Optimal vaccine allocation for COVID-19 in the Netherlands: A data-driven prioritization

Learning from the COVID-19 pandemic: A systematic review of mathematical vaccine prioritization models

As the world becomes ever more connected, the chance of pandemics increases as well. The recent COVID-19 pandemic and the concurrent global mass vaccine roll-out provides an ideal setting to learn from and refine our understanding of infectious disease models for better future preparedness. In this review, we systematically analyze and categorize mathematical models that have been developed to design optimal vaccine prioritization strategies of an initially limited vaccine. As older individuals are disproportionately affected by COVID-19, the focus is on models that take age explicitly into account. The lower mobility and activity level of older individuals gives rise to non-trivial trade-offs. Secondary research questions concern the optimal time interval between vaccine doses and spatial vaccine distribution. This review showcases the effect of various modeling assumptions on model outcomes. A solid understanding of these relationships yields better infectious disease models and thus public health decisions during the next pandemic.

Patients' perspectives on ethical principles to fairly allocate scarce surgical resources during the COVID-19 pandemic in the Netherlands: a Q-methodology study

OBJECTIVES

During the COVID-19 pandemic, healthcare professionals were faced with prioritisation dilemmas due to limited surgical capacity. While the views of healthcare professionals on fair allocation have been given considerable attention, the views of patients have been overlooked. To address this imbalance, our study aimed to identify which ethical principles are most supported by patients regarding the fair allocation of surgical resources.

DESIGN

A Q-methodology study was conducted. Participants ranked ordered 20 statements covering different viewpoints on fair allocation according to their point of view, followed by an interview. Principal component analysis followed by varimax rotation was used to identify subgroups who broadly agreed in terms of their rankings.

SETTING

The setting of this study was in the Netherlands.

PARTICIPANTS

16 patient representatives were purposively sampled.

RESULTS

Two perspectives were identified, both of which supported utilitarianism. In perspective 1, labelled as 'clinical needs and outcomes', resource allocation should aim to maximise the health gains based on individual patient characteristics. In perspective 2, labelled as 'population outcomes and contribution to society', allocation should maximise health gains as with perspective 1, but this should also consider societal gains.

CONCLUSIONS

There was a broad agreement among patient representatives that utilitarianism should be the guiding ethical principle for fair allocation of scarce surgical resources. The insights gained from this study should be integrated into policymaking and prioritisation strategies in future healthcare crises.

[Opportunities and challenges in vaccination implementation against COVID-19: A systematic review]

OBJECTIVE

To analyze the experiences regarding the implementation of COVID-19 vaccination strategies, emphasizing the opportunities and challenges identified during its implementation.

DESIGN

A systematic review of the literature published between 2020 and 2022.

DATA SOURCES

The study was conducted across four databases: PubMed, ScienceDirect, Scielo, and Lilacs.

SELECTION OF STUDIES

Publications selection followed the PRISMA methodology (Preferred Reporting Items for Systematic Review and Meta-Analysis).

DATA EXTRACTION

A database was created where key elements of the selected study were identified and recorded, such as results, discussion, and conclusions. In addition, analysis categories were created such as: stages of the implementation plan, challenges identified and opportunity areas.

RESULTS AND CONCLUSIONS

292 publications were found, of which 25 were selected for analysis. Of these, 64% came from high-income countries and 32% from upper-middle-income countries. According to the stages of the implementation plan, 20% of the studies focused on regulations, planning and coordination; 28% in prioritization of the population to be vaccinated; 16% in acceptance, demand, and risk communication; and 16% in administration and information systems. Reflection on the response to the pandemic invites us to consider various aspects, such as the organization and function of health systems, the importance of collaborative work, efforts to achieve equity, communication strategies, as well as ethical dilemmas when seeking preserve health.

Learning from the COVID-19 pandemic: a systematic review of mathematical vaccine prioritization models

As the world becomes ever more connected, the chance of pandemics increases as well. The recent COVID-19 pandemic and the concurrent global mass vaccine roll-out provides an ideal setting to learn from and refine our understanding of infectious disease models for better future preparedness. In this review, we systematically analyze and categorize mathematical models that have been developed to design optimal vaccine prioritization strategies of an initially limited vaccine. As older individuals are disproportionately affected by COVID-19, the focus is on models that take age explicitly into account. The lower mobility and activity level of older individuals gives rise to non-trivial trade-offs. Secondary research questions concern the optimal time interval between vaccine doses and spatial vaccine distribution. This review showcases the effect of various modeling assumptions on model outcomes. A solid understanding of these relationships yields better infectious disease models and thus public health decisions during the next pandemic.

