Vaccine Distribution Research Articles

Modeling COVID-19 vaccine booster-elicited antibody response and impact of infection history

The 3-dose COVID-19 vaccine (booster vaccination) has been offered worldwide. As booster vaccinations continue, it is important to understand the antibody dynamics elicited by booster vaccination in order to evaluate and develop vaccination needs and strategies. Here, we investigated longitudinal data by monitoring IgG antibodies against the receptor binding domain (RBD) in health care workers. We extended our previously developed mathematical model to booster vaccines and successfully fitted antibody titers over time in the absence and presence of past SARS-CoV-2 infection. Quantitative analysis using our mathematical model indicated that anti-RBD IgG titers increase to a comparable extent after booster vaccination, regardless of the presence or absence of infection, but infection history extends the duration of antibody response by 1.28 times. Such a mathematical modeling approach can be used to inform future vaccination strategies on the basis of an individual's immune history. Our simple quantitative approach can be extended to any kind of vaccination and therefore can form a basis for policy decisions regarding the distribution of booster vaccines to strengthen immunity in future pandemics.

CIA-CVD: cloud based image analysis for COVID-19 vaccination distribution

Due to the huge impact of COVID-19, the world is currently facing a medical emergency and shortage of vaccine. Many countries do not have enough medical equipment and infrastructure to tackle this challenge. Due to the lack of a central administration to guide the countries to take the necessary precautions, they do not proactively identify the cases in advance. This has caused Covid-19 cases to be on the increase, with the number of cases increasing at a geometric progression. Rapid testing, RT-PCR testing, and a CT-Scan/X-Ray of the chest are the primary procedures in identifying the covid-19 disease. Proper immunization is delivered on a priority basis based on the instances discovered in order to preserve human lives. In this research paper, we suggest a technique for identifying covid-19 positive cases and determine the most affected locations of covid-19 cases for vaccine distribution in order to limit the disease's impact. To handle the aforementioned issues, we propose a cloud based image analysis approach for using a COVID-19 vaccination distribution (CIA-CVD) model. The model uses a deep learning, machine learning, digital image processing and cloud solution to deal with the increasing cases of COVID-19 and its priority wise distribution of the vaccination.Graphical

Peran United Nations High Commissioner for Refugee dalam Membantu Kebutuhan Kesehatan Pengungsi Konflik Tigray di Ethiopia (2022-2023)

This article aims to explain the health conditions of Tigray Conflict refugees and the role of the United Nations High Commissioner for Refugees (UNHCR) in assisting the health needs of Tigray Conflict refugees in Ethiopia in the period 2022-2023. The author uses the theory of international organizations which sees that international organizations have five important roles in resolving problems. This theory is very relevant to the context of the author’s research by looking at UNHCR as an international organization which has one of the five important roles of international organizations, namely as an Aid Provider in resolving conflict in the Tigray region. The type of research used in this article is descriptive, using a library study method which is carried out by collecting data from journals, books, websites and articles as a data collection technique. Based on the results of this research, UNHCR as an international organization working in the field of refugees took part in handling refugees due to the conflict that occurred in Ethiopia, specifically in the Tigray region, namely the conflict between the central government and the Tigrayan People's Liberation Front (TPLF), so the author found that UNHCR had plays a role in providing assistance in the form of basic health services, distribution of vaccines and immunizations, as well as workshops and mental health services.

Sustainable Supply Chain Management of COVID-19 Vaccines for Vaccination Delivery based on Routing Algorithms

Covid-19 pandemic has changed daily life in the city of Athens where vaccines are exploited with supply chain technology potentiality. Vaccines are tracked at the city’s airport till their delivery to vaccination centers. Due to the sensitivity of vaccines to the warm climate inherent in the city, delivery is assigned to a fleet of trucks. Specifically, two use cases, i.e., UC-I and UC-II, are proposed, which are based on global and local routing algorithms to exploit trucks’ load COVID-19 vaccine delivery from the airport and transport it to vaccination centers. In this paper, we focus on the supply chain routing algorithm technology of collecting COVID-19 vaccines from the airport and delivering them to vaccination centers in the smart city of Athens, Greece. Concretely, the purpose and the objectives of the research effort are in the areas of: (1) describing in deep detail the proposed supply chain system, (2) exploiting the adopted architecture based on certain separate use cases for system experimentation, (3) adopting specific vaccination routing algorithms to support vaccination distribution, and (4) evaluating experimentally the proposed supply chain system architecture with regards to the adopted use cases’ routing algorithms.

