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India implemented the largest COVID-19 vaccination drive in the world, through which it vaccinated the majority of its population. Lessons from the Indian COVID-19 vaccination experience can be invaluable for other LMICs as well as for preparedness for future outbreaks. Our study is designed to explore the factors associated with COVID-19 vaccination coverage in India at the district level. We used data from COVID-19 vaccination in India combined with several other administrative data to create a unique data set that facilitated a spatio-temporal exploratory analysis by uncovering the factors associated with vaccination rates across different vaccination phases and districts. We found evidence that past reported infection rates were positively correlated with COVID-19 vaccination outcomes. Past cumulative COVID-19 deaths as a proportion of district populations were associated with lower COVID-19 vaccination, but the percentage of past reported infection was positively correlated with first-dose COVID-19 vaccination, which might indicate a positive role of higher awareness created by a higher reported infection rate. Districts that on average had a higher population burden per health centre were likely to have lower COVID-19 vaccination rates. Vaccination rates were lower in rural areas relative to urban areas, whereas the association with literacy rate was positive. Districts with a higher percentage of children with complete immunisation were associated with higher COVID-19 vaccination, whereas low vaccination was observed in districts that had higher percentages of wasted children. COVID-19 vaccination was lower among pregnant and lactating women. Higher vaccination was observed among populations with higher blood pressure and hypertension (which were a few of the co-morbidities associated with COVID-19 infection).
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Developing a vaccine supply chain (VSC) is an intricate process due to product perishability issues and cross-border supply complexities. On top of that, developing a pandemic-driven VSC is more challenging due to having significant operational, infrastructural, and policy-related disruptions. From the perspective of a developing economy such as Bangladesh, handling the global COVID-19 pandemic through the proper establishment of a VSC has been disrupted by a multitude of organizational, economic, and policy barriers. This has hindered the process of establishing a resilient VSC let alone ensuring the sustainability of the supply chain (SC). Therefore, this study strives to identify the key VSC strategies and their interrelationships under four groups: Intra-organizational, Inter-organizational, Legislative, and Environmental, based on previous literature and the expert opinions of industrial practitioners and policymakers. 20 strategies are ranked, and their causal relationships are discussed using the fuzzy DEMATEL method. This study utilizes the fuzzy set theory to deal with the vagueness of human beings' perceptions, and the DEMATEL method to form a structural model to find out the cause (influencing and independent) and effect (influenced and dependent) relationships among different strategies. The outcome of this study shows that ‘developing local production facilities for vaccines', ‘creating extensive governmental policy to ensure efficient distribution of vaccines', ‘ensuring sustainable investment in vaccine manufacturing and distribution', ‘integrating advanced data analytics for robust and resilient demand prediction' and ‘promoting public-private-people partnership for sustainable investment' are the most prominent strategies. The findings provide stakeholders and policymakers with a practical framework for developing a sustainable VSC prepared for any virus outbreak, such as COVID-19, while also achieving the Sustainable Development Goals (SDGs).
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The goal of pandemic response is to provide the greatest protection, for the most people, in the least amount of time. Short response times minimize both current and future health impacts for evolving pathogens that pose global threats. To achieve this goal, efficient and effective systems are needed for distributing and administering vaccines, a cornerstone of pandemic response. COVID-19 vaccines were developed in record time in the U.S. and abroad, but U.S. data shows that they were not distributed efficiently and effectively once available. In an effort to "put vaccines on every corner”, pharmacies and other small venues were a primary means for vaccinating individuals, but daily throughput rates at these locations were very low. This contributed to extended times from manufacture to administration. An important contributing factor to slow administration rates for COVID-19 was vaccine transport and storage box size. In this paper, we establish a general system objective and provide a computationally tractable approach for allocating vaccines in a rolling horizon manner optimally. We illustrate the consequences of both box size and the number and capacity of dispensing locations on achieving system objectives. Using U.S. CDC data, we demonstrate that if vaccines are allocated and distributed according to our proposed strategy, more people would have been vaccinated sooner in the U.S. Many additional days of protection would have occurred, meaning there would have been fewer infections, less demand for healthcare resources, lower overall mortality, and fewer opportunities for the evolution of vaccine-evading strains of the disease.
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[This corrects the article DOI: 10.3389/fpubh.2022.935400.].
