COVID-19: Agent-Based Simulation-Optimization to Vaccine Center Location Vaccine Allocation Problem

This paper presents an agent-based simulation-optimization modeling and algorithmic framework to determine the optimal vaccine center location and vaccine allocation strategies under budget constraints during an epidemic outbreak. Both simulation and optimization models incorporate population health dynamics, such as susceptible (S), vaccinated (V), infected (I) and recovered (R), while their integrated utilization focuses on the COVID-19 vaccine allocation challenges. We first formulate a dynamic location-allocation mixed-integer programming (MIP) model, which determines the optimal vaccination center locations and vaccines allocated to vaccination centers, pharmacies, and health centers in a multi-period setting in each region over a geographical location. We then extend the agent-based epidemiological simulation model of COVID-19 (Covasim) by adding new vaccination compartments representing people who take the first vaccine shot and the first two shots. The Covasim involves complex disease transmission contact networks, including households, schools, and workplaces, and demographics, such as age-based disease transmission parameters. We combine the extended Covasim with the vaccination center location-allocation MIP model into one single simulation-optimization framework, which works iteratively forward and backward in time to determine the optimal vaccine allocation under varying disease dynamics. The agent-based simulation captures the inherent uncertainty in disease progression and forecasts the refined number of susceptible individuals and infections for the current time period to be used as an input into the optimization. We calibrate, validate, and test our simulation-optimization vaccine allocation model using the COVID-19 data and vaccine distribution case study in New Jersey. The resulting insights support ongoing mass vaccination efforts to mitigate the impact of the pandemic on public health, while the simulation-optimization algorithmic framework could be generalized for other epidemics. [ FROM AUTHOR] Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

A two-stages mathematical model for a Covid-19 vaccination fuzzy scheme and resource allocation in hub locations considering transmission risk and environment factor

Tackling with Covid-19 dilemma of vaccine distribution needed a stack of analysis and examination. This paper develops a generalizable framework for designing a hub vaccination dispensing network to achieve expand the Covid-19 vaccination coverage for public. Designing a hub location routing network for vaccine distribution is the main concern for this research. The proposed model hinges on maximum coverage and patients' safety by considering high-priority population alongside the cost reduction in an uncertain environment. The hub location model enhances the accessibility of the vaccines to various communities and helps to overcome the challenges. The results of this model were examined through both numerical and case studies in the north of Tehran to demonstrate its application. Furthermore, in order to reduce the costs of vaccine imports, vaccine entry routing can be developed from border and air points to the country in order to be able to perform vaccination in the fastest time and lowest cost in the future. The results concede that increasing the number of outreach dispensing locations per hub dispensing location will not necessarily result in increased coverage.

Vaccine ethics: An ethical framework for global distribution of COVID-19 vaccines

This paper addresses the just distribution of vaccines against the SARS-CoV- 2 virus and sets forth an ethical framework that prioritises frontline and essential workers, people at high risk of severe disease or death, and people at high risk of infection. Section I makes the case that vaccine distribution should occur at a global level in order to accelerate development and fair, efficient vaccine allocation. Section II puts forth ethical values to guide vaccine distribution including helping people with the greatest need, reducing health disparity, saving the most lives and promoting narrow social utility. It also responds to objections which claim that earlier years have more value than later years. Section III puts forth a practical ethical framework to aid decision-makers and compares it with alternatives. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

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.

The global distribution of COVID-19 vaccines by the public-private partnership COVAX from a public-law perspective

This article examines COVAX, a public private partnership, from a public law perspective. It asks whether COVAX is a legitimate and appropriate instrument with regard to the goal of distributing COVID-19 vaccines in a globally equitable manner and enabling equal access to vaccination worldwide. By developing public-legal legitimacy standards for this purpose, the article critically distances itself from the outset from considering the use of private actors and forms of action in public functions ('privatization') essentially as a release of market economy rationality, which enables efficiency and effectiveness gains and relieves the public sector. With the public law perspective, the article questions precisely whether private-law, market-based action is appropriate with respect to the global distribution of vaccines in the pandemic.

