Allocation Of COVID-19 Vaccines Research Articles

California's COVID-19 Vaccine Equity Policy: Cases, Hospitalizations, And Deaths Averted In Affected Communities.

In March2021, California implemented a vaccine equity policy that prioritized COVID-19 vaccine allocation to communities identified as least advantaged by an area-based socioeconomic measure, the Healthy Places Index. We conducted quasi-experimental and counterfactual analyses to estimate the effect of this policy on COVID-19 vaccination, case, hospitalization, and death rates. Among prioritized communities, vaccination rates increased 28.4percent after policy implementation. Furthermore, an estimated 160,892 COVID-19 cases, 10,248 hospitalizations, and 679 deaths in the least-advantaged communities were averted by the policy. Despite these improvements, the share of COVID-19 cases, hospitalizations, and deaths in prioritized communities remained elevated. These estimates were robust in sensitivity analyses that tested exchangeability between prioritized communities and those not prioritized by the policy; model specifications; and potential temporal confounders, including prior infections. Correcting for disparities by strategically allocating limited resources to the least-advantaged or most-affected communities can reduce the impacts of COVID-19 and other diseases but might not eliminate health disparities.

Trends in SARS-CoV-2 seroprevalence among pregnant women attending first antenatal care visits in Zambia: A repeated cross-sectional survey, 2021-2022.

SARS-CoV-2 serosurveys help estimate the extent of transmission and guide the allocation of COVID-19 vaccines. We measured SARS-CoV-2 seroprevalence among women attending ANC clinics to assess exposure trends over time in Zambia. We conducted repeated cross-sectional SARS-CoV-2 seroprevalence surveys among pregnant women aged 15-49 years attending their first ANC visits in four districts of Zambia (two urban and two rural) during September 2021-September 2022. Serologic testing was done using a multiplex bead assay which detects IgG antibodies to the nucleocapsid protein and the spike protein receptor-binding domain (RBD). We calculated monthly SARS-CoV-2 seroprevalence by district. We also categorized seropositive results as infection alone, infection and vaccination, or vaccination alone based on anti-RBD and anti-nucleocapsid test results and self-reported COVID-19 vaccination status (vaccinated was having received ≥1 dose). Among 8,304 participants, 5,296 (63.8%) were cumulatively seropositive for SARS-CoV-2 antibodies from September 2021 through September 2022. SARS-CoV-2 seroprevalence primarily increased from September 2021 to September 2022 in three districts (Lusaka: 61.8-100.0%, Chongwe: 39.6-94.7%, Chipata: 56.5-95.0%), but in Chadiza, seroprevalence increased from 27.8% in September 2021 to 77.2% in April 2022 before gradually dropping to 56.6% in July 2022. Among 5,906 participants with a valid COVID-19 vaccination status, infection alone accounted for antibody responses in 77.7% (4,590) of participants. Most women attending ANC had evidence of prior SARS-CoV-2 infection and most SARS-CoV-2 seropositivity was infection-induced. Capturing COVID-19 vaccination status and using a multiplex bead assay with anti-nucleocapsid and anti-RBD targets facilitated distinguishing infection-induced versus vaccine-induced antibody responses during a period of increasing COVID-19 vaccine coverage in Zambia. Declining seroprevalence in Chadiza may indicate waning antibodies and a need for booster vaccines. ANC clinics have a potential role in ongoing SARS-CoV-2 serosurveillance and can continue to provide insights into SARS-CoV-2 antibody dynamics to inform near real-time public health responses.

Covid19Vaxplorer: A free, online, user-friendly COVID-19 vaccine allocation comparison tool.