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PurposeEach individual needs to be vaccinated to control the spread of the COVID-19 pandemic in the shortest possible time. However, the vaccine distribution with an already strained supply chain in low- and middle-income countries (LMICs) will not be effective enough to vaccinate all the population in stipulated time. The purpose of this paper is to show that there is a need to revolutionize the vaccine supply chain (VSC) by overcoming the challenges of sustainable vaccine distribution.Design/methodology/approachAn integrated lean, agile and green (LAG) framework is proposed to overcome the challenges of the sustainable vaccine supply chain (SVSC). A hybrid best worst method (BWM)–Measurement of Alternatives and Ranking According to COmpromise Solution (MARCOS) methodology is designed to analyze the challenges and solutions.FindingsThe analysis shows that vaccine wastage is the most critical challenge for SVSC, and the coordination among stakeholders is the most significant solution followed by effective management support.Social implicationsThe result of the analysis can help the health care organizations (HCOs) to manage the VSC. The effective vaccination in stipulated time will help control the further spread of the virus, which will result in the normalcy of business and availability of livelihood for millions of people.Originality/valueTo the best of the author's knowledge, this is the first study to explore sustainability in VSC by considering the environmental and social impact of vaccination. The LAG-based framework is also a new approach in VSC to find the solution for existing challenges.
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In the digital age, operations can be improved by a wise use of information and technological tools. During the COVID-19 pandemic, governments faced various vaccine choices having different efficacy and availability levels at different time points. In this paper, we consider a two-stage vaccine ordering problem of a government from a first and only supplier in the first stage, and either the same supplier or a new second supplier in the second stage. Between the two stages, potential demand information for the vaccine is collected to update the forecast. Using dynamic programming, we derive the government's optimal vaccine ordering policy. We find that the government should select its vaccine supplier based on the disease's infection rate in the society. When the infection rate is low, the government should order nothing at the first stage and order from the supplier with a higher efficacy level at the second stage. When the disease's infection rate is high, the government should order vaccines at the first stage and switch to the other supplier with a lower efficacy level at the second stage. We extend our model to examine (i) the value of blockchain adoption and (ii) the impact of vaccines' side effects. [ 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.)
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PurposeThis paper focuses on multi-objective order allocation with product substitution for the vaccine supply chain under uncertainty.Design/methodology/approachThe weighted-sum minimization approach is used to find a compromised solution between three objectives of minimizing inefficiently vaccinated people, postponed vaccinations, and purchasing costs. A mixed-integer formulation with substitution quantities is proposed, subject to capacity and demand constraints. The substitution ratios between vaccines are assumed to be exogenous. Besides, uncertainty in supplier reliability is formulated using optimistic, most likely, and pessimistic scenarios in the proposed optimization model.FindingsCovid-19 vaccine supply chain process is studied for one government and three vaccine suppliers as an illustrative example. The results provide essential insights for the governments to have proper vaccine allocation and support governments to manage the Covid-19 pandemic.Originality/valueThis paper considers the minimization of postponement in vaccination plans and inefficient vaccination and purchasing costs for order allocation among different vaccine types. To the best of the authors' knowledge, there is no study in the literature on order allocation of vaccine types with substitution. The analytical hierarchy process structure of the Covid-19 pandemic also contributes to the literature.
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PurposeImmunization is one of the most cost-effective ways to save lives while promoting good health and happiness. The coronavirus disease 2019 (COVID-19) pandemic has served as a stark reminder of vaccines' ability to prevent transmission, save lives, and have a healthier, safer and more prosperous future. This research investigates the sustainable development (SD) of the COVID-19 vaccine supply chain (VSC).Design/methodology/approachThis study investigates the relationship between internal process, organizational growth, and its three pillars of SD environmental sustainability, economic sustainability and social sustainability. Survey-based research is carried out in the hospitals providing COVID-19 vaccines. Nine hypotheses are proposed for the study, and all the hypotheses got accepted. The survey was sent to 428 respondents and received 291 responses from health professionals with a response rate of 68%. For the study, the healthcare professionals working in both private and public hospitals across India were selected.FindingsThe structural equation modelling (SEM) approach is used to test the hypothesis. All nine hypotheses are supported. This study examines a link between internal processes and organizational learning and the three sustainability pillars (environmental sustainability, economic sustainability and social sustainability).Practical implicationsThis study will help the management and the policymakers to think and adopt SD in the COVID-19 VSC. This paper also implies that robust immunization systems will be required in the future to ensure that people worldwide are protected from COVID-19 and other diseases.Originality/valueThis paper shows the relationship between organizational learning and internal process with environmental sustainability, economic sustainability and social sustainability for the COVID-19. Studies on VSC of COVID-19 are not evident in any previous literature.