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.

Simulation based adaptive vaccine delivery strategy

Inoculation is one of the most common intervention methods to mitigate the number of incidents during an outbreak. It is a crucial point to decide which age or target groups in a society are priorly vaccinated. In this study, we considered this challenge and a late vaccine distribution scenario with a new vaccine delivery strategy. A given population is divided into five age groups with different contact and transmission rates. The proposed strategy distributes weekly shots to people in an age group or groups according to results of simulation modelling different vaccination strategies for a week time horizon by considering historical incident rates of the outbreak. The method is tested against the strategy of vaccinating schoolchildren considered in many related publications in the literature. According to results, for 20 scenarios based on different contact and transmission rates and under three coverage levels, our method outperforms the benchmark strategy under 20% and 30% coverage levels for each scenario. Both strategies mostly follow same distributions and come up with same results under 10% coverage level. We can conclude that the proposed method is robust to changes in contact and transmission rates and provides superior results when coverage levels are relatively high. The method can provide effective vaccination strategies by considering disease dynamics for primarily COVID-19 and future pandemics.

Evolutionary Optimization of COVID-19 Vaccine Distribution With Evolutionary Demands

Vaccination uptake has become the key factor that will determine our success in containing the coronavirus pneumonia (COVID-19) pandemic. Efficient distribution of vaccines to inoculation spots is crucial to curtailing the spread of the novel COVID-19 pandemic. Normally, in a big city, a huge number of vaccines need to be transported from central depot(s) through a set of satellites to widely scattered inoculation spots by special-purpose vehicles every day. Such a large two-echelon vehicle routing problem is computationally difficult. Moreover, the demands for vaccines evolve with the epidemic spread over time, and the actual demands are hard to determine early and exactly, which not only increases the problem difficulty but also prolongs the distribution time. Based on our practical experience of COVID-19 vaccine distribution in China, we present a hybrid machine learning and evolutionary computation method, which first uses a fuzzy deep learning model to forecast the demands for vaccines for each next day, such that we can predistribute the forecasted number of vaccines to the satellites in advance;after obtaining the actual demands, it uses an evolutionary algorithm (EA) to route vehicles to distribute vaccines from the satellites/depots to the inoculation spots on each day. The EA saves historical problem instances and their high-quality solutions in a knowledge base, so as to capture inherent relationship between evolving problem inputs to solutions;when solving a new problem instance on each day, the EA utilizes historical solutions that perform well on the similar instances to improve initial solution quality and, hence, accelerate convergence. Computational results on real-world instances of vaccine distribution demonstrate that the proposed method can produce solutions with significantly shorter distribution time compared to state-of-the-arts and, hence, contribute to accelerating the achievement of herd immunity.

Distribution and Allocation of Vaccines Against COVID-19 and Vaccination Communication in Thailand

In Thailand, the outbreak of the third wave of COVID-19 infections started on 1 April 2021. From 1 April to 30 November 2021, there have been 2, 087, 009 confirmed cases of COVID-19 with 20, 677 deaths. COVID-19 vaccines are one of many crucial tools in the pandemic response and protect against hospitalization and death. With a population of 66.2 million, Thailand had a target of vaccinating 100 million doses by December 2021. As of 30 November 2021, a total of 92, 658, 390 vaccine doses have been administered, with 48, 307, 704 people receiving a first dose (67.1% of the country's population), 41, 485, 442 people receiving a second dose (57.6% of the country's population), and 3, 438, 317 people receiving a third dose (4.8% of the country's population). Village health volunteers and migrant health volunteers are key and play significant role in public trust in COVID-19 vaccination. COVID-19 vaccination administration is a big challenge to make vaccines available for people residing in Thailand on a foundation of ethics, equality, evidence-based academic, accessible supply, and management capability in national context. © TheEditor(s) (ifapplicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021, 2022.

