Strategies for Vaccine Prioritization and Mass Dispensing

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.

Impaired neutralizing antibodies and preserved cellular immunogenicity against SARS-CoV-2 in systemic autoimmune rheumatic diseases

Reports on vaccine immunogenicity in patients with systemic autoimmune rheumatic diseases (SARDs) have been inconclusive. Here, we report the immunogenicity of heterologous prime-boost with an inactivated vaccine followed by an adenoviral vector vaccine in patients with SARDs using anti-RBD antibodies, neutralizing capacity against Omicron BA.2 [plaque-reduction neutralization test (PRNT)], T cell phenotypes, and effector cytokine production at 4 weeks after vaccination. SARD patients had lower median (IQR) anti-RBD-IgG levels and neutralizing function against the Omicron BA.2 variant than the healthy group (p = 0.003, p = 0.004, respectively). T cell analysis revealed higher levels of IFN-γ- and TNF-α-secreting CD4 + T cells (p < 0.001, p = 0.0322, respectively) in SARD patients than in the healthy group. Effector cytokine production by CD8 + T cells was consistent with Th responses. These results suggest that this vaccine regimen revealed mildly impaired humoral response while preserving cellular immunogenicity and may be an alternative for individuals for whom mRNA vaccines are contraindicated.

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.

Usage of Compartmental Models in Predicting COVID-19 Outbreaks

Accurately predicting the spread of the SARS-CoV-2, the cause of the COVID-19 pandemic, is of great value for global regulatory authorities to overcome a number of challenges including medication shortage, outcome of vaccination, and control strategies planning. Modeling methods that are used to simulate and predict the spread of COVID-19 include compartmental model, structured metapopulations, agent-based networks, deep learning, and complex network, with compartmental modeling as one of the most widely used methods. Compartmental model has two noteworthy features, a flexible framework that allows users to easily customize the model structure and its high adaptivity that allows well-matured approaches (e.g., Bayesian inference and mixed-effects modeling) to improve parameter estimation. We retrospectively evaluated the prediction performances of the compartmental models on the CDC COVID-19 Mathematical Modeling webpage based on data collected between August 2020 and February 2021, and subsequently discussed in detail their corresponding model enhancement. Finally, we presented examples using the compartmental models to assist policymaking. By evaluating all models in parallel, we systemically evaluated the performance and evolution of using compartmental models for COVID-19 pandemic prediction. In summary, as a 100-year-old epidemic approach, the compartmental model presents a powerful tool that is extremely adaptive and can be readily customized and implemented to address new data or emerging needs during a pandemic.

Queueing Theory and COVID-19 Prevention: Model Proposal to Maximize Safety and Performance of Vaccination Sites

Introduction

COVID-19 (Coronavirus Disease 19) has rapidly spread all around the world. Vaccination represents one of the most promising counter-pandemic measures. There is still little specific evidence in literature on how to safely and effectively program access and flow through specific healthcare settings to avoid overcrowding in order to prevent SARS-CoV-2 transmission. Literature regarding appointment scheduling in healthcare is vast. Unpunctuality however, especially when targeting healthcare workers during working hours, is always possible. Therefore, when determining how many subjects to book, using a linear method assuming perfect adhesion to scheduled time could lead to organizational problems.

Methods

This study proposes a “Queuing theory” based approach. A COVID-19 vaccination site targeting healthcare workers based in a teaching hospital in Rome was studied to determine real-life arrival rate variability. Three simulations using Queueing theory were performed.

Results

Queueing theory application reduced subjects queueing over maximum safety requirements by 112 in a real-life based vaccination setting, by 483 in a double-sized setting and by 750 in a mass vaccination model compared with a linear approach. In the 3 settings, respectively, the percentage of station's time utilization was 98.6, 99.4 and 99.8%, while the average waiting time was 27.2, 33.84, and 33.84 min.

Conclusions

Queueing theory has already been applied in healthcare. This study, in line with recent literature developments, proposes the adoption of a Queueing theory base approach to vaccination sites modeling, during the COVID-19 pandemic, as this tool enables to quantify ahead of time the outcome of organizational choices on both safety and performance of vaccination sites.

Prioritizing COVID-19 vaccination. Part 1: Final size comparison between a single dose and double dose

In response to the coronavirus disease 2019 (COVID-19) pandemic, Japan conducted mass vaccination. Seventy-two million doses of vaccine (i.e., for 36 million people if a double dose is planned per person) were obtained, with initial vaccination of the older population (≡ 65 years). Because of the limited number of vaccines, the government discussed shifting the plan to administering only a single dose so that younger individuals (<65 years) could also be vaccinated with one shot. This study aimed to determine the optimal vaccine distribution strategy using a simple mathematical method. After accounting for age-dependent relative susceptibility after single- and double-dose vaccination (vs and vd, respectively, compared with unvaccinated), we used the age-dependent transmission model to compute the final size for various patterns of vaccine distributions. Depending on the values of vs, the cumulative risk of death would be lower if all 72 million doses were used as a double dose for older people than if a single-dose program was conducted in which half is administered to older people and the other half is administered to adults (i.e., 1,856,000 deaths in the former program and 1,833,000-2,355,000 deaths [depending on the values of vs] in the latter). Even if 90% of older people were vaccinated twice and 100% of adults were vaccinated once, the effective reproduction number would be reduced from 2.50 to1.14. Additionally, the cumulative risk of infection would range from 12.0% to 54.6% and there would be 421,000-1,588,000deaths (depending on the values of vs). If an epidemic appears only after completing vaccination, vaccination coverage using a single-dose program with widespread vaccination among adults will not outperform a double-dose strategy.