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As an abrupt epidemic occurs, healthcare systems are shocked by the surge in the number of susceptible patients' demands, and decision-makers mostly rely on their frame of reference for urgent decision-making. Many reports have declared the COVID-19 impediments to trading and global economic growth. This study aims to provide a mathematical model to support pharmaceutical supply chain planning during the COVID-19 epidemic. Additionally, it aims to offer new insights into hospital supply chain problems by unifying cold and non-cold chains and considering a wide range of pharmaceuticals and vaccines. This approach is unprecedented and includes an analysis of various pharmaceutical features such as temperature, shelf life, priority, and clustering. To propose a model for planning the pharmaceutical supply chains, a mixed-integer linear programming (MILP) model is used for a four-echelon supply chain design. This model aims to minimize the costs involved in the pharmaceutical supply chain by maintaining an acceptable service level. Also, this paper considers uncertainty as an intrinsic part of the problem and addresses it through the wait-and-see method. Furthermore, an unexplored unsupervised learning method in the realm of supply chain planning has been used to cluster the pharmaceuticals and the vaccines and its merits and drawbacks are proposed. A case of Tehran hospitals with real data has been used to show the model's capabilities, as well. Based on the obtained results, the proposed approach is able to reach the optimum service level in the COVID conditions while maintaining a reduced cost. The experiment illustrates that the hospitals' adjacency and emergency orders alleviated the service level significantly. The proposed MILP model has proven to be efficient in providing a practical intuition for decision-makers. The clustering technique reduced the size of the problem and the time required to solve the model considerably.
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PurposeDue to the introduction of new vaccines in the child immunization program and inefficient vaccine supply chain (VSC), the universal immunization program (UIP), India is struggling to provide a full schedule of vaccination to the targeted children. In this paper, the authors investigate the critical factors for improving the performance of the existing VSC system by implementing the next-generation vaccine supply chain (NGVSC) in India.Design/methodology/approachThe authors design a fuzzy multi-criteria framework using a fuzzy analytical hierarchical process (FAHP) and fuzzy multi-objective optimization on the basis of ratio analysis (FMOORA) to identify and analyze the critical barriers and enablers for the implementation of NGVSC. Further, the authors carry out a numerical simulation to validate the model.FindingsThe outcome of the analysis contends that demand forecasting is the topmost supply chain barrier and sustainable financing is the most important/critical enabler to facilitate the implementation of the NGVSC. In addition, the simulation reveals that the results of the study are reliable.Social implicationsThe findings of the study can be useful for the child immunization policymakers of India and other developing countries to design appropriate strategies for improving existing VSC performance by implementing the NGVSC.Originality/valueTo the best of the authors' knowledge, the study is the first empirical study to propose the improvement of VSC performance by designing the NGVSC.
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To combat the COVID-19 pandemic, the scientific community has progressed from discovering antivirals to the large-scale production of vaccines. Mass vaccination programs to curb the COVID-19 pandemic started in many parts of the world at the beginning of 2021. Mass vaccination aims to exit from health emergencies by vaccinating all the population with the required dose in the shortest possible time. The production rate has been boosted, and many new production facilities have been opened to fulfill worldwide demand. The objective of the vaccination program is to maximize the medical benefit with the lowest cost and equitable distribution of vaccines worldwide. However, the environmental impact of this long-run immunization program has received very little attention. This study explores the environmental impact of the vaccine supply chain (VSC) and analyzes the mitigation strategies to minimize it without affecting the medical, economic, and social benefits of vaccination. The fuzzy DEMATEL technique has been used to prioritize the mitigation techniques and find cause and effect relations among them. The finding of studies shows that the "optimal vial design” is most important, and "vaccine awareness and education” is the most impactful strategy to tackle the environmental impact of VSC. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Purpose: This study aims to focus on building a conceptual closed-loop vaccine supply chain (CLVSC) to decrease vaccine wastage and counterfeit/fake vaccines. Design/methodology/approach: Through a focused literature review, the framework for the CLVSC is described, and the system dynamics (SD) research methodology is used to build a causal loop diagram (CLD) of the proposed model. Findings: In the battle against COVID-19, waste management systems have become overwhelmed, which has created negative environmental and extremely hazardous societal impacts. A key contributing factor is unused vaccine doses, shown as a source for counterfeit/fake vaccines. The findings identify a CLVSC design and transshipment operations to decrease vaccine wastage and the potential for vaccine theft. Research limitations/implications: This study contributes to establishing a pandemic-specific VSC structure. The proposed model informs the current COVID-19 pandemic as well as potential future pandemics. Social implications: A large part of the negative impact of counterfeit/fake vaccines is on human well-being, and this can be avoided with proper CLVSC. Originality/value: This study develops a novel overarching SD CLD by integrating the epidemic model of disease transmission, VSC and closed-loop structure. This study enhances the policymakers' understanding of the importance of vaccine waste collection, proper handling and threats to the public, which are born through illicit activities that rely on stolen vaccine doses. © 2023, Esen Andiç-Mortan and Cigdem Gonul Kochan.