Management of crisis or crisis of management? The Thai government's “policy as discourse” handling of the COVID-19 pandemic

The complexity of crisis management in the context of COVID-19, which ranges from the global to the national, reveals a wide variety of means and ends, particularly at the national level. This triggered scholarly interest and raised questions regarding how central (authoritarian) governments handle the COVID-19 pandemic within state borders. The paper evaluates the Thai government's response to the COVID-19 pandemic from the beginning of 2020 to the middle of 2022, when the pandemic was declared over. We employ critical discourse analysis to explain Thailand's crisis management by engaging discursively with different population groups in the country. The findings indicate that the discursive policies and measures implemented by the government to deviate from and halt public pressure resulting from his mismanagement of vaccine policy are based on narratives related to national traditions as a means of resolving dilemmas rather than on the social needs of vulnerable individual citizens. We witnessed how discursive policies and measures can lead to other problems and ineffective responses, specifically regarding vaccine distribution. The article contributes to a better understanding of how, why, and to what extent discursive policies and measures were instrumentalized by an authoritarian government for COVID-19 crisis management, which can likely be inferred in similar cases in developing nations.

Vaccine effectiveness of COVID-19 and rebound in the real world.

We intend to evaluate the relationship between the rates of global SARS-CoV-2 vaccination and the number of COVID-19 confirmed cases, as well as the mortality rate after the declaration of a pandemic. Of the data from 191 countries at the time of data retraction, we selected 111 countries that have SARS-CoV-2 vaccination reports. We stratified countries into high-income and non-high-income countries (HIC and non-HIC) based on World Bank income-group. We used a fixed-effects model (FEM) and performed a longitudinal analysis. The number of confirmed cases decreased as the vaccination rates increased in both non-HICs (B = - 0.027, T = - 2.0) and HICs (B = - 0.207, T = - 17.5). The number of deaths decreased as the vaccination rates increased in both non-HICs (B = - 0.151, T = - 2.3) and HICs (B = - 0.230, T = - 40.9). For full vaccination, this measure had a negative association with daily confirmed cases and daily deaths in both non-HICs and HICs. In non-HICs, daily cases and daily deaths decreased as the first vaccination and full vaccination coverages increased. However in HICs, daily cases and daily deaths decreased as the first vaccination and full vaccination coverages increased in the early phase, but after a certain period, they tended to increase again. We observed a significant association between the increase in vaccination coverage in the real world and reduced daily confirmed cases and deaths. However, as the confirmed cases and deaths have rebounded in HICs, our findings indicate that COVID-19 is not completely prevented through vaccine distribution.

A Novel Solution to a Novel Pandemic

This paper provides an outline for how Rowan University School of Osteopathic Medicine (RowanSOM) established a student-run coronavirus disease 2019 (COVID-19) vaccination clinic. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV2), referred to as COVID-19, has ravaged the nation since January of 2020. One of the largest issues during the first months of the vaccine rollout was vaccine distribution to the qualified public. RowanSOM sought to address this obstacle in New Jersey (NJ) by implementing the state’s first medical student-led COVID-19 vaccination clinic. Through the establishment of a supply chain, vaccination area, vaccine storage area, and registration center, the medical school created a straightforward format for “mega sites”, interested medical schools and pop-up sites to follow to implement their own clinics to help to end the fight against COVID-19. RowanSOM closely collaborated with the NJ Department of Health, which has been fundamental in the implementation of the State’s vaccination plan through the creation and execution of this student-run vaccination clinic.

Stanford Vax Crew: A Model for Agile, Community-Centered Vaccination Campaigns.

Stanford Vax Crew, one of the largest medical-student-led vaccination programs in the United States, serves as a case study of a successful community-university partnership that adapted its existing operations to enable COVID-19 vaccine distribution. It offers a model for agile, community-centered vaccination campaigns that harness diverse stakeholder strengths to promote vaccine access and uptake in underserved communities. This case study aims to outline the history and structure of the community-university partnership model developed through Stanford Vax Crew, describe key observations of factors that contributed to the scalability of the model, and provide experience-based recommendations for future community-university collaborations.

