Vaccine Allocation Research Articles

More Pain, More Gain! The Delivery of COVID-19 Vaccines and the Pharmaceutical Industry's Role in Widening the Access Gap.

An effective response to the coronavirus disease 2019 (COVID-19) pandemic entails a comprehensive strategy that ensures equitable access to all COVID-19-fighting technologies. To achieve this goal, the international community has acknowledged immunization as a public good. However, a trend of grossly unequal dose distribution emerged, owing, among other factors, to pharmaceutical companies' profit-driven actions, jeopardizing the mechanisms built to increase vaccine access. The contradiction between public health interests and corporate discretion in determining vaccine dose distribution poses critical concerns about the health risks associated with lengthening the duration of the pandemic and the eventual liability of companies for violations of human rights. To evaluate the risks posed to the COVID-19 immunization program, data on vaccine allocation and delivery, vaccine dose application, immunized populations, and the volume of Advanced Purchase Agreements (APAs) between countries and pharmaceutical companies were compiled and assessed. A descriptive analysis was then conducted to analyze the role of pharmaceutical companies in providing equitable access to COVID-19 vaccines. When the data is broken down by income (as of June 2021), it shows that high-income countries (HICs) have already crossed the COVID-19 Vaccine Global Access (COVAX) 20% immunization threshold. However, countries of all other income levels have yet to achieve this mark for fully vaccinated people. Upper-middle-income countries (UMICs) have approximately 3%, low- and middle-income countries (LMICs) have approximately 2% and low-income countries (LICs) have less than 0.1% of fully vaccinated people per hundred. The supply shortage is expected to last until the second half of 2021. As a result of the COVAX failure, a health gap emerged with countries living in a pre-immunization period for an extended time. The existing conflict between the international response to tackle COVID-19 and corporate profit-driven behavior contributed to prolonging pandemic, especially in Africa. Accordingly, there is a need to approve an international treaty that targets the activities of all actors, including the pharmaceutical companies, in protecting human rights and the right to health realms.

A mathematical model for equitable in-country COVID-19 vaccine allocation

Given the scarcity of COVID-19 vaccines, equitable (fair) allocation of limited vaccines across the main administrative units of a country (e.g. municipalities) has been an important concern for public health authorities worldwide. In this study, we address the equitable allocation of the COVID-19 vaccines inside countries by developing a novel, evidence-based mathematical model that accounts for multiple priority groups (e.g. elderly, healthcare workers), multiple vaccine types, and regional characteristics (e.g. storage capacities, infection risk levels). Our research contributes to the literature by developing and validating a model that proposes equitable vaccine allocation alternatives in a very short time by (a) minimising deviations from the so-called ‘fair coverage’ levels that are computed based on weighted pro-rata rations, and (b) imposing minimum coverage thresholds to control the allocation of vaccines to higher priority groups and regions. To describe the merits of our model, we provide several equity and effectiveness metrics, and present insights on different allocation policies. We compare our methodology with similar models in the literature and show its better performance in achieving equity. To illustrate the performance of our model in practice, we perform a comprehensive numerical study based on actual data corresponding to the early vaccination period in Turkey.

The Race for Global Equitable Access to COVID-19 Vaccines.

