COMMENTARYodern increases in computers' capacity to store and process data have made pos sible the emergence and advancement of countless tech nologies that directly impact the daily lives of the world's population. Although discussion of some of the most powerful new technologies began in the last century, only recent ly has their development generated large paradigm shifts. Important examples include artificial intelli gence, data mining, and machine learning, among others. These tech nologies have then become re sponsible for the socalled Fourth Industrial Revolution (or Industry 4.0), where there is convergence be tween the digital, physical, and biological environment.The concept of Health 4.0 ad dresses the broad possibility of applying the technologies of Indus try 4.0 to improve healthcare (as shown in Table 1; cf. Democracy 4.0 [1]). In this commentary we address the challenges and perspectives of Health 4.0 and how the risk of lack of control can delay its emergence (see also [2]).In recent years, research on dis ruptive technologies in Industry 4.0 has increased exponentially in several fields, both in industrial M

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(1) Background: Any disturbance in the pharmaceutical supply chain (PSC) can disrupt the supply of medicines and affect the efficiency of health systems. Due to shortages in the global pharma supply chain over the past few years and the complex nature of free trade and its limitations when confronted by a major global health and humanitarian crisis, many countries have taken steps to mitigate the risks of disruption, including, for example, recommending the adoption of a plus one diversification approach, increasing safety stock, and nationalizing the medical supply chains. (2) Objective: To scope findings in the academic literature related to decision criteria to guide national policy decisions for the “Partial Nationalization of Pharmaceutical Supply Chain” (PNPSC) from the viewpoints of the three main stakeholders: industry, payers (government and health insurance), and patients. (3) Methods: These consist of a scoping review of the peer-reviewed literature. (4) Results: A total of 115 studies were included. For local manufacturing decisions, five criteria and 15 sub-criteria were identified. Weighting, decision-making, risk assessment, and forecasting were the main data analysis tools applied; (5) Conclusions: The findings could serve as a baseline for constructing PNPSC frameworks after careful adaptation to the local context.

Global vaccine revenues are projected at $59.2 billion, yet large-scale vaccine distribution remains challenging for many diseases in countries around the world. Poor management of the vaccine supply chain can lead to a disease outbreak, or at worst, a pandemic. Fortunately, a large number of those challenges, such as decision-making for optimal allocation of resources, vaccination strategy, inventory management, among others, can be improved through optimization approaches. This work aims to understand how optimization has been applied to vaccine supply chain and logistics. To achieve this, we conducted a rapid review and searched for peer-reviewed journal articles, published between 2009 and March 2020, in four scientific databases. The search resulted in 345 articles, of which 25 unique studies met our inclusion criteria. Our analysis focused on the identification of article characteristics such as research objectives, vaccine supply chain stage addressed, the optimization method used, whether outbreak scenarios were considered, among others. Approximately 64% of the studies dealt with vaccination strategy, and the remainder dealt with logistics and inventory management. Only one addressed market competition (4%). There were 14 different types of optimization methods used, but control theory, linear programming, mathematical model and mixed integer programming were the most common (12% each). Uncertainties were considered in the models of 44% of the studies. One resulting observation was the lack of studies using optimization for vaccine inventory management and logistics. The results provide an understanding of how optimization models have been used to address challenges in large-scale vaccine supply chains.

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