Evaluation of COVID-19 vaccination strategies with a delayed second dose

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.

Dynamics of anti-SARS-CoV-2 antibodies in the Albanian population: Impact of infection- and vaccine-induced immunity during the COVID-19 pandemic

Objectives

Understanding immune response dynamics during the COVID-19 pandemic is crucial for optimizing future vaccine strategies. This study investigated the infection- and vaccine-induced SARS-CoV-2 antibody responses in the Albanian population from August 2021 to August 2022.

Methods

This used a cross-sectional approach, analyzing two independent, randomly selected population samples over 1 year. Participants' demographic, health, vaccination, and COVID-19 data were collected, with blood samples assessed via enzyme linked immunosorbent assay for immunoglobulin G class anti-spike and anti-nucleocapsid antibodies.

Results

By August 2022, all individuals receiving one vaccine dose achieved antibody levels comparable to those receiving two doses (median 7.71 index ratio [IR] vs 7.00 IR). In August 2021, those with previous COVID-19 infection receiving one vaccine dose showed median anti-spike immunoglobulin G levels of 7.22 IR compared with 4.84 IR in those without previous infection receiving two doses. However, individuals aged ≥61 years required two vaccine doses to achieve similar immune responses as younger individuals with one dose.

Conclusions

These findings underscore the importance of hybrid immunity, suggesting one vaccine dose may suffice for individuals with previous COVID-19 infection, whereas older adults require additional doses for optimal protection. This study provides insights into humoral immune response dynamics, which is crucial for refining COVID-19 vaccination strategies in middle-income countries with low vaccination coverage and high infection rates.

Mental illness and COVID-19 vaccination: a multinational investigation of observational & register-based data

Individuals with mental illness are at higher risk of severe COVID-19 outcomes. However, previous studies on the uptake of COVID-19 vaccination in this population have reported conflicting results. Using data from seven cohort studies (N = 325,298) included in the multinational COVIDMENT consortium, and the Swedish registers (N = 8,080,234), this study investigates the association between mental illness (defined using self-report measures, clinical diagnosis and prescription data) and COVID-19 vaccination uptake. Results from the COVIDMENT cohort studies were pooled using meta-analyses, the majority of which showed no significant association between mental illness and vaccination uptake. In the Swedish register study population, we observed a very small reduction in the uptake of both the first and second dose of a COVID-19 vaccine among individuals with vs. without mental illness; the reduction was however greater among those not using psychiatric medication. Here we show that uptake of the COVID-19 vaccine is generally high among individuals both with and without mental illness, however the lower levels of vaccination uptake observed among subgroups of individuals with unmedicated mental illness warrants further attention.

Beyond SIRD models: a novel dynamic model for epidemics, relating infected with entries to health care units and application for identification and restraining policy

Epidemic models of susceptibles, exposed, infected, recovered and deceased (SΕIRD) presume homogeneity, constant rates and fixed, bilinear structure. They produce short-range, single-peak responses, hardly attained under restrictive measures. Tuned via uncertain I,R,D data, they cannot faithfully represent long-range evolution. A robust epidemic model is presented that relates infected with the entry rate to health care units (HCUs) via population averages. Model uncertainty is circumvented by not presuming any specific model structure, or constant rates. The model is tuned via data of low uncertainty, by direct monitoring: (a) of entries to HCUs (accurately known, in contrast to delayed and non-reliable I,R,D data) and (b) of scaled model parameters, representing population averages. The model encompasses random propagation of infections, delayed, randomly distributed entries to HCUs and varying exodus of non-hospitalized, as disease severity subdues. It closely follows multi-pattern growth of epidemics with possible recurrency, viral strains and mutations, varying environmental conditions, immunity levels, control measures and efficacy thereof, including vaccination. The results enable real-time identification of infected and infection rate. They allow design of resilient, cost-effective policy in real time, targeting directly the key variable to be controlled (entries to HCUs) below current HCU capacity. As demonstrated in ex post case studies, the policy can lead to lower overall cost of epidemics, by balancing the trade-off between the social cost of infected and the economic contraction associated with social distancing and mobility restriction measures.