COVID-19 vaccines are possibly the most effective medical countermeasures to mitigate and ultimately bring to a halt the COVID-19 pandemic. As we transition to endemicity, inequitable access to vaccines, and particularly in low- and middle-income countries (LMICs), still poses risks of unprecedented disruptions and the emergence of viral mutations, which potentially lead to notorious vaccine-resistant variants. The missteps learned from the previous responses to the human immunodeficiency virus (HIV) and influenza outbreaks founded the hypothetical plan to ensure that vaccine accessibility to LMICs is not impeded. The SARS-CoV-2 vaccines’ social promise was to lessen the underlying racial, ethnic, and geographic inequities that COVID-19 has both made apparent and intensified. Vaccine nationalism was evident throughout the COVID-19 crisis. Many high-income countries directly negotiated large advance orders for the vaccines, leaving resource-limited countries scrambling for access. This occurred despite international initiatives to structure the development and equitable distribution of vaccines, channeled through a vaccine pillar: COVID-19 Vaccines Global Access (COVAX). The serious supply shortages and national procurement methods of some countries that bypassed the vaccine pillar hindered the optimal function of COVAX in delivering timely and adequate doses to participating countries. COVAX strategized its approach by promoting fundraising, coordinating vaccine donations from countries with surplus doses, expediting reviews of vaccine candidates, and facilitating the expansion of the manufacturing capacity. While increasing capacity for production, technology transfer led to lesser siloes, enhanced manufacturing standardization, and less secrecy over production data. Ultracold storage requirements for leading vaccines were a considerable hurdle to the global immunization efforts, and particularly in LMICs with limited equipment and resources to support sophisticated cold-chain systems. Manufacturers strived to ease cold-chain restrictions on the basis of stability data submitted to national regulatory bodies. The development of single-dose vaccines offered promising solutions to simplify the administrative and logistic complexities that existed within the COVID-19 vaccination programs. As such, the requirements for both ultracold storage conditions were eased, and concerns over booster doses were addressed. To expand coverage, the dosing intervals of the Oxford/AstraZeneca vaccines were extended according to data from Phase III clinical trials on effectiveness. In addition, with the recent outbreak of monkeypox, the lessons from past experiences of curbing infectious diseases, including COVID-19, must be learned and acted upon. The review summarizes the global efforts with respect to vaccine development, production, allocation, and deployment to achieve equitable access.

Which countries need COVID-19 vaccines the most? Development of a prioritisation tool

BackgroundThe COVID-19 pandemic and associated non-pharmaceutical interventions (NPIs) have affected all countries. With a scarcity of COVID-19 vaccines there has been a need to prioritize populations, but assessing relative needs has been challenging. The COVAX Facility allocates vaccines to cover 20% of each national population, followed by a needs assessment that considers five quantitative metrics alongside a qualitative assessment. The objective of this study was to identify the most important factors for assessing countries’ needs for vaccines, and to weight each, generating a scoring tool for prioritising countries.MethodsThe study was conducted between March and November 2021. The first stage involved an online Delphi survey with a purposive and snowball sample of public health experts, to reach consensus on country-level factors for assessing relative needs for COVID-19 vaccines. The second stage involved a discrete choice experiment (DCE) to determine weights for the most important factors.ResultsResponses were received from 28 experts working across 13 different countries and globally. The most common job titles reported were director and professor, with most based in national public health institutes (n = 9) and universities (n = 8). The Delphi survey found 37 distinct factors related to needs. Nine of the most important factors were included in the DCE. Among these, the most important factor was the ‘proportion of overall population not fully vaccinated’ (with a mean weight of 19.5), followed by ‘proportion of high-risk population not fully vaccinated’ (16.1), ‘health system capacity’ (14.2), ‘capacity to purchase vaccines’ (11.9) and the ‘proportion of the population clinically vulnerable’ (11.3).ConclusionsSeveral factors exist, extending beyond those currently used, which may lead to some countries having a greater need for vaccines compared to others. By assessing relative needs, this scoring tool can build on existing methods to further the role of equity in global COVID-19 vaccine allocation.

A new vaccine supply chain network under COVID-19 conditions considering system dynamic: Artificial intelligence algorithms

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.

Public attitudes about equitable COVID-19 vaccine allocation: a randomised experiment of race-based versus novel place-based frames

Equity was—and is—central in the US policy response to COVID-19, given its disproportionate impact on disadvantaged communities of colour. In an unprecedented turn, the majority of US states used place-based...

COVID-19 Vaccine Hesitancy Among Health Care Workers in Thailand: The Comparative Results of Two Cross-Sectional Online Surveys Before and After Vaccine Availability.

