A Sociotheological Approach to Catholic Social Teaching: The Role of Religion in Moral Responsibility During COVID-19

This book introduces Catholic social teaching (CST) and its teaching on the common good to the reader and applies them in the realm of public health to critically analyze the major global issues of COVID-19 that undermine public interest. It uses the sociotheological approach that combines the moral principles of CST and the holistic analysis of modern sociology and also utilizes the secondary literature as the main source of textual data. Specifically, it investigates the corporate moral irresponsibility and some unethical business practices of Big Pharma in the sale and distribution of its anti-COVID vaccines and medicines, the injustice in the inequitable global vaccine distribution, the weakening of the United States Congress's legislative regulation against the pharmaceutical industry's overpricing and profiteering, the inadequacy of the World Health Organization's (WHO) law enforcement system against corruption, and the lack of social monitoring in the current public health surveillance system to safeguard the public good from corporate fraud and white-collar crime. This book highlights the contribution of sociology in providing the empirical foundation of CST's moral analysis and in crafting appropriate Catholic social action during the pandemic. It is hoped that through this book, secular scholars, social scientists, religious leaders, moral theologians, religious educators, and Catholic lay leaders would be more appreciative of the sociotheological approach to understanding religion and COVID-19. "This book brings into dialogue two bodies of literature: documents of Catholic social teaching, and modern sociology and its core thinkers and texts…The author does especially well to describe how taking ‘the sociotheological turn'…will benefit the credibility and dissemination of Catholic social thought.” - Rev. Fr. Thomas Massaro, S.J., Professor of Moral Theology, Jesuit School of Theology, Santa Clara University, Berkeley, California. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.

Optimizing the COVID-19 cold chain vaccine distribution network with medical waste management: A robust optimization approach.

This paper investigates the distribution problem of the COVID-19 vaccine at the provincial level in Turkey and the management of medical waste, considering the cold chain requirements and the perishable nature of vaccines. In this context, a novel multi-period multi-objective mixed-integer linear programming model is initially presented over a 12-month planning horizon for solving the deterministic distribution problem. The model includes newly structured constraints due to the feature of COVID-19 vaccines, which must be administered in two doses at specified intervals. Then, the presented model is tested for the province of Izmir with deterministic data, and the results show that the demand can be satisfied and community immunity can be achieved in the specified planning horizon. Moreover, for the first time, a robust model is created using polyhedral uncertainty sets to manage uncertainties related to supply and demand quantities, storage capacity, and deterioration rate, and it has been analyzed under different uncertainty levels. Accordingly, as the level of uncertainty increases, the percentage of meeting the demand gradually decreases. It is observed that the biggest effect here is the uncertainty in supply, and in the worst case, approximately 30% of the demand cannot be met.

An optimization framework for COVID-19 vaccine allocation and inventory management: A case study.

As the novel coronavirus pandemic wreaked havoc globally, governments have implemented massive vaccination programs to tackle it. However, since the pandemic's emergence moves beyond the second year, some issues have stymied vaccination programs, including vaccine hesitancy, vaccine distribution inequality, new strains of the virus, and a possibility that the virus enters a stage of a requirement for cyclical vaccination. These challenges highlight the need for an appropriate mass COVID-19 vaccination program. Therefore, we attempt to address this problem by developing a bi-objective integrated vaccine allocation and inventory management framework. The goal is to minimize the system costs while maximizing the vaccination service level. Several important factors, such as multiple types of vaccines, the vaccines' perishability concept, demand uncertainty, and motivational strategy, have been addressed using dynamic planning. Besides that, the model development mechanism is carried out to be compatible and applicable to the current general vaccination program policies, forcing few strategic changes. Then, a case study concerning the vaccination program of the city of Mashhad in Iran is applied to the model. The results demonstrated significant advantages in total cost, vaccine shortage, and wastage compared to the current policy. Finally, the Lagrangian relaxation method is implemented on the model to strengthen further its capacity to handle larger-scale problems.

Life Science Business Value of Blockchain Case Use Blockchain Real World Deployment Taking Bad Actors and Counterfeit Drugs off the Market: Blockchain Real World Deployment Taking Bad Actors and Counterfeit Drugs off the Market

How is blockchain technology empowering patients to verify provenance and quality of COVID-19 vaccines and other lifesaving drugs through Asia's first blockchain solution for supply chain connectivity and traceability for pharmaceuticals and vaccines? With two million products on the blockchain network, this ground breaking scalable initiative controls "pack" vs "batch." Authorities are now seizing counterfeits and consumers are benefitting by verifying their drug(s) are effective and can be trusted as safe and efficacious. Why use blockchain and what are the vaccine management issues along the pharma supply chain?