Age-specific transmission dynamics of SARS-CoV-2 during the first 2 years of the pandemic

During its first 2 years, the SARS-CoV-2 pandemic manifested as multiple waves shaped by complex interactions between variants of concern, non-pharmaceutical interventions, and the immunological landscape of the population. Understanding how the age-specific epidemiology of SARS-CoV-2 has evolved throughout the pandemic is crucial for informing policy decisions. In this article, we aimed to develop an inference-based modeling approach to reconstruct the burden of true infections and hospital admissions in children, adolescents, and adults over the seven waves of four variants (wild-type, Alpha, Delta, and Omicron BA.1) during the first 2 years of the pandemic, using the Netherlands as the motivating example. We find that reported cases are a considerable underestimate and a generally poor predictor of true infection burden, especially because case reporting differs by age. The contribution of children and adolescents to total infection and hospitalization burden increased with successive variants and was largest during the Omicron BA.1 period. However, the ratio of hospitalizations to infections decreased with each subsequent variant in all age categories. Before the Delta period, almost all infections were primary infections occurring in naive individuals. During the Delta and Omicron BA.1 periods, primary infections were common in children but relatively rare in adults who experienced either reinfections or breakthrough infections. Our approach can be used to understand age-specific epidemiology through successive waves in other countries where random community surveys uncovering true SARS-CoV-2 dynamics are absent but basic surveillance and statistics data are available.

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.

Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review

BACKGROUND

The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical models of SARS-CoV-2 applied to real-world data, including immunization for coronavirus 2019 (COVID-19).

METHODOLOGY

PubMed, JSTOR, medRxiv, LILACS, EconLit, and other databases were searched for studies employing epidemiological mathematical models of SARS-CoV-2 applied to real-world data. We summarized the information qualitatively, and each article included was assessed for bias risk using the Joanna Briggs Institute (JBI) and PROBAST checklist tool. The PROSPERO registration number is CRD42022344542.

FINDINGS

In total, 5646 articles were retrieved, of which 411 were included. Most of the information was published in 2021. The countries with the highest number of studies were the United States, Canada, China, and the United Kingdom; no studies were found in low-income countries. The SEIR model (susceptible, exposed, infectious, and recovered) was the most frequently used approach, followed by agent-based modeling. Moreover, the most commonly used software were R, Matlab, and Python, with the most recurring health outcomes being death and recovery. According to the JBI assessment, 61.4% of articles were considered to have a low risk of bias.

INTERPRETATION

The utilization of mathematical models increased following the onset of the SARS-CoV-2 pandemic. Stakeholders have begun to incorporate these analytical tools more extensively into public policy, enabling the construction of various scenarios for public health. This contribution adds value to informed decision-making. Therefore, understanding their advancements, strengths, and limitations is essential.

Impact of vaccine measures on the transmission dynamics of COVID-19

In many nations, efforts to prevent and control COVID-19 have been significantly impeded by the SARS-CoV-2 virus ongoing mutation. The Omicron strain, a more recent and prevalent strain, has had more significant detrimental effects in countries worldwide. To investigate the impact of the Omicron BA.2 strain on vaccine efficacy, we proposed a model with vaccination and immunological decline in this research. Then, we fitted our model based on the number of daily new instances reported by the government in Jilin and Shanghai, China. We estimated the effective reproduction number Re = 4.71 for the Jilin and Re = 3.32 for Shanghai. Additionally, we do sensitivity analysis to identify the critical factors affecting the effective reproduction number Re. It was found that vaccination rate, effectiveness rate, and declining rate had a significant effect on Re. Further, we investigate the relevant parameter thresholds that make Re lower than unity. Finally, rich numerical experiments were then carried out. We observed that even when vaccine efficiency was not high, increasing vaccination rates had a significant effect on early disease transmission, that limiting social distance was the most economical and rational measure to control the spread of disease, and that for a short period, reducing immune decline was not significant in curbing disease transmission.

