Fuzzy APPSS: A novel method for quantifying COVID-19 impact in India under triangular spherical fuzzy environment.

Fuzzy APPSS: A novel method for quantifying COVID-19 impact in India under triangular spherical fuzzy environment.

Publication date: Dec 28, 2024

In the current scenario, decision-making models are essential for analyzing real-world problems. To address the dynamic nature of these problems, fuzzy decision-making models have been proposed by various researchers. However, an advanced technique is needed to assess uncertainty in real-time complex situations. Therefore, an association between preference and performance with satisfactory score (APPSS) method is introduced as a fuzzy decision-making method that incorporates two components: preference and performance. This method focuses on demonstrating a connection between preference and performance with a satisfactory measure. Preference analysis evaluates the significance of criteria, while performance analysis assesses the effectiveness of each alternative based on these criteria. Additionally, the satisfactory measure ensures the reliability of the outcomes. The applicability of the proposed method is demonstrated by analyzing the impact of COVID-19 on different age groups in India across various categories. The proposed method employs triangular spherical fuzzy numbers (TSFN), which is a mathematical model that extends beyond conventional fuzzy numbers by incorporating both triangular and spherical characteristics. Furthermore, a new scoring function for TSFN is developed using the graded mean integration method. The analysis reveals that the age group between 60-69 is highly vulnerable to COVID-19. The robustness of these outcomes is verified through sensitivity and comparative analyses. The findings also assist policymakers in more effectively assessing potential future health complications.

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Concepts Keywords
Covid Adult
India Aged
Mathematical COVID-19
Policymakers Decision Making
Reliability Female
Fuzzy APPSS method
Fuzzy Logic
Humans
India
Middle Aged
Models, Theoretical
Preference and performance
SARS-CoV-2
Scoring function

Semantics

Type Source Name
disease MESH COVID-19
disease MESH uncertainty
disease MESH complications
drug DRUGBANK Aspartame
disease IDO process
disease MESH emergencies
drug DRUGBANK Coenzyme M
disease MESH cardiovascular disease
disease MESH anxiety
disease MESH lifestyles
disease IDO facility
drug DRUGBANK Pentaerythritol tetranitrate
disease MESH stroke
disease MESH Sepsis
disease IDO quality
disease MESH African swine fever
drug DRUGBANK Tropicamide
drug DRUGBANK Steviolbioside
drug DRUGBANK Lauric Acid
drug DRUGBANK Peracetic acid
disease IDO country
disease IDO susceptibility
disease MESH aids
disease MESH infection
disease MESH syndromes
disease MESH burnout
disease MESH comorbidity
disease MESH Infectious Diseases
pathway REACTOME Reproduction

Original Article

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