There are many COVID-19 vaccines currently available, however, Low- and middle-income countries (LMIC) still have large proportions of their populations unvaccinated. Decision-makers must decide how to effectively allocate available vaccines (e.g. boosters or primary series vaccination, which age groups to target) but LMIC often lack the resources to undergo quantitative analyses of vaccine allocation, resulting in ad-hoc policies. We developed Covid19Vaxplorer (https://covid19vaxplorer.fredhutch.org/), a free, user-friendly online tool that simulates region-specific COVID-19 epidemics in conjunction with vaccination with the purpose of providing public health officials worldwide with a tool for vaccine allocation planning and comparison. We developed an age-structured mathematical model of SARS-CoV-2 transmission and COVID-19 vaccination. The model considers vaccination with up to three different vaccine products, primary series and boosters. We simulated partial immunity derived from waning of natural infection and vaccination. The model is embedded in an online tool, Covid19Vaxplorer that was optimized for its ease of use. By prompting users to fill information through several windows to input local parameters (e.g. cumulative and current prevalence), epidemiological parameters (e.g basic reproduction number, current social distancing interventions), vaccine parameters (e.g. vaccine efficacy, duration of immunity) and vaccine allocation (both by age groups and by vaccination status). Covid19Vaxplorer connects the user to the mathematical model and simulates, in real time, region-specific epidemics. The tool then produces key outcomes including expected numbers of deaths, hospitalizations and cases, with the possibility of simulating several scenarios of vaccine allocation at once for a side-by-side comparison. We provide two usage examples of Covid19Vaxplorer for vaccine allocation in Haiti and Afghanistan, which had as of Spring 2023, 2% and 33% of their populations vaccinated, and show that for these particular examples, using available vaccine as primary series vaccinations prevents more deaths than using them as boosters.

Assessing the Intersectionality of Risk Factors and Health Disparities in Canadian COVID-19 Vaccine Allocation

Assessing the Intersectionality of Risk Factors and Health Disparities in Canadian COVID-19 Vaccine Allocation

Devaluing Incarcerated Populations: Deprioritizing Incarcerated Populations for COVID-19 Vaccination.

The COVID-19 pandemic has highlighted the need for increased attention to measures in place to protect the health of incarcerated populations. Correctional facilities saw massive COVID-19 outbreaks and correctional nurses have been at the forefront of efforts to control COVID-19 in correctional facilities. Before vaccines were widely available, the National Academies of Science, Engineering, and Medicine developed a framework to guide equitable COVID-19 vaccine allocation. This study assessed the use of the framework by reviewing 15 state COVID-19 vaccination plans to identify how incarcerated populations were prioritized. Thirteen initial plans could be located. Ten of these plans placed incarcerated persons in Phases 1 and 2, while 1 state placed them in Phase 3. However, subsequent versions of the plans revealed that 8 states had deprioritized incarcerated populations by no longer considering them as a unique population. The framework was developed to promote equity, however, incarcerated persons were often dis-included as a high-risk population for vaccine prioritization, prolonging their risk of COVID-19. Engaging in the opportunity to influence both policy and practice, and promote the ethical consideration of incarcerated populations may help to address both the structural (prison) challenges and larger political structures that impacted vaccine availability and ability to provide the best care possible to this high-risk population.

Retrospective analysis of equity-based optimization for COVID-19 vaccine allocation.

Marginalized racial and ethnic groups in the United States were disproportionally affected by the COVID-19 pandemic. To study these disparities, we construct an age-and-race-stratified mathematical model of SARS-CoV-2 transmission fitted to age-and-race-stratified data from 2020 in Oregon and analyze counterfactual vaccination strategies in early 2021. We consider two racial groups: non-Hispanic White persons and persons belonging to BIPOC groups (including non-Hispanic Black persons, non-Hispanic Asian persons, non-Hispanic American-Indian or Alaska-Native persons, and Hispanic or Latino persons). We allocate a limited amount of vaccine to minimize overall disease burden (deaths or years of life lost), inequity in disease outcomes between racial groups (measured with five different metrics), or both. We find that, when allocating small amounts of vaccine (10% coverage), there is a trade-off between minimizing disease burden and minimizing inequity. Older age groups, who are at a greater risk of severe disease and death, are prioritized when minimizing measures of disease burden, and younger BIPOC groups, who face the most inequities, are prioritized when minimizing measures of inequity. The allocation strategies that minimize combinations of measures can produce middle-ground solutions that similarly improve both disease burden and inequity, but the trade-off can only be mitigated by increasing the vaccine supply. With enough resources to vaccinate 20% of the population the trade-off lessens, and with 30% coverage, we can optimize both equity and mortality. Our goal is to provide a race-conscious framework to quantify and minimize inequity that can be used for future pandemics and other public health interventions.