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The vaccine distribution system, being a bio-pharmaceutical cold chain, is a complicated and sensitive system that must be effectively managed and maintained due to its direct impact on public health. However, vaccine supply chains continue to be affected by concerns, including vaccine expiry, inclusion of counterfeit vaccines, and vaccine record fraud. The blockchain technology integrated with the Internet of Things (IoT) can create a solution for global vaccine distributions with improved trust, transparency, traceability, and data management, which will help monitor the cold chain, tackle counterfeit drugs, surveillance, and waste management. Several theoretical models for vaccine management with blockchain have recently been published, and a few pilot studies for COVID-19 vaccine management using blockchain have been started in India. Still, full-scale adoption of blockchain technology in vaccine distribution and management has yet to be achieved due to underlying barriers. This study explores the adoption barriers utilizing Technology-Organization-Environment (TOE) framework with the help of extant literature and inputs from administrators, academics, immunization, and blockchain experts and then analyzed using the Delphi and fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) techniques. The finding shows that the requirement of change in organizational structure and policies is the most prominent barrier, and the barrier related to requirement of large-scale IoT infrastructure and lack of technical expertise are the most impactful barriers. The theoretical contribution of this study lies in the identification and analysis of barriers that should be addressed to achieve blockchain technology adoption in the vaccine supply chain. © 2022 Elsevier B.V.
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With the discovery of the COVID-19 vaccine, what has always been worrying the decision-makers is related to the distribution management, the vaccination centers' location, and the inventory control of all types of vaccines. As the COVID-19 vaccine is highly demanded, planning for its fair distribution is a must. University is one of the most densely populated areas in a city, so it is critical to vaccinate university students so that the spread of this virus is curbed. As a result, in the present study, a new stochastic multi-objective, multi-period, and multi-commodity simulation-optimization model has been developed for the COVID-19 vaccine's production, distribution, location, allocation, and inventory control decisions. In this study, the proposed supply chain network includes four echelons of manufacturers, hospitals, vaccination centers, and volunteer vaccine students. Vaccine manufacturers send the vaccines to the vaccination centers and hospitals after production. The students with a history of special diseases such as heart disease, corticosteroids, blood clots, etc. are vaccinated in hospitals because of accessing more medical care, and the rest of the students are vaccinated in the vaccination centers. Then, a system dynamic structure of the prevalence of COVID -19 in universities is developed and the vaccine demand is estimated using simulation, in which the demand enters the mathematical model as a given stochastic parameter. Thus, the model pursues some goals, namely, to minimize supply chain costs, maximize student desirability for vaccination, and maximize justice in vaccine distribution. To solve the proposed model, Variable Neighborhood Search (VNS) and Whale Optimization Algorithm (WOA) algorithms are used. In terms of novelties, the most important novelties in the simulation model are considering the virtual education and exerted quarantine effect on estimating the number of the vaccines. In terms of the mathematical model, one of the remarkable contributions is paying attention to social distancing while receiving the injection and the possibility of the injection during working and non-working hours, and regarding the novelties in the solution methodology, a new heuristic method based on a meta-heuristic algorithm called Modified WOA with VNS (MVWOA) is developed. In terms of the performance metrics and the CPU time, the MOWOA is discovered with a superior performance than other given algorithms. Moreover, regarding the data, a case study related to the COVID-19 pandemic period in Tehran/Iran is provided to validate the proposed algorithm. The outcomes indicate that with the demand increase, the costs increase sharply while the vaccination desirability for students decreases with a slight slope.