Optimal design for redistributions among endogenous buyers and sellers

Given a large market of individuals entitled to equal shares of a limited resource, each allowed to buy or sell the shares, we characterize the interim incentive-constrained Pareto frontier subject to market clearance and budget balance. At most two prices—partitioning the type space into at most three tiers and using rations only on the middle tier—are needed to attain any interim Pareto optimum. When the virtual surplus function satisfies a single crossing condition without having to be monotone, the optimal mechanism reduces to a single, posted price and requires neither rationing nor lump sum transfers. We find which types gain, and which types lose, when the social planner chooses a rationing mechanism over the single-price solution, as well as the welfare weight of which type is crucial to the choice. The finding suggests a market-like mechanism to distribute Covid vaccines optimally within the same priority group.

Simulation based adaptive vaccine delivery strategy

Inoculation is one of the most common intervention methods to mitigate the number of incidents during an outbreak. It is a crucial point to decide which age or target groups in a society are priorly vaccinated. In this study, we considered this challenge and a late vaccine distribution scenario with a new vaccine delivery strategy. A given population is divided into five age groups with different contact and transmission rates. The proposed strategy distributes weekly shots to people in an age group or groups according to results of simulation modelling different vaccination strategies for a week time horizon by considering historical incident rates of the outbreak. The method is tested against the strategy of vaccinating schoolchildren considered in many related publications in the literature. According to results, for 20 scenarios based on different contact and transmission rates and under three coverage levels, our method outperforms the benchmark strategy under 20% and 30% coverage levels for each scenario. Both strategies mostly follow same distributions and come up with same results under 10% coverage level. We can conclude that the proposed method is robust to changes in contact and transmission rates and provides superior results when coverage levels are relatively high. The method can provide effective vaccination strategies by considering disease dynamics for primarily COVID-19 and future pandemics. (English) [ FROM AUTHOR] Aşılama, bir salgın sırasında oluşacak vaka sayısını azaltmak için kullanılan en yaygın müdahale yöntemlerinden biridir. Bir toplumda hangi yaş ve hedef gruplarının öncelikle aşılanacağına karar vermek çok önemli bir noktadır. Bu çalışmada hem bu nokta hem de geç aşı dağıtım senaryosu, yeni bir aşı dağıtma stratejisi ile düşünülmüştür. Íncelenen popülasyon farklı kontak ve bulaştırma oranları düşünülerek beş farklı gruba ayrılmıştır. Önerilen aşı dağıtma stratejisi, salgın sırasında oluşan vaka sayılarını da düşünerek, haftalık dağıtılabilecek aşıları farklı yaş gruplarında bulunan kişilere, bir hafta süre için farklı dağıtım stratejilerini modelleyen bir benzetimin sonuçlarına göre dağıtmaktadır. Bu metot, literatürdeki birçok çalışmada düşünülen okul çağındaki çocukları öncelikle aşılama stratejisine karşı test edilmiştir. Farklı kontak ve bulaştırma oranlarına göre oluşturulan 20 farklı senaryo ve 3 farklı kapsama seviyesi için elde edilen sonuçlara göre önerilen metot, %20 ve %30 kapsama seviyesi için karşılaştırılan stratejiden daha iyi sonuçlar vermiş, %10 kapsama seviyesi için de benzer sonuçlar gözlenmiştir. Sonuç olarak, kapsama seviyesinin göreceli daha yüksek olduğu durumlarda, önerilen metodun kontak ve bulaş oranlarında meydana gelen değişimlere karşı daha gürbüz olduğu ve daha iyi sonuçlar verdiği görülmüştür. Başta COVID-19 olmak üzere gelecekte yaşanabilecek salgınlarda, hastalık dinamiklerini de düşünerek, efektif aşı dağıtımlarını gerçekleştirebilecektir. (Turkish) [ FROM AUTHOR] Copyright of Journal of the Faculty of Engineering & Architecture of Gazi University / Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, is the property of Gazi University, Faculty of Engineering & Architecture and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Simulation based adaptive vaccine delivery strategy.

