A hybrid approach based on ELECTRE III-genetic algorithm and TOPSIS method for selection of optimal COVID-19 vaccines

Abstract

COVID-19 pandemic poses unprecedented challenges to the world health system, prompting academics and health professionals to develop appropriate solutions. Researchers reported different COVID-19 vaccines introduced by institutions and companies around the globe, which are at different stages of development. However, research developing an integrated framework for selecting and ranking the optimal potential vaccine against COVID-19 is minimal. This paper aimed to fill this gap by using a hybrid methodology based on ELimination Et Choice Translating REality III (ELECTRE III)–Genetic Algorithm (GA) and Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) approach to select the optimal SARS-CoV-2 vaccine. ELECTRE III method yields a fathomable analysis of the concordance index, while GA is known for its ability to disaggregate decision-making preferences from holistic decisions. TOPSIS is preferred for picking an ideal and an anti-ideal solution. Thus, combining ELECTRE III-GA and TOPSIS is considered the best model to assess vaccines against the pandemic. The results confirm that the best vaccines rely on a high level of safety, efficacy, and availability. Our developed evaluation framework can help healthcare professionals and researchers gain research information and make critical decisions regarding potential vaccines against the disease.