Evaluating the direct effect of vaccination and non-pharmaceutical interventions during the COVID-19 pandemic in Europe

BACKGROUND

Across Europe, countries have responded to the COVID-19 pandemic with a combination of non-pharmaceutical interventions and vaccination. Evaluating the effectiveness of such interventions is of particular relevance to policy-makers.

METHODS

We leverage almost three years of available data across 38 European countries to evaluate the effectiveness of governmental responses in controlling the pandemic. We developed a Bayesian hierarchical model that flexibly relates daily COVID-19 incidence to past levels of vaccination and non-pharmaceutical interventions as summarised in the Stringency Index. Specifically, we use a distributed lag approach to temporally weight past intervention values, a tensor-product smooth to capture non-linearities and interactions between both types of interventions, and a hierarchical approach to parsimoniously address heterogeneity across countries.

RESULTS

We identify a pronounced negative association between daily incidence and the strength of non-pharmaceutical interventions, along with substantial heterogeneity in effectiveness among European countries. Similarly, we observe a strong but more consistent negative association with vaccination levels. Our results show that non-linear interactions shape the effectiveness of interventions, with non-pharmaceutical interventions becoming less effective under high vaccination levels. Finally, our results indicate that the effects of interventions on daily incidence are most pronounced at a lag of 14 days after being in place.

CONCLUSIONS

Our Bayesian hierarchical modelling approach reveals clear negative and lagged effects of non-pharmaceutical interventions and vaccination on confirmed COVID-19 cases across European countries.

Novel multi-cluster workflow system to support real-time HPC-enabled epidemic science: Investigating the impact of vaccine acceptance on COVID-19 spread

We present MacKenzie, a HPC-driven multi-cluster workflow system that was used repeatedly to configure and execute fine-grained US national-scale epidemic simulation models during the COVID-19 pandemic. Mackenzie supported federal and Virginia policymakers, in real-time, for a large number of "what-if" scenarios during the COVID-19 pandemic, and continues to be used to answer related questions as COVID-19 transitions to the endemic stage of the disease. MacKenzie is a novel HPC meta-scheduler that can execute US-scale simulation models and associated workflows that typically present significant big data challenges. The meta-scheduler optimizes the total execution time of simulations in the workflow, and helps improve overall human productivity. As an exemplar of the kind of studies that can be conducted using Mackenzie, we present a modeling study to understand the impact of vaccine-acceptance in controlling the spread of COVID-19 in the US. We use a 288 million node synthetic social contact network (digital twin) spanning all 50 US states plus Washington DC, comprised of 3300 counties, with 12 billion daily interactions. The highly-resolved agent-based model used for the epidemic simulations uses realistic information about disease progression, vaccine uptake, production schedules, acceptance trends, prevalence, and social distancing guidelines. Computational experiments show that, for the simulation workload discussed above, MacKenzie is able to scale up well to 10K CPU cores. Our modeling results show that, when compared to faster and accelerating vaccinations, slower vaccination rates due to vaccine hesitancy cause averted infections to drop from 6.7M to 4.5M, and averted total deaths to drop from 39.4K to 28.2K across the US. This occurs despite the fact that the final vaccine coverage is the same in both scenarios. We also find that if vaccine acceptance could be increased by 10% in all states, averted infections could be increased from 4.5M to 4.7M (a 4.4% improvement) and total averted deaths could be increased from 28.2K to 29.9K (a 6% improvement) nationwide.

Incorporating social determinants of health into transmission modeling of COVID-19 vaccine in the US: a scoping review

During COVID-19 in the US, social determinants of health (SDH) have driven health disparities. However, the use of SDH in COVID-19 vaccine modeling is unclear. This review aimed to summarize the current landscape of incorporating SDH into COVID-19 vaccine transmission modeling in the US. Medline and Embase were searched up to October 2022. We included studies that used transmission modeling to assess the effects of COVID-19 vaccine strategies in the US. Studies' characteristics, factors incorporated into models, and approaches to incorporate these factors were extracted. Ninety-two studies were included. Of these, 11 studies incorporated SDH factors (alone or combined with demographic factors). Various sets of SDH factors were integrated, with occupation being the most common (8 studies), followed by geographical location (5 studies). The results show that few studies incorporate SDHs into their models, highlighting the need for research on SDH impact and approaches to incorporating SDH into modeling.