Prioritizing COVID-19 vaccine allocation in resource poor settings: Towards an Artificial Intelligence-enabled and Geospatial-assisted decision support framework.

To propose a novel framework for COVID-19 vaccine allocation based on three components of Vulnerability, Vaccination, and Values (3Vs). A combination of geospatial data analysis and artificial intelligence methods for evaluating vulnerability factors at the local level and allocate vaccines according to a dynamic mechanism for updating vulnerability and vaccine uptake. A novel approach is introduced including (I) Vulnerability data collection (including country-specific data on demographic, socioeconomic, epidemiological, healthcare, and environmental factors), (II) Vaccination prioritization through estimation of a unique Vulnerability Index composed of a range of factors selected and weighed through an Artificial Intelligence (AI-enabled) expert elicitation survey and scientific literature screening, and (III) Values consideration by identification of the most effective GIS-assisted allocation of vaccines at the local level, considering context-specific constraints and objectives. We showcase the performance of the 3Vs strategy by comparing it to the actual vaccination rollout in Kenya. We show that under the current strategy, socially vulnerable individuals comprise only 45% of all vaccinated people in Kenya while if the 3Vs strategy was implemented, this group would be the first to receive vaccines.

COVID-19 Conceptual Modeling: Single-Center Cross-Sectional Study.

Conceptual models are abstract representations of the real world. They are used to refine medical and nonmedical health care scopes of service. During the COVID-19 pandemic, numerous analytic predictive models were generated aiming to evaluate the impact of implemented policies on mitigating the spread of the virus. The models also aimed to examine the psychosocial factors that might govern the general population's adherence to these policies and to identify factors that could affect COVID-19 vaccine uptake and allocation. The outcomes of these analytic models helped set priorities when vaccines were available and predicted readiness to resume non-COVID-19 health care services. The objective of our research was to implement a descriptive-analytical conceptual model that analyzes the data of all COVID-19-positive cases admitted to our hospital from March 1 to May 31, 2020, the initial wave of the pandemic, the time interval during which local policies and clinical guidelines were constantly updated to mitigate the local effects of COVID-19, minimize mortality, reduce intensive care unit (ICU) admission, and ensure the safety of health care providers. The primary outcome of interest was to identify factors that might affect mortality and ICU admission rates and the impact of the implemented policy on COVID-19 positivity among health care providers. The secondary outcome of interest was to evaluate the sensitivity of the COVID-19 visual score, implemented by the Saudi Arabia Ministry of Health for COVID-19 risk assessment, and CURB-65 (confusion, urea, respiratory rate, blood pressure, and age >65 years) scores in predicting ICU admission or mortality among the study population. This was a cross-sectional study. The relevant attributes were constructed based on research findings from the first wave of the pandemic and were electronically retrieved from the hospital database. Analysis of the conceptual model was based on the International Society for Pharmacoeconomics and Outcomes Research guidelines and the Society for Medical Decision-Making. A total of 275 individuals tested positive for COVID-19 within the study design interval. The conceptualization model revealed a low-risk population based on the following attributes: a mean age of 42 (SD 19.2) years; 19% (51/275) of the study population being older adults ≥60 years of age; 80% (220/275) having a CURB-65 score <4; 53% (147/275) having no comorbidities; 5% (13/275) having extreme obesity; and 20% (55/275) having a significant hematological abnormality. The overall rate of ICU admission for the study population was 5% (13/275), and the overall mortality rate was 1.5% (4/275). The multivariate correlation analysis revealed that a high-selectivity approach was adopted, resulting in patients with complex medical problems not being sent to MOH isolation facilities. Furthermore, 5% of health care providers tested positive for COVID-19, none of whom were health care providers allocated to the COVID-19 screening areas, indicating the effectiveness of the policy implemented to ensure the safety of health care providers. Based on the conceptual model outcome, the selectivity applied in retaining high-risk populations within the hospital might have contributed to the observed low mortality rate, without increasing the risk to attending health care providers.

