A probabilistic hesitant fuzzy MCDM approach to evaluate India's intervention strategies against the COVID-19 pandemic

The unprecedented coronavirus pandemic (COVID-19) has wreaked havoc across the globe. The Covid-19 pandemic has affected all countries, including government intervention programs, thus becoming a significant threat. This study aims to develop a hybrid fuzzy multi-criteria decision-making (F-MCDM) model in a constrained ecosystem in response to specific government strategies and the effectiveness of interventions used in different countries during the COVID-19 pandemic. An empirical case study is conducted in India with five prospective criteria: "high acceptance among the populace", "effectiveness in halting the COVID-19 epidemic", "compatibility by any other standard", "estimated total cost", and "simple to implement". Regarding the ranking of strategies, "vaccinations", "social isolation", and "development of an emergence"are the top three strategies. As a result, SARS-CoV-2 vaccines have reduced COVID-19-related hospitalizations in the elderly, which has reduced post-CoVID morbidity and mortality. Many countries have different recommendations for selecting possible government initiatives and implementing those decisions. India's strategies for developing public health policies, preventing misinformation, and managing behavior and response were ranked as the top three priorities among the listed strategies. Sensitivity analysis confirmed the validity of these results. In this work, the implications of these findings are discussed in terms of a developing nation.