Inoculation is one of the most common intervention methods to mitigate the number of incidents during an outbreak. It is a crucial point to decide which age or target groups in a society are priorly vaccinated. In this study, we considered this challenge and a late vaccine distribution scenario with a new vaccine delivery strategy. A given population is divided into five age groups with different contact and transmission rates. The proposed strategy distributes weekly shots to people in an age group or groups according to results of simulation modelling different vaccination strategies for a week time horizon by considering historical incident rates of the outbreak. The method is tested against the strategy of vaccinating schoolchildren considered in many related publications in the literature. According to results, for 20 scenarios based on different contact and transmission rates and under three coverage levels, our method outperforms the benchmark strategy under 20% and 30% coverage levels for each scenario. Both strategies mostly follow same distributions and come up with same results under 10% coverage level. We can conclude that the proposed method is robust to changes in contact and transmission rates and provides superior results when coverage levels are relatively high. The method can provide effective vaccination strategies by considering disease dynamics for primarily COVID-19 and future pandemics. (English) [ FROM AUTHOR] Aşılama, bir salgın sırasında oluşacak vaka sayısını azaltmak için kullanılan en yaygın müdahale yöntemlerinden biridir. Bir toplumda hangi yaş ve hedef gruplarının öncelikle aşılanacağına karar vermek çok önemli bir noktadır. Bu çalışmada hem bu nokta hem de geç aşı dağıtım senaryosu, yeni bir aşı dağıtma stratejisi ile düşünülmüştür. Íncelenen popülasyon farklı kontak ve bulaştırma oranları düşünülerek beş farklı gruba ayrılmıştır. Önerilen aşı dağıtma stratejisi, salgın sırasında oluşan vaka sayılarını da düşünerek, haftalık dağıtılabilecek aşıları farklı yaş gruplarında bulunan kişilere, bir hafta süre için farklı dağıtım stratejilerini modelleyen bir benzetimin sonuçlarına göre dağıtmaktadır. Bu metot, literatürdeki birçok çalışmada düşünülen okul çağındaki çocukları öncelikle aşılama stratejisine karşı test edilmiştir. Farklı kontak ve bulaştırma oranlarına göre oluşturulan 20 farklı senaryo ve 3 farklı kapsama seviyesi için elde edilen sonuçlara göre önerilen metot, %20 ve %30 kapsama seviyesi için karşılaştırılan stratejiden daha iyi sonuçlar vermiş, %10 kapsama seviyesi için de benzer sonuçlar gözlenmiştir. Sonuç olarak, kapsama seviyesinin göreceli daha yüksek olduğu durumlarda, önerilen metodun kontak ve bulaş oranlarında meydana gelen değişimlere karşı daha gürbüz olduğu ve daha iyi sonuçlar verdiği görülmüştür. Başta COVID-19 olmak üzere gelecekte yaşanabilecek salgınlarda, hastalık dinamiklerini de düşünerek, efektif aşı dağıtımlarını gerçekleştirebilecektir. (Turkish) [ FROM AUTHOR] Copyright of Journal of the Faculty of Engineering & Architecture of Gazi University / Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, is the property of Gazi University, Faculty of Engineering & Architecture and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

A robust optimization problem for drone-based equitable pandemic vaccine distribution with uncertain supply.

Widespread vaccination is the only way to overcome the COVID-19 global crisis. However, given the vaccine scarcity during the early outbreak of the pandemic, ensuring efficient and equitable distribution of vaccines, particularly in rural areas, has become a significant challenge. To this end, this study develops a two-stage robust vaccine distribution model that addresses the supply uncertainty incurred by vaccine shortages. The model aims to optimize the social and economic benefits by jointly deciding vaccination facility location, transportation capacity, and reservation plan in the first stage, and rescheduling vaccinations in the second stage after the confirmation of uncertainty. To hedge vaccine storage and transportation difficulties in remote areas, we consider using drones to deliver vaccines in appropriate and small quantities to vaccination points. Two tailored column-and-constraint generation algorithms are proposed to exactly solve the robust model, in which the subproblems are solved via the vertex traversal and the dual methods, respectively. The superiority of the dual method is further verified. Finally, we use real-world data to demonstrate the necessity to account for uncertain supply and equitable distribution, and analyze the impacts of several key parameters. Some managerial insights are also produced for decision-makers.