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In 2020, the COVD-19 pandemic emerged as the most severe crisis of the century. Several vaccine manufacturing firms have taken the necessary initiatives to combat this problem. However, profitability issues can bring down these firms' vaccine manufacturing efforts, thus leading to lower vaccination coverage. Motivated by this issue, we depict a private COVID-19 vaccine supply chain with a supply chain framework comprising of one vaccine manufacturer and multiple private hospitals under demand uncertainty. We incorporate a Stackelberg game-theoretic approach to demonstrate the collaboration between the vaccine manufacturer and the private hospital using wholesale price, two-part tariff and revenue sharing contracts. We determine the optimal number of vaccines and coordination criteria for each contract. Using a real-life approximation of Indian data, we conduct several numerical studies and facilitate the visual depiction of all the theoretical insights obtained from the model. We also discuss the managerial implications of this study. As per our analysis, when private hospitals procure a higher number of vaccines from the vaccine manufacturer, the two-part tariff contract-based collaboration mechanism yields a win-win situation for both the private hospitals and the vaccine manufacturer and is better than the wholesale price contract.
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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.
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Currently, the global spread of COVID-19 is taking a heavy toll on the lives of the global population. There is an urgent need to improve and strengthen the coordination of vaccine supply chains in response to this severe pandemic. In this study, we consider a vaccine supply chain based on a combination of artificial intelligence and blockchain technologies and model the supply chain as a two-player dynamic game with inventory level as the dynamic equation of the system. The study focuses on the applicability and effectiveness of the two technologies in the vaccine supply chain and provides management insights. The impact of the application of the technologies on environmental performance is also considered in the model. We also examine factors such as the number of people vaccinated, positive and side effects of vaccines, vaccine decay rate, revenue-sharing/cost-sharing ratio, and commission ratio. The results are as follows: the correlation between the difficulty in obtaining certified vaccines and the profit of a vaccine manufacturer is not monotonous; the vaccine manufacturer is more sensitive to changes in the vaccine attenuation rate. The study's major conclusions are as follows: First, the vaccine supply chain should estimate the level of consumers' difficulty in obtaining a certified vaccine source and the magnitude of the production planning and demand forecasting error terms before adopting the two technologies. Second, the application of artificial intelligence (AI) technology is meaningful in the vaccine supply chain when the error terms satisfy a particular interval condition.
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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.
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Vaccination offers health, economic, and social benefits. However, three major issues-vaccine quality, demand forecasting, and trust among stakeholders-persist in the vaccine supply chain (VSC), leading to inefficiencies. The COVID-19 pandemic has exacerbated weaknesses in the VSC, while presenting opportunities to apply digital technologies to manage it. For the first time, this study establishes an intelligent VSC management system that provides decision support for VSC management during the COVID-19 pandemic. The system combines blockchain, internet of things (IoT), and machine learning that effectively address the three issues in the VSC. The transparency of blockchain ensures trust among stakeholders. The real-time monitoring of vaccine status by the IoT ensures vaccine quality. Machine learning predicts vaccine demand and conducts sentiment analysis on vaccine reviews to help companies improve vaccine quality. The present study also reveals the implications for the management of supply chains, businesses, and government.
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The vaccine distribution system, being a bio-pharmaceutical cold chain, is a complicated and sensitive system that must be effectively managed and maintained due to its direct impact on public health. However, vaccine supply chains continue to be affected by concerns, including vaccine expiry, inclusion of counterfeit vaccines, and vaccine record fraud. The blockchain technology integrated with the Internet of Things (IoT) can create a solution for global vaccine distributions with improved trust, transparency, traceability, and data management, which will help monitor the cold chain, tackle counterfeit drugs, surveillance, and waste management. Several theoretical models for vaccine management with blockchain have recently been published, and a few pilot studies for COVID-19 vaccine management using blockchain have been started in India. Still, full-scale adoption of blockchain technology in vaccine distribution and management has yet to be achieved due to underlying barriers. This study explores the adoption barriers utilizing Technology-Organization-Environment (TOE) framework with the help of extant literature and inputs from administrators, academics, immunization, and blockchain experts and then analyzed using the Delphi and fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) techniques. The finding shows that the requirement of change in organization structure and policies is the most prominent barrier, and the barrier related to requirement of large-scale IoT infrastructure and lack of technical expertise are the most impactful barriers. The theoretical contribution of this study lies in the identification and analysis of barriers that should be addressed to achieve blockchain technology adoption in the vaccine supply chain.