Investigating sustainable development for the COVID-19 vaccine supply chain: a structural equation modelling approach

Examining the Impact of Key Factors on COVID-19 Vaccination Coverage in India: A PLS-SEM Approach

During the coronavirus disease 2019 (COVID-19) pandemic, numerous factors determined the performance of COVID-19 vaccination coverage. The purpose of this study is to examine the influence of factors such as government stewardship, planning and implementation, and community participation on COVID-19 vaccination coverage. This study applied partial least square structured equation modeling (PLS-SEM) by analyzing 187 responses from the stakeholders involved in vaccination programs in four select states of India. This study empirically validates a framework for improving vaccination coverage by confirming the significant impact of planning and implementation on vaccination coverage followed by government stewardship and community participation. Additionally, this study highlights the individual impact of each factor on vaccination coverage. Based on the findings, strategic recommendations were proposed that can be utilized for formulating policy-level actions to facilitate the vaccination program.

Modeling a sustainable vaccine supply chain for a healthcare system

This study develops a vaccine supply chain (VSC) to ensure sustainable distribution during a global crisis in a developing economy. In this study, a multi-objective mixed-integer programming (MIP) model is formulated to develop the VSC, ensuring the entire network's economic performance. This is achieved by minimizing the overall cost of vaccine distribution and ensuring environmental and social sustainability by minimizing greenhouse gas (GHG) emissions and maximizing job opportunities in the entire network. The shelf-life of vaccines and the uncertainty associated with demand and supply chain (SC) parameters are also considered in this study to ensure the robustness of the model. To solve the model, two recently developed metaheuristics-namely, the multi-objective social engineering optimizer (MOSEO) and multi-objective feasibility enhanced particle swarm optimization (MOFEPSO) methods-are used, and their results are compared. Further, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model has been integrated into the optimization model to determine the best solution from a set of non-dominated solutions (NDSs) that prioritize environmental sustainability. The results are analyzed in the context of the Bangladeshi coronavirus disease (COVID-19) vaccine distribution systems. Numerical illustrations reveal that the MOSEO-TOPSIS model performs substantially better in designing the network than the MOFEPSO-TOPSIS model. Furthermore, the solution from MOSEO results in achieving better environmental sustainability than MOFEPSO with the same resources. Results also reflect that the proposed MOSEO-TOPSIS can help policymakers establish a VSC during a global crisis with enhanced economic, environmental, and social sustainability within the healthcare system.