Comparative performance of between-population vaccine allocation strategies with applications for emerging pandemics.

Vaccine allocation decisions during emerging pandemics have proven to be challenging due to competing ethical, practical, and political considerations. Complicating decision making, policy makers need to consider vaccine allocation strategies that balance needs both within and between populations. When vaccine stockpiles are limited, doses should be allocated in locations to maximize their impact. Using a susceptible-exposed-infectious-recovered (SEIR) model we examine optimal vaccine allocation decisions across two populations considering the impact of characteristics of the population (e.g., size, underlying immunity, heterogeneous risk structure, interaction), vaccine (e.g., vaccine efficacy), pathogen (e.g., transmissibility), and delivery (e.g., varying speed and timing of rollout). Across a wide range of characteristics considered, we find that vaccine allocation proportional to population size (i.e., pro-rata allocation) performs either better or comparably to nonproportional allocation strategies in minimizing the cumulative number of infections. These results may argue in favor of sharing of vaccines between locations in the context of an epidemic caused by an emerging pathogen, where many epidemiologic characteristics may not be known.

Resource distribution under spatiotemporal uncertainty of disease spread: Stochastic versus robust approaches

We consider the problem of optimizing locations of distribution centers (DCs) and plans for distributing resources such as test kits and vaccines, under spatiotemporal uncertainties of disease spread and demand for the resources. We aim to balance the operational cost (including costs of deploying facilities, shipping, and storage) and quality of service (reflected by demand coverage), while ensuring equity and fairness of resource distribution across multiple populations. We compare a sample-based stochastic programming (SP) approach with a distributionally robust optimization (DRO) approach using a moment-based ambiguity set. Numerical studies are conducted on instances of distributing COVID-19 vaccines in the United States and test kits, to compare SP and DRO models with a deterministic formulation using estimated demand and with the current resource distribution plans implemented in the US. We demonstrate the results over distinct phases of the pandemic to estimate the cost and speed of resource distribution depending on scale and coverage, and show the "demand-driven” properties of the SP and DRO solutions. Our results further indicate that if the worst-case unmet demand is prioritized, then the DRO approach is preferred despite of its higher overall cost. Nevertheless, the SP approach can provide an intermediate plan under budgetary restrictions without significant compromises in demand coverage. © 2022 Elsevier Ltd

Simulation based adaptive vaccine delivery strategy

Inoculation is one of the most common intervention methods to mitigate the number of incidents during an outbreak. It is a crucial point to decide which age or target groups in a society are priorly vaccinated. In this study, we considered this challenge and a late vaccine distribution scenario with a new vaccine delivery strategy. A given population is divided into five age groups with different contact and transmission rates. The proposed strategy distributes weekly shots to people in an age group or groups according to results of simulation modelling different vaccination strategies for a week time horizon by considering historical incident rates of the outbreak. The method is tested against the strategy of vaccinating schoolchildren considered in many related publications in the literature. According to results, for 20 scenarios based on different contact and transmission rates and under three coverage levels, our method outperforms the benchmark strategy under 20% and 30% coverage levels for each scenario. Both strategies mostly follow same distributions and come up with same results under 10% coverage level. We can conclude that the proposed method is robust to changes in contact and transmission rates and provides superior results when coverage levels are relatively high. The method can provide effective vaccination strategies by considering disease dynamics for primarily COVID-19 and future pandemics. (English) [ FROM AUTHOR]