The daily updated Dutch national database on COVID-19 epidemiology, vaccination and sewage surveillance

The Dutch national open database on COVID-19 has been incrementally expanded since its start on 30 April 2020 and now includes datasets on symptoms, tests performed, individual-level positive cases and deaths, cases and deaths among vulnerable populations, settings of transmission, hospital and ICU admissions, SARS-CoV-2 variants, viral loads in sewage, vaccinations and the effective reproduction number. This data is collected by municipal health services, laboratories, hospitals, sewage treatment plants, vaccination providers and citizens and is cleaned, analysed and published, mostly daily, by the National Institute for Public Health and the Environment (RIVM) in the Netherlands, using automated scripts. Because these datasets cover the key aspects of the pandemic and are available at detailed geographical level, they are essential to gain a thorough understanding of the past and current COVID-19 epidemiology in the Netherlands. Future purposes of these datasets include country-level comparative analysis on the effect of non-pharmaceutical interventions against COVID-19 in different contexts, such as different cultural values or levels of socio-economic disparity, and studies on COVID-19 and weather factors.

An integrated chance constraints approach for optimal vaccination strategies under uncertainty for COVID-19

Despite concerted efforts by health authorities worldwide to contain COVID-19, the SARS-CoV-2 virus has continued to spread and mutate into new variants with uncertain transmission characteristics. Therefore, there is a need for new data-driven models for determining optimal vaccination strategies that adapt to the new variants with their uncertain transmission characteristics. Motivated by this challenge, we derive an integrated chance constraints stochastic programming (ICC-SP) approach for finding vaccination strategies for epidemics that incorporates population demographics for any region of the world, uncertain disease transmission and vaccine efficacy. An optimal vaccination strategy specifies the proportion of individuals in a given household-type to vaccinate to bring the reproduction number to below one. The ICC-SP approach provides a quantitative method that allows to bound the expected excess of the reproduction number above one by an acceptable amount according to the decision-maker's level of risk. This new methodology involves a multi-community household based epidemiology model that uses census demographics data, vaccination status, age-related heterogeneity in disease susceptibility and infectivity, virus variants, and vaccine efficacy. The new methodology was tested on real data for seven neighboring counties in the United States state of Texas. The results are promising and show, among other findings, that vaccination strategies for controlling an outbreak should prioritize vaccinating certain household sizes as well as age groups with relatively high combined susceptibility and infectivity.

Evaluating primary and booster vaccination prioritization strategies for COVID-19 by age and high-contact employment status using data from contact surveys

The debate around vaccine prioritization for COVID-19 has revolved around balancing the benefits from: (1) the direct protection conferred by the vaccine amongst those at highest risk of severe disease outcomes, and (2) the indirect protection through vaccinating those that are at highest risk of being infected and of transmitting the virus. While adults aged 65+ are at highest risk for severe disease and death from COVID-19, essential service and other in-person workers with greater rates of contact may be at higher risk of acquiring and transmitting SARS-CoV-2. Unfortunately, there have been relatively little data available to understand heterogeneity in contact rates and risk across these demographic groups. Here, we retrospectively analyze and evaluate vaccination prioritization strategies by age and worker status. We use a mathematical model of SARS-CoV-2 transmission and uniquely detailed contact data collected as part of the Berkeley Interpersonal Contact Survey to evaluate five vaccination prioritization strategies: (1) prioritizing only adults over age 65, (2) prioritizing only high-contact workers, (3) splitting prioritization between adults 65+ and high-contact workers, (4) tiered prioritization of adults over age 65 followed by high-contact workers, and (5) tiered prioritization of high-contact workers followed by adults 65+. We find that for the primary two-dose vaccination schedule, assuming 70% uptake, a tiered roll-out that first prioritizes adults 65+ averts the most deaths (31% fewer deaths compared to a no-vaccination scenario) while a tiered roll-out that prioritizes high contact workers averts the most number of clinical infections (14% fewer clinical infections compared to a no-vaccination scenario). We also consider prioritization strategies for booster doses during a subsequent outbreak of a hypothetical new SARS-CoV-2 variant. We find that a tiered roll-out that prioritizes adults 65+ for booster doses consistently averts the most deaths, and it may also avert the most number of clinical cases depending on the epidemiology of the SARS-CoV-2 variant and the vaccine efficacy.