IntroductionThe arrival of COVID-19 vaccines in Thailand has supported the fight against the COVID-19 pandemic. This study examined COVID-19 vaccine acceptance among health care workers (HCWs) in Thailand before and after vaccines' availability and investigated factors (both enablers and barriers) affecting their decisions.MethodsTwo online self-administered questionnaires were distributed to HCWs in two time-periods: (1) the pre-vaccine arrival period (prior to COVID-19 vaccines' arrival in Thailand, January 28 to February 16, 2021); and (2) the post-vaccine arrival period (April 21 to May 9, 2021). Descriptive analyses and multinomial logistic regression were conducted to examine factors associated with vaccine hesitancy.ResultsThere were 55,068 respondents in the pre-vaccine arrival period and 27,319 respondents in the post-vaccine arrival period. In the pre-vaccine arrival period, 55.0% of respondents were willing to accept the vaccines, 35.4% were uncertain, and 9.6% declined. In the post-vaccine arrival period, ~16% already received two doses of either the Sinovac or AstraZeneca vaccine, and 43% were administered one dose. Approximately 12% of those who had received the first dose were uncertain or not willing to accept the second dose. Demographic and socio-demographic factors of participants, including their sex, place of residence, and whether they were frontline COVID-19 workers, were found to be the significant factors explaining vaccination hesitancy. Moreover, when comparing the pre-vaccine arrival and post-vaccine arrival periods, it was found that older HCWs were more likely to decline a COVID-19 vaccine in the pre-vaccine arrival period; on the other hand, older HCWs were less likely to decline or be uncertain to receive a COVID-19 vaccine in the post-vaccine arrival period.ConclusionInformation on HCWs' acceptance of COVID-19 vaccines, including who is more likely to accept the vaccines, could assist in planning vaccine allocation to both HCWs and the general public, who often believe HCWs' recommendations. This study's findings set out how policies can be addressed to reduce vaccine hesitancy. This study also highlights HCWs' characteristics (including gender, work region, occupation, and history of receiving influenza vaccination) and the reasons they cited for their vaccine acceptance or hesitance.

Social equity-based distribution networks design for the COVID-19 vaccine

This study aims to investigate the role of social equity in vaccine distribution network design problems. Inspired by the current COVID-19 vaccine allocation in-country context, we capture social equity-based distribution by modeling three theories: Rawls’ theory, Sadr’s theory, and utilitarianism. We consider various social groups based on degree of urbanization, including inhabitants of cities, towns and suburbs, and rural areas. The distribution problem is subject to, on the one hand, demand-side uncertainty characterized by the daily contamination rate and its space–time propagation that anticipate the in-need population. On the other hand, supply-side uncertainty characterized by the stochastic arrival of vaccine doses for the supply period. To tackle this problem, we propose a novel bi-objective two-stage stochastic programming model using the sample average approximation (SAA) method. We also develop a lexicographic goal programming approach where the social equity objective is prioritized, thereafter reaching an efficiency level. Using publicly available data on COVID-19 in-country propagation and the case of two major provinces in Iran as example of middle-income country, we provide evidence of the benefits of considering social equity in a model-based decision-making approach. The findings suggest that the design solution produced by each social equity theory matches its essence in social science, differing considerably from the cost-based design solution. According to the general results, we can infer that each social equity theory has its own merits. Implementing Rawls’ theory brings about a greater coverage percentage in rural areas, while utilitarianism results in a higher allocation of vaccine doses to social groups compared to the Sadr and Rawls theories. Finally, Sadr’s theory outperforms Rawls’ in terms of both the allocation and cost perspective. These insights would help decision-makers leverage the right equity approach in the COVID-19 vaccine context, and be better prepared for any pandemic crisis that the future may unfold.

Development of China's health care system in the time of COVID‐19: Challenges, opportunities, and call for actions

Development of China's health care system in the time of COVID‐19: Challenges, opportunities, and call for actions

COVID-19 vaccination policies under uncertain transmission characteristics using stochastic programming.

We develop a new stochastic programming methodology for determining optimal vaccination policies for a multi-community heterogeneous population. An optimal policy provides the minimum number of vaccinations required to drive post-vaccination reproduction number to below one at a desired reliability level. To generate a vaccination policy, the new method considers the uncertainty in COVID-19 related parameters such as efficacy of vaccines, age-related variation in susceptibility and infectivity to SARS-CoV-2, distribution of household composition in a community, and variation in human interactions. We report on a computational study of the new methodology on a set of neighboring U.S. counties to generate vaccination policies based on vaccine availability. The results show that to control outbreaks at least a certain percentage of the population should be vaccinated in each community based on pre-determined reliability levels. The study also reveals the vaccine sharing capability of the proposed approach among counties under limited vaccine availability. This work contributes a decision-making tool to aid public health agencies worldwide in the allocation of limited vaccines under uncertainty towards controlling epidemics through vaccinations.