A humanitarian cold supply chain distribution model with equity consideration: The case of COVID-19 vaccine distribution in the European Union

This research develops a humanitarian cold supply chain model with equity consideration for COVID-19 vaccine distribution during a pandemic, considering deprivation cost and an important social concept named equity. The proposed comprehensive plan minimizes all incurred costs, including transportation costs, shortage costs, deprivation costs, and holding costs, while aiming at eliminating infection and mortality rates. The proposed three-echelon supply chain model includes suppliers, distributors, and affected regions (ARs), as destinations. We apply the proposed model to the actual vaccine distribution data during the COVID-19 outbreak in Europe. A mixed integer programming (MIP) model is developed to minimize the costs and satisfy the demand goals in the vaccine distribution plan. A sensitivity analysis demonstrates how total and deprivation costs affect each other, helping the managers establish a trade-off between them. The results show that appropriate supply chain planning can minimize logistics and social costs. The proposed model can help policymakers, and decision-makers better understand the importance of equity and implement a fair distribution of vaccines, considering the deprivation cost as a social cost.

A decision support system for prioritised COVID-19 two-dosage vaccination allocation and distribution.

This study proposes a decision support system (DSS) that integrates GIS, analytics, and simulation methods to help develop a priority-based distribution of COVID-19 vaccines in a large urban setting. The methodology applies novel hierarchical heuristic-simulation procedures to create a holistic algorithm for prioritising the process of demand allocation and optimising vaccine distribution. The Melbourne metropolitan area in Australia with a population of over five million is used as a case study. Three vaccine supply scenarios, namely limited, excessive, and disruption, were formulated to operationalise a two-dose vaccination program. Vaccine distribution with hard constraints were simulated and then further validated with sensitivity analyses. The results show that vaccines can be prioritised to society's most vulnerable segments and distributed using the current logistics network with 10 vehicles. Compared with other vaccine distribution plans with no prioritisation, such as equal allocation of vaccines to local government areas based on population size or one on a first-come-first-serve basis, the plans generated by the proposed DSS ensure prioritised vaccination of the most needed and vulnerable population. The aim is to curb the spread of the infection and reduce mortality rate more effectively. They also achieve vaccination of the entire population with less logistical resources required. As such, this study contributes to knowledge and practice in pandemic vaccine distribution and enables governments to make real-time decisions and adjustments in daily distribution plans. In this way any unforeseen disruptions in the vaccine supply chain can be coped with.

Optimizing vaccine distribution via mobile clinics: a case study on COVID-19 vaccine distribution to long-term care facilities.

BACKGROUND: People living in clustered communities with health comorbidities are highly vulnerable to COVID-19 infection. Rapid vaccination of vulnerable populations is critical to reducing fatalities and mitigating strain on healthcare systems. We present a case study on COVID-19 vaccine distribution via mobile vans to residents/staff of 47,907 long-term care facilities (LTCFs) across the United States that relied on algorithms to optimize vaccine distribution. METHODS: We developed a modeling framework for vaccine distribution to high-risk populations in a supply-constrained environment. Our framework decomposed this challenge as two separate problems: an assignment problem where we optimally mapped each LTCF to select CVS stores responsible for distributing vaccines; and a scheduling problem where we developed an algorithm to assign available resources efficiently. RESULTS: We assigned 1,214 retail stores as depots for vaccine distribution to LTCFs throughout the United States. Forty-one percent of matched depot-LTCF pairs were within 5 miles of a depot, 74% were within 20 miles, and only 8% mapped to depots farther than 50 miles away. Our two-step approach ensured that the first LTCF vaccination dose was distributed within 9 days after the program start date in 76% of states, and greater than 90% of doses were administered in the minimum amount of time. CONCLUSIONS: We demonstrate that algorithmic approaches are instrumental in maximizing vaccine distribution efficiency. Our learning and framework may be of use to other organizations, including communities where mobile clinics can be established to efficiently distribute vaccines and other healthcare resources in a variety of scenarios.

Equitable and Effective Distribution of the COVID-19 Vaccines - A Scientific and Moral Obligation.

The rapid development of coronavirus disease 2019 (COVID-19) vaccines has not been met with the assurance of an effective and equitable global distribution mechanism. Low-income countries are especially at-risk, with the price of the vaccines and supply shortages limiting their ability to procure and distribute the vaccines. While the COVAX initiative is one of the solutions to these challenges, vaccine nationalism has resulted in the hoarding of vaccines and the signing of parallel bilateral deals, undermining this formerly promising initiative. Moreover, inequity in local distribution also remains a problem, with clear discrimination of minorities and lack of logistical preparation in some countries. As we continue to distribute the COVID-19 vaccines, pharmaceutical companies should share their technology to increase supply and reduce prices, governments should prioritize equitable distribution to the most at-risk in all nations and low-income countries should bolster their logistical capacity in preparation for mass vaccination campaigns.