Asymptotic Analysis of Optimal Vaccination Policies

Targeted vaccination policies can have a significant impact on the number of infections and deaths in an epidemic. However, optimising such policies is complicated, and the resultant solution may be difficult to explain to policy-makers and to the public. The key novelty of this paper is a derivation of the leading-order optimal vaccination policy under multi-group susceptible-infected-recovered dynamics in two different cases. Firstly, it considers the case of a small vulnerable subgroup in a population and shows that (in the asymptotic limit) it is optimal to vaccinate this group first, regardless of the properties of the other groups. Then, it considers the case of a small vaccine supply and transforms the optimal vaccination problem into a simple knapsack problem by linearising the final size equations. Both of these cases are then explored further through numerical examples, which show that these solutions are also directly useful for realistic parameter values. Moreover, the findings of this paper give some general principles for optimal vaccination policies which will help policy-makers and the public to understand the reasoning behind optimal vaccination programs in more generic cases.

Optimizing two-dose vaccine resource allocation to combat a pandemic in the context of limited supply: The case of COVID-19

The adequate vaccination is a promising solution to mitigate the enormous socio-economic costs of the ongoing COVID-19 pandemic and allow us to return to normal pre-pandemic activity patterns. However, the vaccine supply shortage will be inevitable during the early stage of the vaccine rollout. Public health authorities face a crucial challenge in allocating scarce vaccines to maximize the benefits of vaccination. In this paper, we study a multi-period two-dose vaccine allocation problem when the vaccine supply is highly limited. To address this problem, we constructed a novel age-structured compartmental model to capture COVID-19 transmission and formulated as a nonlinear programming (NLP) model to minimize the total number of deaths in the population. In the NLP model, we explicitly take into account the two-dose vaccination procedure and several important epidemiologic features of COVID-19, such as pre-symptomatic and asymptomatic transmission, as well as group heterogeneity in susceptibility, symptom rates, severity, etc. We validated the applicability of the proposed model using a real case of the 2021 COVID-19 vaccination campaign in the Midlands of England. We conducted comparative studies to demonstrate the superiority of our method. Our numerical results show that prioritizing the allocation of vaccine resources to older age groups is a robust strategy to prevent more subsequent deaths. In addition, we show that releasing more vaccine doses for first-dose recipients could lead to a greater vaccination benefit than holding back second doses. We also find that it is necessary to maintain appropriate non-pharmaceutical interventions (NPIs) during the vaccination rollout, especially in low-resource settings. Furthermore, our analysis indicates that starting vaccination as soon as possible is able to markedly alleviate the epidemic impact when the vaccine resources are limited but are currently available. Our model provides an effective tool to assist policymakers in developing adaptive COVID-19 likewise vaccination strategies for better preparedness against future pandemic threats.

Comparative analysis of humoral responses to BNT162b2 vaccine among patients with hematologic disorders and organ transplant recipients

Vaccination against SARS-COV-2 is considered the most promising approach to curbing the pandemic. Patients with an immunocompromised state, such as those with hematological malignancies and organ transplantation recipients, are considered more susceptible to infection, but these at-risk patients were underrepresented in early clinical trials for vaccination. Although a growing body of studies suggests that the humoral response to COVID-19 vaccination in each of these at-risk groups of patients may be suboptimal in comparison to healthy controls, a clinical and strategic information for the further comparative analysis among these groups is not fully described. The humoral responses after two doses of BNT162b2 vaccination were evaluated in a total of 187 patients either with allogeneic hematopoietic transplantation, with renal transplantation, with anti-CD20 antibody therapy, or with anti-CD38 antibody therapy, and in 66 healthy controls. The early response at one to three months after vaccination was significantly inferior among patients with renal transplantation, patients with anti-CD20 antibody therapy, and patients with anti-CD38 antibody therapy in comparison to healthy control. But the patients with allogeneic hematopoietic transplantation showed early humoral response comparable to healthy control. The late response at 6 months after vaccination was still suboptimal among patients with renal transplantation and patients with anti-CD20 therapy. Among our patient group, renal transplant recipients had the lowest antibody titers after vaccination regardless of timing of vaccination. Patients who had received allogeneic hematopoietic transplantation attained a comparable serological response to the control group especially if they are vaccinated >300 days after transplantation, but the response was suboptimal if the vaccination was within 300 days after transplantation. Our results may provide policy makers with critical information for the further stratification of at-risk groups, helping contribute to a better allocation of resources, including additional booster vaccination.