COVID-19: Agent-based simulation-optimization to vaccine center location vaccine allocation problem

This article presents an agent-based simulation-optimization modeling and algorithmic framework to determine the optimal vaccine center location and vaccine allocation strategies under budget constraints during an epidemic outbreak. Both simulation and optimization models incorporate population health dynamics, such as susceptible (S), vaccinated (V), infected (I) and recovered (R), while their integrated utilization focuses on the COVID-19 vaccine allocation challenges. We first formulate a dynamic location–allocation Mixed-Integer Programming (MIP) model, which determines the optimal vaccination center locations and vaccines allocated to vaccination centers, pharmacies, and health centers in a multi-period setting in each region over a geographical location. We then extend the agent-based epidemiological simulation model of COVID-19 (Covasim) by adding new vaccination compartments representing people who take the first vaccine shot and the first two shots. The Covasim involves complex disease transmission contact networks, including households, schools, and workplaces, and demographics, such as age-based disease transmission parameters. We combine the extended Covasim with the vaccination center location-allocation MIP model into one single simulation-optimization framework, which works iteratively forward and backward in time to determine the optimal vaccine allocation under varying disease dynamics. The agent-based simulation captures the inherent uncertainty in disease progression and forecasts the refined number of susceptible individuals and infections for the current time period to be used as an input into the optimization. We calibrate, validate, and test our simulation-optimization vaccine allocation model using the COVID-19 data and vaccine distribution case study in New Jersey. The resulting insights support ongoing mass vaccination efforts to mitigate the impact of the pandemic on public health, while the simulation-optimization algorithmic framework could be generalized for other epidemics.

A decision support system for the optimal allocation and distribution of COVID-19 vaccines using analytic hierarchy process (AHP) and integer programming (IP) model

IntroductionIn the Philippines, scheduling the COVID-19 vaccine rollout has been a challenging task. The local government units (LGUs) have to carefully work with limited resources to avoid underutilizing vaccines while considering prioritization schemes in the rollout. As the country continues to vaccinate its population to manage the spread of COVID-19, planning the prioritization of individuals becomes more apparent in the vaccination rollout with respect to the COVID-19 situation in their respective areas.MethodsOur study develops a location-allocation model that aims to optimally schedule the COVID-19 vaccine rollout of a municipality. Here we applied the analytic hierarchy process (AHP) to determine the prioritization of communities (i.e., barangays) in the vaccine rollout based on the number of COVID-19 cases, the population density, and the proximity of the community to the available vaccine sites. Consequently, an integer programming (IP) model was formulated to determine the assignment of individuals to the vaccine stations with respect to the prioritization of their community in the rollout, as well as the minimum number of vaccine stations to open.Results and discussionThe AHP-based IP model proposed in this study not only assigns individuals to vaccination centers, but is also capable of tailoring the vaccine rollout to prioritize individuals depending on the preferences of the LGUs, and the COVID-19 situation in their areas. Furthermore, the vaccine rollout framework in this work is applicable to other vaccine-preventable diseases (e.g., rabies, measles, etc.) should LGUs embark on adapting this approach.

