Fuzzy AHP Approach for Multi-Criteria Stress Analysis During COVID-19: A Case Study

Abstract

The novel coronavirus 2019 (COVID-19) pandemic poses a challenging and stressful situation among people from 2019 to date globally. This study analyzes various factors responsible for the stress intensity among India's urban cities to identify the most affected metropolitan city. The study identified six Indian urban cities and three major stress factors: routine, social, and job, with each factor having sub-criteria. A fuzzy analytic hierarchy process is used to model the situation, and a synthetic extent analysis method (EAM) is employed to solve the model. The modified EAM has been also applied and the two results compared. It has been found that the modified method is more robust than EAM. The concept of the minimum degree of possibility is applied to obtain the weight vector for the main criteria. After identifying the main stress criteria and its sub-criteria and six metropolitan cities, the hierarchy of identifying India's most affected metropolitan cities during COVID-19 is structured. The study found that using the EAM, Mumbai is the worst affected city, followed by Bengaluru, Chennai, Delhi-NCR, and Hyderabad. While using the modified method, the most affected areas are found to be Pune, followed by Mumbai, Chennai, Hyderabad, Delhi-NRC, and Bengaluru. It further discovered that routine stress-related is the most critical factor in identifying the most affected metropolitan city, followed by social and job-related stresses.