Funding

This research was funded by the Centers for Disease Control and Prevention (CDC).

Effectiveness of Pfizer Vaccine BNT162b2 Against SARS-CoV-2 in Americans 16 and Older: A Systematic Review

This systematic review evaluates the efficacy and long-term effectiveness of the Pfizer-BioNTech COVID-19 vaccine (BNT162b2) across diverse clinical and observational settings within the United States in Americans aged 16 and older. We conducted an extensive literature search utilizing various types of studies to assess the vaccine's performance in preventing symptomatic SARS-CoV-2 infection and severe COVID-19 outcomes. Our initial search in PubMed on March 14, 2022, yielded 6,725 potentially relevant articles, with 26 undergoing full-text assessment and eight meeting the inclusion criteria. To incorporate the most up-to-date findings, a secondary search was conducted on July 6, 2024, using improved and refined Medical Subject Headings (MeSH) terms within the PubMed and Scopus databases. This expanded approach resulted in 78 potentially relevant articles from PubMed and 1,567 from Scopus, with 40 articles undergoing full-text assessment and an additional 14 articles meeting the inclusion criteria. Early clinical trials reported initial vaccine effectiveness (VE) up to 95% with sustained immunity in various populations. Observational studies and systematic reviews further confirmed VE above 90% against symptomatic infections and highlighted nearly complete protection against hospitalizations and deaths. Recent research underscores the critical role of booster doses in maintaining high VE, especially against emerging variants, showing restored effectiveness up to 95% and supporting their strategic importance in ongoing pandemic responses. Despite observed waning immunity and breakthrough infections, the BNT162b2 vaccine continues to exhibit robust protection across different demographic groups and under varying epidemiological conditions. Our findings advocate for continuous booster updates and adaptive vaccination strategies to manage emerging SARS-CoV-2 variants, reinforcing the pivotal role of mRNA vaccine technology in addressing global health emergencies.

Deviation from the recommended schedule: optimal dosing interval for a two-dose vaccination programme

Optimizing vaccination impact during an emerging disease becomes crucial when vaccine supply is limited, and robust protection requires multiple doses. Facing this challenge during the early stages of the COVID-19 vaccine deployment, a pivotal policy question arose: whether to administer a single dose to a larger proportion of the population by deferring the second dose, or to prioritize stronger protection for a smaller subset of the population with the established dosing interval from clinical trials. Using a delay-differential model and considering waning immunity and distribution capacity, we compared these strategies. We found that the efficacy of the first dose significantly influences the impact of delaying the second dose. Even for a relatively low efficacy of the first dose, a delayed strategy may outperform vaccination with the recommended dosing interval in reducing short-term hospitalizations and deaths despite increase in infections. The optimal delay, however, depends on the specific outcome measured and timelines within which the vaccination strategy is evaluated. We found transition lines for the relative reduction of infection, hospitalization and death below which vaccination with the recommended schedule is the preferred strategy. In a realistic parameter space, our results highlight scenarios in which the conclusions of previous studies are invalid.

A Phase 2 Clinical Trial to Evaluate the Safety, Reactogenicity, and Immunogenicity of Different Prime-Boost Vaccination Schedules of 2013 and 2017 A(H7N9) Inactivated Influenza Virus Vaccines Administered With and Without AS03 Adjuvant in Healthy US Adults

INTRODUCTION

A surge of human influenza A(H7N9) cases began in 2016 in China from an antigenically distinct lineage. Data are needed about the safety and immunogenicity of 2013 and 2017 A(H7N9) inactivated influenza vaccines (IIVs) and the effects of AS03 adjuvant, prime-boost interval, and priming effects of 2013 and 2017 A(H7N9) IIVs.