Examining corruption risks in the procurement and distribution of COVID-19 vaccines in select states in Nigeria

BackgroundPublic health emergencies raise significant concerns about corruption and accountability; however, these concerns can manifest in different ways across diverse locations. For instance, more developed countries with a stronger rule of law may experience more corruption in vaccine procurement, whereas developing countries may experience more corruption at the point of distribution and delivery to end users. This research focuses on corruption concerns in Nigeria, specifically examining the procurement and distribution of COVID-19 vaccines.MethodsThis paper utilizes a scoping review and a qualitative research approach. Key informants (n = 40) involved in the procurement and distribution of COVID-19 vaccines across two states in Nigeria were interviewed. Findings from the scoping review were summarized, and collected data were inductively coded and analysed in themes, revealing clear examples of implementation irregularities and corruption in the country’s COVID-19 vaccination processes.ResultsVaccination programme budgeting processes were unclear, and payment irregularities were frequently observed, resulting in vaccinators soliciting informal payments while in the field. Recruitment and engagement of vaccination personnel was opaque, while target vaccination rates incentivized data falsification during periods of vaccine hesitancy. Accountability mechanisms, such as health worker supervision, vaccination data review, and additional technical support provided by donors were implemented but not effective at preventing corruption among frontline workers.ConclusionsFuture accountability measures should be evidence-driven based on findings from this research. Personnel recruitment, contracting, budgeting, and remuneration should focus on transparency and accountability.

Antigen-loaded flagellate bacteria for enhanced adaptive immune response by intradermal injection

Since the skin limits the distribution of intradermal vaccines, a large number of dendritic cells in the skin cannot be fully utilized to elicit a more effective immune response. Here, we loaded the antigen to the surface of the flagellate bacteria that was modified by cationic polymer, thus creating antigen-loaded flagellate bacteria (denoted as ‘FB-Ag’) to overcome the skin barrier and perform the active delivery of antigen in the skin. The FB-Ag showed fast speed (∼0.2 μm s−1) and strong dendritic cell activation capabilities in the skin model in vitro. In vivo, the FB-Ag promoted the spread of antigen in the skin through active movement, increased the contact between Intradermal dendritic cells and antigen, and effectively activated the internal dendritic cells in the skin. In a mouse of pulmonary metastatic melanoma and in mice bearing subcutaneous melanoma tumor, the FB-Ag effectively increased antigen-specific therapeutic efficacy and produced long-lasting immune memory. More importantly, the FB-Ag also enhanced the level of COVID-19 specific antibodies in the serum and the number of memory B cells in the spleen of mice. The movement of antigen-loaded flagellate bacteria to overcome intradermal constraints may enhance the activation of intradermal dendritic cells, providing new ideas for developing intradermal vaccines.

Geospatial vaccine misinformation risk on social media: Online insights from an English/Spanish natural language processing (NLP) analysis of vaccine-related tweets

BackgroundMisinformation is known to affect norms, attitudes, and intentions to engage with healthy behaviors. Evidence strongly supports that Spanish speakers may be particularly affected by misinformation and its outcomes, yet current insights into the scope and scale of misinformation is primarily ethnocentric, with greater emphasis on English-language design. ObjectiveThis study applies Natural Language Processing (NLP) to analyze a corpus of English/Spanish tweets about vaccines, broadly defined, for misinformation indicators. MethodsWe analyzed NEnglish = 247,140 and NSpanish = 104,445 tweets using Latent Dirichlet Allocation (LDA) topic models with Coherence score calculation (model fit) with a Mallet adjustment (topic optimization). We used informal coding to name computer-identified topics and compare misinformation scope and scale between languages. ResultsThe LDA analysis yielded a 12-topic solution for English and a 14-topic solution for Spanish. Both corpora contained overlapping misinformation, including uncertainty of research guiding policy recommendations or standing in support of antivax movements. However, the Spanish data were positioned in a global context, where misinformation was directed at government equity and disparate vaccine distribution. ConclusionOur findings support that misinformation is a global issue. However, misinformation may vary depending on culture and language. As such, tailored strategies to combat misinformation in digital planes are strongly encouraged.