Covid 19 vaccine order allocation: an optimization model with substitution

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.

Sequential allocation of vaccine to control an infectious disease

The problem of optimally allocating a limited supply of vaccine to control a communicable disease has broad applications in public health and has received renewed attention during the COVID-19 pandemic. This allocation problem is highly complex and nonlinear. Decision makers need a practical, accurate, and interpretable method to guide vaccine allocation. In this paper we develop simple analytical conditions that can guide the allocation of vaccines over time. We consider four objectives: minimize new infections, minimize deaths, minimize life years lost, or minimize quality-adjusted life years lost due to death. We consider an SIR model with interacting population groups. We approximate the model using Taylor series expansions, and develop simple analytical conditions characterizing the optimal solution to the resulting problem for a single time period. We develop a solution approach in which we allocate vaccines using the analytical conditions in each time period based on the state of the epidemic at the start of the time period. We illustrate our method with an example of COVID-19 vaccination, calibrated to epidemic data from New York State. Using numerical simulations, we show that our method achieves near-optimal results over a wide range of vaccination scenarios. Our method provides a practical, intuitive, and accurate tool for decision makers as they allocate limited vaccines over time, and highlights the need for more interpretable models over complicated black box models to aid in decision making.

An agent-based model to assess large-scale COVID-19 vaccination campaigns for the Italian territory: The case study of Lombardy region

BackgroundIn Italy, the administration of COVID-19 vaccines began in late 2020. In the early stages, the number of available doses was limited. To maximize the effectiveness of the vaccine campaign, the national health agency assigned priority access to at-risk individuals, such as health care workers and the elderly. Current vaccination campaign strategies do not take full advantage of the latest mathematical models, which capture many subtle nuances, allowing different territorial situations to be analyzed aiming to make context-specific decisions. ObjectivesThe main objective is the definition of an agent-based model using open data and scientific literature to assess and optimize the impact of vaccine campaigns for an Italian region. Specifically, the aim is twofold: (i) estimate the reduction in the number of infections and deaths attributable to vaccines, and (ii) assess the performances of alternative vaccine allocation strategies. MethodsThe COVID-19 Agent-based simulator Covasim has been employed to build an agent-based model by considering the Lombardy region as case study. The model has been tailored by leveraging open data and knowledge from the scientific literature. Dynamic mobility restrictions and the presence of Variant of Concern have been explicitly represented. Free parameters have been calibrated using the grid search methodology. ResultsThe model mimics the COVID-19 wave that hit Lombardy from September 2020 to April 2021. It suggests that 168,492 cumulative infections 2,990 cumulative deaths have been avoided due to the vaccination campaign in Lombardy from January 1 to April 30, 2021. Without vaccines, the number of deaths would have been 66% greater in the 80–89 age group and 114% greater for those over 90. The best vaccine allocation strategy depends on the goal. To minimize infections, the best policy is related to dose availability. If at least 1/3 of the population can be covered in 4 months, targeting at-risk individuals and the elderly first is recommended; otherwise, the youngest people should be vaccinated first. To minimize overall deaths, priority is best given to at-risk groups and the elderly in all scenarios. ConclusionsThis work proposes a methodological approach that leverages open data and scientific literature to build a model of COVID-19 capable of assessing and optimizing the impact of vaccine campaigns. This methodology can help national institutions to design regional mathematical models that can support pandemic-related decision-making processes.

Optimal control of the spatial allocation of COVID-19 vaccines: Italy as a case study.