METHODS

Healthy adults (n = 180), ages 19-50 years, were enrolled into this partially blinded, randomized, multicenter phase 2 clinical trial. Participants were randomly assigned to 1 of 6 vaccination groups evaluating homologous versus heterologous prime-boost strategies with 2 different boost intervals (21 vs 120 days) and 2 dosages (3.75 or 15 μg of hemagglutinin) administered with or without AS03 adjuvant. Reactogenicity, safety, and immunogenicity measured by hemagglutination inhibition and neutralizing antibody titers were assessed.

RESULTS

Two doses of A(H7N9) IIV were well tolerated, and no safety issues were identified. Although most participants had injection site and systemic reactogenicity, these symptoms were mostly mild to moderate in severity; injection site reactogenicity was greater in vaccination groups receiving adjuvant. Immune responses were greater after an adjuvanted second dose, and with a longer interval between prime and boost. The highest hemagglutination inhibition geometric mean titer (95% confidence interval) observed against the 2017 A(H7N9) strain was 133.4 (83.6-212.6) among participants who received homologous, adjuvanted 3.75 µg + AS03/2017 doses with delayed boost interval.

CONCLUSIONS

Administering AS03 adjuvant with the second H7N9 IIV dose and extending the boost interval to 4 months resulted in higher peak antibody responses. These observations can broadly inform strategic approaches for pandemic preparedness. Clinical Trials Registration. NCT03589807.

Estimation of trajectory of protective efficacy in infectious disease prevention trials using recurrent event times

In studies of infectious disease prevention, the level of protective efficacy of medicinal products such as vaccines and prophylactic drugs tends to vary over time. Many products require administration of multiple doses at scheduled times, as opposed to one-off or continual intervention. Accurate information on the trajectory of the level of protective efficacy over time facilitates informed clinical recommendations and implementation strategies, for example, with respect to the timing of administration of the doses. Based on concepts from pharmacokinetic and pharmacodynamic modeling, we propose a non-linear function for modeling the trajectory after each dose. The cumulative effect of multiple doses of the products is captured by an additive series of the function. The model has the advantages of parsimony and interpretability, while remaining flexible in capturing features of the trajectories. We incorporate this series into the Andersen-Gill model for analysis of recurrent event time data and compare it with alternative parametric and non-parametric functions. We use data on clinical malaria disease episodes from a trial of four doses of an anti-malarial drug combination for chemoprevention to illustrate, and evaluate the performance of the methods using simulation. The proposed method out-performed the alternatives in the analysis of real data in terms of Akaike and Bayesian Information Criterion. It also accurately captured the features of the protective efficacy trajectory such as the area under curve in simulations. The proposed method has strong potential to enhance the evaluation of disease prevention measures and improve their implementation strategies.

Vaccination strategies impact the probability of outbreak extinction: A case study of COVID-19 transmission

Mass vaccination has proven to be an effective control measure for mitigating the transmission of infectious diseases. Throughout history, various vaccination strategies have been employed to control infections and terminate outbreaks. In this study, we utilized the transmission of COVID-19 as a case study and constructed a stochastic age-structured compartmental model to investigate the effectiveness of different vaccination strategies. Our analysis focused on estimating the outbreak extinction probability under different vaccination scenarios in both homogeneous and heterogeneous populations. Notably, we found that population heterogeneity can enhance the likelihood of outbreak extinction at varying levels of vaccine coverage. Prioritizing vaccinations for individuals with higher infection risk was found to maximize outbreak extinction probability and reduce overall infections, while allocating vaccines to those with higher mortality risk has been proven more effective in reducing deaths. Moreover, our study highlighted the significance of booster doses as the vaccine effectiveness wanes over time, showing that they can significantly enhance the extinction probability and mitigate disease transmission.

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.

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.

The effectiveness of single and two-dose Pfizer-BioNTech vaccine against SARS-COV-2: A real-world evidence from Saudi Arabia

BACKGROUND

Several studies proved the effectiveness of Severe Acute Respiratory Syndrome Corona Virus (SARS-CoV-2) vaccines; however, the number of doses and the period between doses that warrant the highest protection remain unclear. This study aims to assess the effectiveness of the Pfizer-BioNTech vaccine and to evaluate the effectiveness of early and delayed second-dose administration of the vaccine.

