References 111 publications

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“…As such, it could be useful when determining the priority of a large number of alternatives. In fact, Abellana (2021) successfully used BWM to reduce a large number of antecedents of the dengue epidemic. Kheybari et al (2019) also used the BWM to determine the overall score of bioethanol facility locations given a large number of criteria in a location selection problem.…”

Section: Best-worst Methodsmentioning

confidence: 99%

Benchmarking of COVID-19 testing facilities: a case in the Philippines

Velos

Dayupay

et al. 2022

JM2

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Purpose With the aggressive movement towards testing for COVID-19 across the globe, this study aims to shed light on how testing facilities perform in an operational perspective. Design/methodology/approach With 102 testing facilities in the Philippines, the relative efficiencies of each facility are quantified using a data envelopment analysis technique. Afterwards, a best-worst method was conducted to assign priority weights to each testing facility. Findings Results show that the proposed approach effectively prioritizes testing facilities that most likely have high utilization. Research limitations/implications The findings in this study would be significant to the literature in a number of respects. For one, it reveals results that would stimulate the interest among scholars in a wide variety of disciplines such as management, data mining, policymaking, decision science and epidemiology, among others. Originality/value This study differs from previous works in a number of respects, particularly, in that to the best of the authors’ knowledge, this is the first study to examine the relative efficiencies of COVID-19 testing facilities.

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Section: Best-worst Methodsmentioning

confidence: 99%

Benchmarking of COVID-19 testing facilities: a case in the Philippines

Velos

Dayupay

et al. 2022

JM2

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“…Step 1: Establish the decision matrices X (1) , X (2) , X (3) of legal expert ε 1 , investment expert ε 2 , and population expert ε 3 , respectively, as Tables 1-3: Step 2: Determine the average decision matrix X = (x ij ) l * n of legal expert ε 1 , investment expert ε 2 , and population expert ε 3 using Equation (2) as Table 4: Step 3: Determine the similarity matrix. Using Equation (4), the distance d…”

Section: Determination Of Expert Weightsmentioning

confidence: 99%

“…, and then the similarity matrix S (k) = (s (k) ) l * n of each expert and ideal solution X are obtained by the similarity given by Equation (5). The similarity matrices S (1) , S (2) , and S (3) of ε 1 , ε 2 , and ε 3 , respectively, are as Tables 5-7: Step 4: Determine the similarity correction matrix. Using Equation ( 7) to correct the similarity, the similarity correction matrices S * (1) , S * (2) , and S * (3) of ε 1 , ε 2 , and ε 3 .…”

Section: Determination Of Expert Weightsmentioning

confidence: 99%

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A Weighting Method Based on the Improved Hesitation of Pythagorean Fuzzy Sets

Zhou

et al. 2023

Electronics

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The existing expert weight determination method for multi-attribute decision making based on the Pythagorean fuzzy number approach does not make sufficient use of the hesitation involved with the decision information, which may cause biased weight assignment. Therefore, to address the issue of unknown expert weights and attribute evaluation based on Pythagorean fuzzy numbers in multi-attribute group decision-making problems, a weight determination method is proposed that improves the treatment of hesitation in Pythagorean fuzzy sets. Firstly, the proximity of experts and similarity of the modified ones are determined according to the evaluation matrix. Then, the expert weights are integrated from the aspects of proximity and corrected similarity to obtain an assembled comprehensive evaluation matrix. Finally, the alternatives are ranked using the PF-TOPSIS method. The results of expert weight analysis and data verification demonstrate that the proposed method fully utilizes expert decision-making information, leading to a significant improvement in the rationality and accuracy of multi-attribute group decision-making problems.

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“…In epidemiological diagnostics, this is an extremely urgent task, since the data collected by institutions and government centers does not depend on their importance for modeling. Therefore, dimensionality reduction methods help to discard unnecessary data on morbidity [69], reduce computational complexity [70], and identify informative signs [71] and factors influencing the epidemic process [72].…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Simulation of Epidemic Processes: A Review of Modern Methods, Models and Approaches

Chumachenko

2022

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The paper is devoted to an overview of the current state of research on the modeling of epidemic processes. The classification of mathematical and simulation models of epidemic processes is carried out. The disadvantages of classical models are revealed. Specific characteristics inherent in epidemic processes have been determined, which must be taken into account when constructing mathematical and simulation models. A review of deterministic compartment models is carried out. Various methods and approaches to the construction of statistical models of epidemic processes are considered. The types of problems that are solved using machine learning are analyzed.

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Modelling the interdependent relationships among epidemic antecedents using fuzzy multiple attribute decision making (F-MADM) approaches

Cited by 4 publications

References 111 publications

Benchmarking of COVID-19 testing facilities: a case in the Philippines

Benchmarking of COVID-19 testing facilities: a case in the Philippines

A Weighting Method Based on the Improved Hesitation of Pythagorean Fuzzy Sets

Simulation of Epidemic Processes: A Review of Modern Methods, Models and Approaches

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