While campaigns of vaccination against SARS-CoV-2 are underway across the world, communities face the challenge of a fair and effective distribution of a limited supply of doses. Current vaccine allocation strategies are based on criteria such as age or risk. In the light of strong spatial heterogeneities in disease history and transmission, we explore spatial allocation strategies as a complement to existing approaches. Given the practical constraints and complex epidemiological dynamics, designing effective vaccination strategies at a country scale is an intricate task. We propose a novel optimal control framework to derive the best possible vaccine allocation for given disease transmission projections and constraints on vaccine supply and distribution logistics. As a proof-of-concept, we couple our framework with an existing spatially explicit compartmental COVID-19 model tailored to the Italian geographic and epidemiological context. We optimize the vaccine allocation on scenarios of unfolding disease transmission across the 107 provinces of Italy, from January to April 2021. For each scenario, the optimal solution significantly outperforms alternative strategies that prioritize provinces based on incidence, population distribution, or prevalence of susceptibles. Our results suggest that the complex interplay between the mobility network and the spatial heterogeneities implies highly non-trivial prioritization strategies for effective vaccination campaigns. Our work demonstrates the potential of optimal control for complex and heterogeneous epidemiological landscapes at country, and possibly global, scales.

Cluster Analysis of US COVID-19 Infected States for Vaccine Distribution.

Since December 2019, COVID-19 has been raging worldwide. To prevent the spread of COVID-19 infection, many countries have proposed epidemic prevention policies and quickly administered vaccines, However, under facing a shortage of vaccines, the United States did not put forward effective epidemic prevention policies in time to prevent the infection from expanding, resulting in the epidemic in the United States becoming more and more serious. Through “The COVID Tracking Project”, this study collects medical indicators for each state in the United States from 2020 to 2021, and through feature selection, each state is clustered according to the epidemic’s severity. Furthermore, through the confusion matrix of the classifier to verify the accuracy of the cluster analysis, the study results show that the Cascade K-means cluster analysis has the highest accuracy. This study also labeled the three clusters of the cluster analysis results as high, medium, and low infection levels. Policymakers could more objectively decide which states should prioritize vaccine allocation in a vaccine shortage to prevent the epidemic from continuing to expand. It is hoped that if there is a similar epidemic in the future, relevant policymakers can use the analysis procedure of this study to determine the allocation of relevant medical resources for epidemic prevention according to the severity of infection in each state to prevent the spread of infection.

Precancerous cervical lesions caused by non-vaccine-preventable HPV types after vaccination with the bivalent AS04-adjuvanted HPV vaccine: an analysis of the long-term follow-up study from the randomised Costa Rica HPV Vaccine Trial.