COVID-19: Prediction, Prevalence, and the Operations of Vaccine Allocation

Problem definition: Mitigating the COVID-19 pandemic poses a series of unprecedented challenges, including predicting new cases and deaths, understanding true prevalence beyond what tests are able to detect, and allocating different vaccines across various regions. In this paper, we describe our efforts to tackle these issues and explore the impact on combating the pandemic in terms of case and death prediction, true prevalence, and fair vaccine distribution. Methodology/results: We present the methods we developed for predicting cases and deaths using a novel machine-learning-based aggregation method to create a single prediction that we call MIT-Cassandra. We further incorporate COVID-19 case prediction to determine true prevalence and incorporate this prevalence into an optimization model for efficiently and fairly managing the operations of vaccine allocation. We study the trade-offs of vaccine allocation between different regions and age groups, as well as first-and second-dose distribution of different vaccines. This also allows us to provide insights into how prevalence and exposure of the disease in different parts of the population can affect the distribution of different vaccine doses in a fair way. Managerial implications: MIT-Cassandra is currently being used by the Centers for Disease Control and Prevention and is consistently among the best-performing methods in terms of accuracy, often ranking at the top. In addition, our work has been helping decision makers by predicting how cases and true prevalence of COVID-19 will progress over the next few months in different regions and utilizing the knowledge for vaccine distribution under various operational constraints. Finally, and very importantly, our work has specifically been used as part of a collaboration with the Massachusetts Institute of Technology's (MIT's) Quest for Intelligence and as part of MIT's process to reopen the institute.

Improving the distribution of covid-19 vaccines using the blockchain technology: the Italian case study.

Vaccination is one of the most effective ways to prevent and control the outbreak of infectious diseases. The vaccine supply chain differs from the traditional supply chains because of the perishability of the products, which need strict transport and warehousing conditions to guarantee the health and safety of people. In addition, in case of pandemics, the big amount of doses requested for the implementation of a mass vaccination campaign forces governments to design a proper logistic network and plan a rapid and efficient distribution of vaccines. This paper studies the organization of allocation and distribution of the covid-19 vaccines in Italy. The main criticalities in managing the vaccine supply chain have been identified and, because of its peculiarities, the blockchain has been considered a suitable technology to solve them. A simulation model has been developed to reproduce the current distribution of vaccines in Italy, and a future scenario with blockchain has been studied. The findings show that it is possible to improve the performance of the vaccine supply chain and make it more resilient by implementing the blockchain technology.

Analysis of performance of Covid-19 vaccine supply chain in India

PurposeThis study aims to analyse the structure of the Indian vaccine supply chain (SC) during the Covid-19 crisis and explore the underlying challenges at each stage in the network. It also brings out the difference in performance of various constituent states. Design/methodology/approachThis study relied on both primary and secondary data for the analyses. For the primary data, the study gathered experts' opinions to validate the authors' inferences. For the secondary data, it relies on government data provided in websites. FindingsBased on the quartile analysis and cluster analysis of the secondary data, the authors find that the constituent states responded differently during the first and second waves. This was due to the differences in SC characteristics attributed to varied demographics and administrative efficiency. Research limitations/implicationsThis paper's analyses is primarily limited to secondary information and inferences are based on them. The study has important implications for implementing the large-scale vaccination drives by government and constituent states for better coordination and last-mile delivery. Originality/valueThe contribution is unique in studying the performance of constituent states using statistical techniques, with secondary data from authentic sources. It is also unique in combining this observation with validation from experts.

An Examination of US COVID-19 Vaccine Distribution in New Jersey, Pennsylvania, and New York.

COVID-19 quickly spread across the United States (US) while communications and policies at all government levels suffered from inconsistency, misinformation, and lack of coordination. In order to explain the discrepancy between availability and population uptake, a case study was conducted analyzing vaccine rollout plans, social media, and Health Officer/Other Key Informant interviews in New Jersey, New York, and Pennsylvania. Key research questions included, "What were the barriers and facilitators of early COVID vaccine distribution?" and "What mechanisms in the community emerged to alleviate strains in early vaccination?" Findings from this study revealed that pre-existing emergency preparedness infrastructures and plans developed since the 9/11 tragedy were seemingly abandoned. This caused health departments at all levels of government to make impromptu, non-uniform decisions leading to confusion, vaccine hesitancy, and ultimately low uptake. The results indicate that future vaccine rollout best practices must include evidence-based decision-making, coordinated communications, and outreach to high-priority and vulnerable communities.