A Review of the Past, Present, and Future of the Monkeypox Virus: Challenges, Opportunities, and Lessons from COVID-19 for Global Health Security.

Monkeypox, a rare but significant zoonotic and orthopoxviral disease, has garnered increasing attention due to its potential for human-to-human transmission and its recent resurgence in multiple countries throughout Europe, North America, and Oceania. The disease has emerged as a novel threat to the global health systems that are still striving to recover from the major shocks of the COVID-19 pandemic. The unusual manifestation of the illness highlights a substantial knowledge deficit and necessitates the immediate development of a public health action strategy, considering the epidemiological differences observed in the ongoing outbreak and the appearance of cases in non-endemic nations. This literature review aims to synthesize existing knowledge on monkeypox, encompassing its historical context, etiology, epidemiology, surveillance, prevention, transmission, clinical presentation, diagnosis, treatments, and recent outbreak. Particular attention is given to both advances and gaps in our understanding of monkeypox, and we point toward future directions for research and intervention efforts as pertains to vaccine development and distribution. Lastly, we will also review the recent outbreak through a sociopolitical lens as relates to decision-making strategies, especially given the lessons learned from COVID-19.

Need for open data on COVID-19 vaccine uptake among pregnant people in the Caribbean: a call to action.

Pregnant people with coronavirus disease 2019 (COVID-19) have a higher risk of adverse maternal and fetal outcomes compared with pregnant people without COVID-19. In 2021, large increases in maternal mortality were reported in Jamaica, almost half of which were attributable to COVID-19. COVID-19 vaccination has been shown to reduce these risks, but low- and middle-income countries lack free, publicly available data, known as open data, on COVID-19 vaccine uptake for their pregnant populations. The objectives of this paper were to: review how high-income countries use open data to detect trends in COVID-19 vaccine uptake among pregnant people and develop vaccination distribution strategies; outline barriers to making open data available for maternal COVID-19 vaccination in the Caribbean; and propose a multipronged strategy that would increase the availability of open data on maternal COVID-19 vaccination in the Caribbean. A multipronged strategy to fill the data void would involve: (i) utilizing existing Caribbean maternal immunization data collection entities; (ii) adapting digital software tools to establish maternal electronic immunization registries; and (iii) collaborating with local partners skilled in data analytics. Making open data available for COVID-19 vaccine uptake among pregnant people in the Caribbean could offer substantial benefits, including the development of measurable maternal COVID-19 vaccination goals and the facilitation of vaccine decision-making discussions between providers and pregnant people.

Immunity testing against COVID-19 from blood by an IoT-enabled and AI-controlled multiplexed microfluidic platform

Developing herd immunity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is pivotal for changing the course of the coronavirus disease 2019 (COVID-19) pandemic. However, the uncertainty of vaccine-induced immunity development and inequitable distribution of vaccines hinders the global vaccination effort. Therefore, routine serodiagnosis and ensuring effective vaccination on a time-to-time basis are essential for developing sustainable immunity against SARS-CoV-2. Herein, an AI-driven multiplexed point-of-care testing (POCT) platform capable of utilizing a microfluidic lab-on-a-chip (LOC) device has been proposed for analyzing bodily fluid response against SARS-CoV-2. The developed platform has been successfully utilized for the quantification of SARS-CoV-2 S-protein, N-protein, IgM, and IgG from human blood samples with limits of detection (LODs) as low as 0.01, 0.02, 0.69, and 0.61 ng/mL respectively. Finally, a data-receptive web-based dashboard system has been developed and demonstrated to provide real-time, territory-specific analysis of herd immunity progress from the test results. Thus, the proposed platform could be an imperative tool for healthcare authorities to analyze and restrain ongoing COVID-19 outbreaks or similar pandemics in the future by ensuring effective immunization.

The impact of national income and vaccine hesitancy on country-level COVID-19 vaccine uptake.