In women vaccinated against human papillomavirus (HPV), reductions in cervical disease and related procedures results in more women having intact transformation zones, potentially increasing the risk of cervical lesions caused by non-vaccine-preventable HPV types, a phenomenon termed clinical unmasking. We aimed to evaluate HPV vaccine efficacy against cervical intraepithelial neoplasia grade 2 or worse (CIN2+) and cervical intraepithelial neoplasia grade 3 or worse (CIN3+) attributed to non-preventable HPV types in the long-term follow-up phase of the Costa Rica HPV Vaccine Trial (CVT). CVT was a randomised, double-blind, community-based trial done in Costa Rica. Eligible participants were women aged 18-25 years who were in general good health. Participants were randomly assigned (1:1) to receive an HPV 16 and 18 AS04-adjuvanted vaccine or control hepatitis A vaccine, using a blocked randomisation method (permuted block sizes of 14, 16, and 18). Vaccines in both groups were administered intramuscularly with 0·5 mL doses at 0, 1, and 6 months. Masking of vaccine allocation was maintained throughout the 4-year randomised trial phase, after which participants in the hepatitis A virus vaccine control group were provided the HPV vaccine and exited the study; a screening-only, unvaccinated control group was enrolled. The unvaccinated control group and HPV vaccine group were followed up for 7 years, during which treatment allocation was not masked. One of the prespecified primary endpoints for the long-term follow-up phase was precancers associated with HPV types not prevented by the vaccine, defined as histologically confirmed incident CIN2+ events or CIN3+ events attributed to any HPV type except HPV 16, 18, 31, 33, and 45. Our primary analytical period was years 7-11. Primary analyses were in all participants with at least one follow-up visit and excluded participants with a previous endpoint (ie, modified intention-to-treat cohort). Safety endpoints have been reported elsewhere. This trial is registered with ClinicalTrials.gov, NCT00128661 and NCT00867464. The randomised, masked trial phase is completed; an unmasked subset of women in the HPV-vaccinated group is under active investigation. Between June 28, 2004, and Dec 21, 2005, 7466 participants were enrolled (HPV vaccine group n=3727 and hepatitis A virus vaccine control group n=3739). Between March 30, 2009, and July 5, 2012, 2836 women enrolled in the new unvaccinated control group. The primary analytical cohort (years 7 to 11) included 2767 participants in the HPV vaccine group and 2563 in the unvaccinated group for the CIN2+ events endpoint assessment and 2826 participants in the HPV vaccine group and 2592 in the unvaccinated control group for the CIN3+ events endpoint assessment. Median follow-up during years 7 to 11 for women included for the CIN2+ events analysis was 52·8 months (IQR 44·0 to 60·7) for the HPV vaccine group and 49·8 months (42·0 to 56·9) for the unvaccinated control group. During years 7 to 11, clinical unmasking was observed with a negative vaccine efficacy against CIN2+ events attributed to non-preventable HPV types (-71·2% [95% CI -164·0 to -12·5]), with 9·2 (95% CI 2·1 to 15·6) additional CIN2+ events attributed to non-preventable HPV types per 1000 HPV-vaccinated participants versus HPV-unvaccinated participants. 27·0 (95% CI 14·2 to 39·9) fewer CIN2+ events irrespective of HPV type per 1000 vaccinated participants were observed during 11 years of follow-up. Vaccine efficacy against CIN3+ events attributed to non-preventable HPV types during years 7 to 11 was -135·0% (95% CI -329·8 to -33·5), with 8·3 (3·0 to 12·8) additional CIN3+ events attributed to non-preventable HPV types per 1000 vaccinated participants versus unvaccinated participants. Higher rates of CIN2+ events and CIN3+ events due to non-preventable HPV types in vaccinated versus unvaccinated participants suggests clinical unmasking could attenuate long-term reductions in high-grade disease following successful implementation of HPV vaccination programmes in screened populations. Importantly, the net benefit of vaccination remains considerable; therefore, HPV vaccination should still be prioritised as primary prevention for cervical cancer. National Cancer Institute and National Institutes of Health Office of Research on Women's Health. For the Spanish translation of the abstract see Supplementary Materials section.

An alternative vaccine prioritization approach in response to COVID-19 pandemic

PurposeThe purpose of this study is to evaluate the impact of food access and other vulnerability measures on the COVID-19 progression to inform the public health decision-makers while setting priority rules for vaccine schedules.Design/methodology/approachIn this paper, the authors used the Supplemental Nutrition Assistance Program (SNAP) data combined with the Centers for Disease Control and Prevention (CDC)’s social vulnerability score variables and diabetes and obesity prevalence in a set of models to assess the associations with the COVID-19 prevalence and case-fatality rates in the United States (US) counties. Using the case prevalence estimates provided by these models, the authors developed a COVID-19 vulnerability score. The COVID-19 vulnerability score prioritization is then compared with the pro-rata approach commonly used for vaccine distribution.FindingsThe study found that the population proportion residing in a food desert is positively correlated with the COVID-19 prevalence. Similarly, the population proportion registered to SNAP is positively correlated with the COVID-19 prevalence. The findings demonstrate that commonly used pro-rata vaccine allocation can overlook vulnerable communities, which can eventually create disease hot-spots.Practical implicationsThe proposed methodology provides a rapid and effective vaccine prioritization scoring. However, this scoring can also be considered for other humanitarian programs such as food aid and rapid test distribution in response to the current and future pandemics.Originality/valueHumanitarian logistics domain predominantly relies on equity measures, where each jurisdiction receives resources proportional to their population. This study provides a tool to rapidly identify and prioritize vulnerable communities while determining vaccination schedules.

Leveraging Black pharmacists to promote equity in COVID-19 vaccine uptake within Black communities: A framework for researchers and clinicians.