Immunogenicity and safety of an intradermal ChAdOx1 nCoV-19 boost in a healthy population

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic. Two doses of an inactivated SARS-CoV-2 vaccine (CoronaVac) have been shown to be insufficient to protect against variants of concern (VOCs), while viral vector vaccines remain protective against the infection. Herein, we conducted a preliminary study to evaluate the safety and immunity in an adult population who received the conventional 2 dosage-regimen of inactivated SARS-CoV-2 vaccine; with an additional intradermal ChAdOx1 nCoV-19 reciprocal dosage (1:5). An Intramuscular ChAdOx1 nCoV-19 booster was also included as a control. Immediate and delayed local reactions were frequently observed in the fractional intradermal boost, but systemic side effects were significantly decreased compared to the conventional intramuscular boost. The anti-RBD-IgG levels, the neutralising function against delta variants, and T cell responses were significantly increased after boosting via both routes. Interestingly, the shorter interval elicited higher immunogenicity compared to the extended interval. Taken together, a reciprocal dosage of intradermal ChAdOx1 nCoV-19 booster reduces systemic adverse reactions and enhances non inferiority humoral and cellular immune responses compared to a full dose of intramuscular boosting. These findings provide for an effective vaccine management during the shortages of vaccine supply.

Strategies for Disease Containment: A Biological-Behavioral-Intervention Computational Informatics Framework

In this study, we describe the development and use of a biological-behavior-intervention computational informatics framework that combines disease modelling for infectious virus with stratifications for social behavior and employment, and resource logistics. The framework incorporates heterogeneous group behavior and interaction dynamics, and optimizes intervention and resources for effective containment. We demonstrate its usage by analyzing and optimizing containment strategies for the 2014-2016 West Africa Ebola outbreak, and its implementation for responses to the 2020 COVID-19 pandemic in the United States. Our analysis shows that timely action within 1.5 months from the onset of confirmed cases can cut down 90% of overall infections and bring rapid containment within 6-8 months. The additional medical resources required are minor and would ensure proper treatment and quarantine of patients while reducing the risk of infections among healthcare workers. The benefit (in infection / death control) would be reduced by 10 to over 100 fold and time to containment would increase by 2-4 fold when intervention and medical resources are injected within 5 months. In contrast, the additional resources needed to bring down the overall infection in a delayed intervention are significant, with inferior results. The disease module can be tailored for different pathogens. It expands the well-used SEIR model to include social and intervention activities, asymptomatic and post-recovery transmission, hospitalization, outcome of recovery, and funeral events. The model also examines the transmission rate of health care workers and allows for heterogenous infection factors among different groups. It also captures time-variant human behavior during the horizon of the outbreak. The framework optimizes the intervention timeline and resource allocation during an infectious disease outbreak and offers insights on how resource availability in time and quantity can affect the disease trends and containment significantly. This can inform policy, disease management and resource allocation. While focusing on bed availability for quarantine and treatment appears to be simplistic, their necessity for Ebola responses cannot be overemphasized. We link these insights to a web-based tool to provide quick and intuitive observations for decision making and investigation of the disease outbreak situation. Subsequent use of the system to determine the optimal timing and effectiveness and tradeoffs analysis of various non-pharmaceutical intervention strategies for COVID-19 provide a foundation for policy makers to execute the first-step response. These results have been implemented on the ground since March 2020. The web-based tool pinpoints accurately the import of disease from global travels and associated disease spread and health burdens. This prospectively affirms the importance of such a real-time computational system, and its availability before onset of a pandemic.

Immunogenicity and Safety of an Intradermal BNT162b2 mRNA Vaccine Booster after Two Doses of Inactivated SARS-CoV-2 Vaccine in Healthy Population

Effective vaccine coverage is urgently needed to tackle the COVID-19 pandemic. Inactivated vaccines have been introduced in many countries for emergency usage, but have only provided limited protection. Heterologous vaccination is a promising strategy to maximise vaccine immunogenicity. Here, we conducted a phase I, randomised control trial to observe the safety and immunogenicity after an intradermal boost, using a fractional dosage (1:5) of BNT162b2 mRNA vaccine in healthy participants in Songkhla, Thailand. In total, 91 volunteers who had been administered with two doses of inactivated SARS-CoV-2 (CoronaVac) were recruited into the study, and then randomised (1:1:1) to received different regimens of the third dose. An intramuscular booster with a full dose of BNT162b2 was included as a conventional control, and a half dose group was included as reciprocal comparator. Both, immediate and delayed adverse events following immunisation (AEFI) were monitored. Humoral and cellular immune responses were examined to observe the booster effects. The intradermal booster provided significantly fewer systemic side effects, from 70% down to 19.4% (p < 0.001); however, they were comparable to local reactions with the conventional intramuscular booster. In the intradermal group after receiving only one fifth of the conventional dosage, serum Anti-RBD IgG was halved compared to the full dose of an intramuscular injection. However, the neutralising function against the Delta strain remained intact. T cell responses were also less effective in the intradermal group compared to the intramuscular booster. Together, the intradermal booster, using a fractional dose of BNT162b2, can reduce systemic reactions and provides a good level and function of antibody responses compared to the conventional booster. This favourable intradermal boosting strategy provides a suitable alternative for vaccines and effective vaccine management to increase the coverage during the vaccine shortage.