The rapid development and rollout of COVID-19 vaccines helped reduce the pandemic's mortality burden. The vaccine rollout, however, has been uneven; it is well known that vaccination rates tend to be lower in lower income countries. Vaccine uptake, however, ultimately depends on the willingness of individuals to get vaccinated. If vaccine confidence is low, then uptake will be low, regardless of country income level. We investigated the impact on country-level COVID-19 vaccination rates of both national income and vaccine hesitancy. We estimated a linear regression model of COVID-19 vaccine uptake across 145 countries; this cross-sectional model was estimated at each of four time points: 6, 12, 18, and 24 months after the onset of global vaccine distribution. Vaccine uptake reflects the percentage of the population that had completed their primary vaccination series at the time point. Covariates include per capita GDP, an estimate of the percentage of country residents who strongly disagreed that vaccines are safe, and a variety of control variables. Next, we estimated these models of vaccine uptake by country income (countries below, and above the international median per capita GDP) to examine whether the impact of vaccine hesitancy varies by country income. We find that GDP per capita has a pronounced impact on vaccine uptake at 6 months after global rollout. After controlling for other factors, there was a 22 percentage point difference in vaccination rates between the top 20% and the bottom 20% of countries ranked by per capita GDP; this difference grew to 38% by 12 months. The deleterious impact of distrust of vaccine safety on vaccine uptake became apparent by 12 months and then increased over time. At 24 months, there was a 17% difference in vaccination rates between the top 20% and the bottom 20% of countries ranked by distrust. The income stratified models reveal that the deleterious impact of vaccine distrust on vaccine uptake at 12 and 24 months is particularly evident in lower income countries. Our study highlights the important role of both national income and vaccine hesitancy in determining COVID-19 vaccine uptake globally. There is a need to increase the supply and distribution of pandemic vaccines to lower-income countries, and to take measures to improve vaccine confidence in these countries.

Epidemic Vulnerability Index for Effective Vaccine Distribution Against Pandemic.

COVID-19 vaccine distribution route directly impacts the community's mortality and infection rate. Therefore, optimal vaccination dissemination would appreciably lower the death and infection rates. This paper proposes the Epidemic Vulnerability Index (EVI) that quantitatively evaluates the subject's potential risk. Our primary aim for the suggested index is to diminish both infection rate and death rate efficiently. EVI was accordingly designed with clinical factors determining the mortality and social factors incorporating the infection rate. Through statistical COVID-19 patient dataset analysis and social network analysis with an agent-based model that is analogous to a real-world system, we define and experimentally validate the capability of EVI. Our experiments consist of nine vaccination distribution scenarios, including existing indexes which estimate the risk and stochastically proliferate the contagion and vaccine in a 300,000 agent-based graph network. We compared the outcome and variation of the three metrics in the experiments: infection case, death case, and death rate. Through this assessment, vaccination by the descending order of EVI has shown to have a significant outcome with an average of 5.0% lower infection cases, 9.4% lower death cases, and 3.5% lower death rate than other vaccine distribution routes.

What are effective vaccine distribution approaches for equity-deserving and high-risk populations during COVID-19? Exploring best practices and recommendations in Canada: protocol for a mixed-methods multiple case codesign study

IntroductionThe WHO has stated that vaccine hesitancy is a serious threat to overcoming COVID-19. Vaccine hesitancy among underserved and at-risk communities is an ongoing challenge in Canada. Public confidence in...

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.

Case Study: Role of Supply Chain & Transportation in Food and Healthcare

This case study delves into the indispensable roles of supply chain and transportation networks in the pivotal sectors of food and healthcare. These domains are not only vital for our well-being but also profoundly impacted by dynamic market forces, evolving consumer preferences, and global events. In the food sector, the journey from field to fork is a complex web of growers, manufacturers, distributors, and retailers. It must strike a delicate balance between delivering a diverse range of products while ensuring safety, freshness, and sustainability. Conversely, the healthcare sector demands an entirely different level of precision. The timely availability of life-saving medicines, medical equipment, and personal protective gear is non-negotiable. The efficiency of healthcare supply chains can be a matter of life and death. Recent challenges, from pandemics to vaccine distribution, have illuminated the need for agile healthcare supply chains and robust transportation networks. This case study will explore the critical challenges faced by these sectors, innovative solutions that have been devised to address them, and the profound lessons learned in the pursuit of building more resilient, efficient, and responsive supply chain and transportation systems.

Comparative Analysis: AI-Assisted Vaccine Distribution During COVID-19 Pandemic

Comparative Analysis: AI-Assisted Vaccine Distribution During COVID-19 Pandemic