Black Americans are disproportionately represented among coronavirus disease 2019 (COVID‐19)‐related morbidities and mortalities. While the COVID‐19 vaccines are positioned to change this disparity, vaccine hesitancy, attributed to decades of systemic racism and mistreatment by the United States health care system, heavily exists among this racially and ethnically minoritized group. In addition, social determinants of health within Black communities including the lack of health care access and inequitable COVID‐19 vaccine allocation, further impacts vaccine uptake. Black pharmacists have worked to address the pandemic's deleterious effects that have been recognized within Black communities, as they are intimately aware of the structural and systematic limitations that contribute to lower vaccination rates in comparison to other racial and ethnic groups. Black pharmacists have been integral to promoting equity in COVID‐19 uptake within Black communities by disseminating factual, trustworthy information in collaboration with community leaders, advocating for the equitable access to the immunizations into vulnerable areas, and creating, low‐barrier, options to distribute the vaccines. Herein, we thoroughly explain these points and offer a framework that describes the role of Black pharmacists in narrowing vaccine equity gaps.

Optimizing Spatio-Temporal Allocation of the COVID-19 Vaccine Under Different Epidemiological Landscapes.

An efficient and safe vaccine is expected to allow people to return to normal life as soon as possible. However, vaccines for new diseases are likely to be in short supply during the initial deployment due to narrow production capacity and logistics. There is an urgent need to optimize the allocation of limited vaccines to improve the population effectiveness of vaccination. Existing studies mostly address a single epidemiological landscape. The robustness of the effectiveness of other proposed strategies is difficult to guarantee under other landscapes. In this study, a novel vaccination allocation model based on spatio-temporal heterogeneity of epidemiological landscapes is proposed. This model was combined with optimization algorithms to determine the near-optimal spatio-temporal allocation for vaccines with different effectiveness and coverage. We fully simulated the epidemiological landscapes during vaccination, and then minimized objective functions independently under various epidemiological landscapes and degrees of viral transmission. We find that if all subregions are in the middle or late stages of the pandemic, the difference between the effectiveness of the near-optimal and pro-rata strategies is very small in most cases. In contrast, under other epidemiological landscapes, when minimizing deaths, the optimizer tends to allocate the remaining doses to sub-regions with relatively higher risk and expected coverage after covering the elderly. While to minimize symptomatic infections, allocating vaccines first to the higher-risk sub-regions is near-optimal. This means that the pro-rata allocation is a good option when the subregions are all in the middle to late stages of the pandemic. Moreover, we suggest that if all subregions are in the period of rapid virus transmission, vaccines should be administered to older adults in all subregions simultaneously, while when the epidemiological dynamics of the subregions are significantly different, priority can be given to older adults in subregions that are still in the early stages of the pandemic. After covering the elderly in the region, high-risk sub-regions can be prioritized.

Assessing Vaccination Prioritization Strategies for COVID-19 in South Africa Based on Age-Specific Compartment Model

The vaccines are considered to be important for the prevention and control of coronavirus disease 2019 (COVID-19). However, considering the limited vaccine supply within an extended period of time in many countries where COVID-19 vaccine booster shot are taken and new vaccines are developed to suppress the mutation of virus, designing an effective vaccination strategy is extremely important to reduce the number of deaths and infections. Then, the simulations were implemented to study the relative reduction in morbidity and mortality of vaccine allocation strategies by using the proposed model and actual South Africa's epidemiological data. Our results indicated that in light of South Africa's demographics, vaccinating older age groups (>60 years) largely reduced the cumulative deaths and the “0–20 first” strategy was the most effective way to reduce confirmed cases. In addition, “21–30 first” and “31–40 first” strategies have also had a positive effect. Partial vaccination resulted in lower numbers of infections and deaths under different control measures compared with full vaccination in low-income countries. In addition, we analyzed the sensitivity of daily testing volume and infection rate, which are critical to optimize vaccine allocation. However, comprehensive reduction in infections was mainly affected by the vaccine proportion of the target age group. An increase in the proportion of vaccines given priority to “0–20” groups always had a favorable effect, and the prioritizing vaccine allocation among the “60+” age group with 60% of the total amount of vaccine consistently resulted in the greatest reduction in deaths. Meanwhile, we observed a significant distinction in the effect of COVID-19 vaccine allocation policies under varying priority strategies on relative reductions in the effective reproduction number. Our results could help evaluate to control measures performance and the improvement of vaccine allocation strategy for COVID-19 epidemic.