Decision-making framework for identifying regions vulnerable to transmission of COVID-19 pandemic. (July 2022)

• Record Type: Journal Article
• Authors: Gupta, Rohit; Rathore, Bhawana; Srivastava, Abhishek; Biswas, Baidyanath
• Is Part Of: Computers & industrial engineering. Volume 169(2022)
• Publication Date: 2022-07
• Subjects: COVID-19 -- Epidemic transmission -- Fuzzy decision framework -- Fuzzy delphi -- Fuzzy A.H.P. -- Fuzzy TOPSIS; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2022.108207
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Proposed a hybrid fuzzy decision-making framework for the analysis of COVID-19. Identified the twenty-five relevant factors and classified them into clusters. Ranked the clusters and factors based on their criticality to COVID-19 transmission. Cities are ranked based on their vulnerability to COVID-19 transmission. Study contributes to the discipline of decision analytics and healthcare risk management. Abstract: At the beginning of 2020, the World Health Organization (WHO) identified an unusual coronavirus and declared the associated COVID-19 disease as a global pandemic. We proposed a novel hybrid fuzzy decision-making framework to identify and analyze these transmission factors and conduct proactive decision-making in this context. We identified thirty factors from the extant literature and classified them into six major clusters ( climate, hygiene and safety, responsiveness to decision-making, social and demographic, economic, and psychological) with the help of domain experts. We chose the most relevant twenty-five factors using the Fuzzy Delphi Method (FDM) screening from the initial thirty. We computed the weights of those clusters and their constituting factors and ranked them based on their criticality, applying the Fuzzy Analytic Hierarchy Process (FAHP). We found that the top five factors were global travel, delay in travel restriction, close contact, social cohesiveness, and asymptomatic. To evaluate our framework, we chose ten different geographically located cities and analyzed their exposure to COVID-19 pandemic by ranking them based on their vulnerability of transmission using Fuzzy Technique for Order of Preference by Similarity To Ideal Solution (FTOPSIS). Our study contributes to the disciplines of decision analytics and healthcare risk management during a pandemic through these novel findings. Policymakers and healthcare officials will benefit from our study by formulating and improving existing preventive measures to mitigate future global pandemics. Finally, we performed a sequence of sensitivity analyses to check for the robustness and generalizability of our proposed hybrid decision-making framework.