‘Prioritized Distribution of Equal Shares’—An Ethical and Practicable Allocation Framework for COVID-19 Vaccines

In the context of the global COVID-19 pandemic, the fast and equitable distribution of effective vaccines worldwide is one of the challenges faced by international institutions in charge, as global equity in vaccine supply has not yet been achieved. Our paper explains the current state of ethical research on equity in global COVID-19 vaccine allocation, focusing on the COVAX Facility established by the WHO, acting as the global vaccine distributor. The article presents a detailed analysis of the first year of COVAX allocation in 2021 identifying problematic aspects of its allocation framework regarding the implementation of COVAX’s fundamental allocation principles. We argue that the COVAX Facility has developed a proper concept to deal with global vaccine allocation—but to address uncovered defaults, we introduce the ‘Prioritized Distribution of Equal Shares’ model—a both ethical and practically feasible alternative allocation framework to protect the value of human lives in both high- and low-income countries through fair and fast global vaccine distribution in health emergencies. Nonetheless, we argue that the COVAX Facility remains the main organization to provide equitable access to vaccines. Yet, the global community has to consider further aspects such as patent protection, vaccine production and the lack of power of global structures to address the inequities that have arisen. Since new wars and further crises have arisen, a shift in public global attention endangers the processing of COVID-19-related issues. That is why now more than ever extensive efforts to achieve vaccine equity are needed.

The optimal allocation of Covid-19 vaccines.

We develop a simple model of vaccine prioritization for a potential pandemic. We illustrate how the model applies to the case of Covid-19, using an early 2020 primitive estimate of occupation-based exposure risks and age-based infection fatality rates. Even based on primitive estimates the vaccine distribution strongly emphasizes age-based mortality risk rather than occupation-based exposure risk. Among others, our result suggests that 50-year-old food-processing workers and 60-year-old financial advisors should have been equally prioritized. We also find that the priorities minimally change when certain populations' exposure risks are altered by targeted stay-at-home orders or call-up of essential workers.

The political economy of vaccine distribution and China's Belt and Road Initiative

AbstractTo what extent do national strategic interests influence countries’ distribution of health assistance during a global health crisis? We examine China's global COVID-19 vaccine allocation, focusing on the relationship between its vaccine prioritization and its geopolitical expansion through the Belt and Road Initiative (BRI). We claim China uses its vaccine diplomacy as a comprehensive tool to promote its grand strategy and expand its global leadership and influence. Employing a newly available dataset on Chinese COVID-19 vaccine deliveries for a cross-section of 108 BRI member countries, our study shows that countries with foreign direct investment flows into BRI projects have received more vaccines from China. Our findings confirm that donor strategic concerns affect bilateral foreign assistance. Our results remain robust to several robustness checks, including endogeneity concerns.

Fairness and efficiency considerations in COVID-19 vaccine allocation strategies: A case study comparing front-line workers and 65-74 year olds in the United States.

The COVID-19 epidemic in the United States has been characterized by two stark disparities. COVID-19 burden has been unequally distributed among racial and ethnic groups and at the same time the mortality rates have been sharply higher among older age groups. These disparities have led some to suggest that inequalities could be reduced by vaccinating front-line workers before vaccinating older individuals, as older individuals in the US are disproportionately Non-Hispanic White. We compare the performance of two distribution policies, one allocating vaccines to front-line workers and another to older individuals aged 65-74-year-old. We estimate both the number of lives saved and the number of years of life saved under each of the policies, overall and in every race/ethnicity groups, in the United States and every state. We show that prioritizing COVID-19 vaccines for 65-74-year-olds saves both more lives and more years of life than allocating vaccines front-line workers in each racial/ethnic group, in the United States as a whole and in nearly every state. When evaluating fairness of vaccine allocation policies, the overall benefit to impact of each population subgroup should be considered, not only the proportion of doses that is distributed to each subgroup. Further work can identify prioritization schemes that perform better on multiple equity metrics.

