Transforming the vaccine supply chain in Australia: Opportunities and challenges

Designing an optimization model for the vaccine supply chain during the COVID-19 pandemic

The COVID-19 pandemic has affected people's lives worldwide. Among various strategies being applied to addressing such a global crisis, public vaccination has been arguably the most appropriate approach to control a pandemic. However, vaccine supply chain and management have become a new challenge for governments. In this study, a solution for the vaccine supply chain is presented to address the hurdles in the public vaccination program according to the concerns of the government and the organizations involved. For this purpose, a robust bi-level optimization model is proposed. At the upper level, the risk of mortality due to the untimely supply of the vaccine and the risk of inequality in the distribution of the vaccine is considered. All costs related to the vaccine supply chain are considered at the lower level, including the vaccine supply, allocation of candidate centers for vaccine injection, cost of maintenance and injection, transportation cost, and penalty cost due to the vaccine shortage. In addition, the uncertainty of demand for vaccines is considered with multiple scenarios of different demand levels. Numerical experiments are conducted based on the vaccine supply chain in Kermanshah, Iran, and the results show that the proposed model significantly reduces the risk of mortality and inequality in the distribution of vaccines as well as the total cost, which leads to managerial insights for better coordination of the vaccination network during the COVID-19 pandemic.

Opportunities and challenges for commercializing microarray patches for vaccination from a MAP developer's perspective

Continued advances in microarray patch (MAP) technology are starting to make needle-free delivery of a broad range of vaccines an achievable goal. The drivers and potential benefits of a MAP platform for pandemic response and routine vaccination are clear and include dose-sparing, cold-chain elimination, increased safety, and potential self-administration. MAP technology is regarded as a priority innovation to overcome vaccination barriers, ensure equitable access, and improve the effectiveness of vaccines. Vaxxas, a global leader in this technology, has built a strong evidence-base for the commercial application of their high-density (HD) MAP platform, and is rapidly advancing scale-up of the manufacturing process for HD-MAPs. A greater awareness and understanding of the implications of the technology amongst supply-chain participants, regulatory authorities, and global healthcare organizations and foundations is needed to accelerate adoption and, particularly, to prepare for MAP use in pandemics. Key challenges remain in the commercialization of MAP technology and its adoption, including market acceptance, scale-up of production, regulatory approval, and the availability of capital to build advanced manufacturing infrastructure ahead of late-stage clinical trials.

A multi-echelon dynamic cold chain for managing vaccine distribution

While cold chain management has been part of healthcare systems, enabling the efficient administration of vaccines in both urban and rural areas, the COVID-19 virus has created entirely new challenges for vaccine distributions. With virtually every individual worldwide being impacted, strategies are needed to devise best vaccine distribution scenarios, ensuring proper storage, transportation and cost considerations. Current models do not consider the magnitude of distribution efforts needed in our current pandemic, in particular the objective that entire populations need to be vaccinated. We expand on existing models and devise an approach that considers the needed extensive distribution capabilities and special storage requirements of vaccines, while at the same time being cognizant of costs. As such, we provide decision support on how to distribute the vaccine to an entire population based on priority. We do so by conducting predictive analysis for three different scenarios and dividing the distribution chain into three phases. As the available vaccine doses are limited in quantity at first, we apply decision tree analysis to find the best vaccination scenario, followed by a synthetic control analysis to predict the impact of the vaccination programme to forecast future vaccine production. We then formulate a mixed-integer linear programming (MILP) model for locating and allocating cold storage facilities for bulk vaccine production, followed by the proposition of a heuristic algorithm to solve the associated objective functions. The application of the proposed model is evaluated by implementing it in a real-world case study. The optimized numerical results provide valuable decision support for healthcare authorities.