Use of the Improvement Index to Evaluate Equitable COVID-19 Vaccine Allocation in the San Francisco Bay Area

A social determinants of health index score, or Vaccine Equity Metric (VEM), was used to prioritize resources and address geographical disparities in California's vaccination coverage. We calculated the improvement index (II), or percent of the vaccination disparity gap closed, to evaluate the effects of this vaccination strategy in the SF Bay Area during the SARS-CoV-2 Delta variant surge. We conducted a cross-sectional study on SF Bay Area zip codes during the Delta surge (July 6-October 5, 2021). Data came from the California Immunization Registry and 2019 5-year American Community Survey. We used Spearman correlations to examine relationships between VEM category and vaccine coverage, and Kruskal-Wallis tests to compare vaccination II across VEM categories. We studied 248 zip codes in the SF Bay Area. Those with the lowest resources (VEM Level 1) had the highest absolute increase in vaccination coverage (14.3 vs. 5.4 percentage points in VEM Level 4); though a contribution was higher starting vaccination rates in Level 4 zip codes with the greatest resources. The ratio of vaccination coverage between the lowest and highest resourced zip codes increased from 0.79 to 0.9, suggesting reduced disparity. However, it is difficult to interpret given wide differences in n (Level 1 n=8 vs. Level 4 n=151). In contrast, the vaccination II accounts for each VEM category's baseline vaccination; all were statistically similar (grand mean 41.5%, p=0.367), implying comparable improvement across all zip codes. Using VEM to identify and prioritize resources to vulnerable communities contribute to equitable vaccine allocation in the SF Bay Area. Our study demonstrates an example of the II's advantages over conventional health equity metrics, such as absolute differences and relative effect measures, which can overestimate an intervention's impact.azz health disparity, San Francisco Bay Area, improvement index.

Optimizing COVID-19 vaccine allocation considering the target population.

Vaccine allocation strategy for COVID-19 is an emerging and important issue that affects the efficiency and control of virus spread. In order to improve the fairness and efficiency of vaccine distribution, this paper studies the optimization of vaccine distribution under the condition of limited number of vaccines. We pay attention to the target population before distributing vaccines, including attitude toward the vaccination, priority groups for vaccination, and vaccination priority policy. Furthermore, we consider inventory and budget indexes to maximize the precise scheduling of vaccine resources. A mixed-integer programming model is developed for vaccine distribution considering the target population from the viewpoint of fairness and efficiency. Finally, a case study is provided to verify the model and provide insights for vaccine distribution.

Walking a tightrope: understanding COVID-19 vaccine allocation in India

Walking a tightrope: understanding COVID-19 vaccine allocation in India

Spatial Optimization to Improve COVID-19 Vaccine Allocation

Early distribution of COVID-19 vaccines was largely driven by population size and did not account for COVID-19 prevalence nor location characteristics. In this study, we applied an optimization framework to identify distribution strategies that would have lowered COVID-19 related morbidity and mortality. During the first half of 2021 in the state of Missouri, optimized vaccine allocation would have decreased case incidence by 8% with 5926 fewer COVID-19 cases, 106 fewer deaths, and 4.5 million dollars in healthcare cost saved. As COVID-19 variants continue to be identified, and the likelihood of future pandemics remains high, application of resource optimization should be a priority for policy makers.

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

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

How should COVID-19 vaccines be distributed between the Global North and South: a discrete choice experiment in six European countries.

The global distribution of COVID-19 vaccinations remains highly unequal. We examine public preferences in six European countries regarding the allocation of COVID-19 vaccines between the Global South and Global North. We conducted online discrete choice experiments with adult participants in France (n=766), Germany (n=1964), Italy (n=767), Poland (n=670), Spain (n=925), and Sweden (n=938). Respondents were asked to decide which one of two candidates should receive the vaccine first. The candidates varied on four attributes: age, mortality risk, employment, and living in a low- or high-income country. We analysed the relevance of each attribute in allocation decisions using conditional logit regressions. In all six countries, respondents prioritised candidates with a high mortality and infection risk, irrespective of whether the candidate lived in the respondent's own country. All else equal, respondents in Italy, France, Spain, and Sweden gave priority to a candidate from a low-income country, whereas German respondents were significantly more likely to choose the candidate from their own country. Female, younger, and more educated respondents were more favourable to an equitable vaccine distribution. Given these preferences for global solidarity, European governments should promote vaccine transfers to poorer world regions. Funding was provided by the European Union's Horizon H2020 research and innovation programme under grant agreement 101016233 (PERISCOPE).