METHODS

This is a retrospective cohort study that was conducted using the data from March 1st, 2021, to August 31st, 2021. Data regarding vaccination coverage and confirmed SARS-CoV-2 infection were obtained using academic hospitals databases and Health Electronic Surveillance Network (HESN) platform. The vaccination status of the participants were categorized as: unvaccinated, vaccinated 1st dose, and vaccinated 2nd dose of Pfizer-BioNTech vaccine. The outcome of interest was positive polymerase chain reaction test for SARS-CoV-2. Generalized linear model with a Poisson distribution was used to estimate the incidence of the infection.

FINDINGS

Among 66,775 participants included, 2615 SARS-CoV-2 infections were observed. The sample was relatively young with median age of 22 years and 43% female. A single dose of Pfizer-BioNTech vaccine had 40 % effectiveness. The effectiveness of the vaccine was doubled after the second dose of Pfizer-BioNTech (80 %). The time between the first and the second dose appears to be crucial after observing 75 %, 90 % and 85 % effectiveness with early vaccination, on-time vaccination, and delayed vaccination, respectively.

CONCLUSION

For Pfizer-BioNTech vaccine recipients in Saudi Arabia, particularly among a predominantly young population, higher effectiveness against SARS-CoV-2 was observed with two doses of the vaccine. The timing of the second dose appears crucial for the extent of protection against SARS-CoV-2. However, potential residual confounding cannot be discounted, and further studies are needed to validate these findings and improve generalizability.

The role of immune suppression in COVID-19 hospitalization: clinical and epidemiological trends over three years of SARS-CoV-2 epidemic

Specific immune suppression types have been associated with a greater risk of severe COVID-19 disease and death. We analyzed data from patients >17 years that were hospitalized for COVID-19 at the "Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico" in Milan (Lombardy, Northern Italy). The study included 1727 SARS-CoV-2-positive patients (1,131 males, median age of 65 years) hospitalized between February 2020 and November 2022. Of these, 321 (18.6%, CI: 16.8-20.4%) had at least one condition defining immune suppression. Immune suppressed subjects were more likely to have other co-morbidities (80.4% vs. 69.8%, p < 0.001) and be vaccinated (37% vs. 12.7%, p < 0.001). We evaluated the contribution of immune suppression to hospitalization during the various stages of the epidemic and investigated whether immune suppression contributed to severe outcomes and death, also considering the vaccination status of the patients. The proportion of immune suppressed patients among all hospitalizations (initially stable at <20%) started to increase around December 2021, and remained high (30-50%). This change coincided with an increase in the proportions of older patients and patients with co-morbidities and with a decrease in the proportion of patients with severe outcomes. Vaccinated patients showed a lower proportion of severe outcomes; among non-vaccinated patients, severe outcomes were more common in immune suppressed individuals. Immune suppression was a significant predictor of severe outcomes, after adjusting for age, sex, co-morbidities, period of hospitalization, and vaccination status (OR: 1.64; 95% CI: 1.23-2.19), while vaccination was a protective factor (OR: 0.31; 95% IC: 0.20-0.47). However, after November 2021, differences in disease outcomes between vaccinated and non-vaccinated groups (for both immune suppressed and immune competent subjects) disappeared. Since December 2021, the spread of the less virulent Omicron variant and an overall higher level of induced and/or natural immunity likely contributed to the observed shift in hospitalized patient characteristics. Nonetheless, vaccination against SARS-CoV-2, likely in combination with naturally acquired immunity, effectively reduced severe outcomes in both immune competent (73.9% vs. 48.2%, p < 0.001) and immune suppressed (66.4% vs. 35.2%, p < 0.001) patients, confirming previous observations about the value of the vaccine in preventing serious disease.

Study of optimal vaccination strategies for early COVID-19 pandemic using an age-structured mathematical model: A case study of the USA

In this paper we study different vaccination strategies that could have been implemented for the early COVID-19 pandemic. We use a demographic epidemiological mathematical model based on differential equations in order to investigate the efficacy of a variety of vaccination strategies under limited vaccine supply. We use the number of deaths as the metric to measure the efficacy of each of these strategies. Finding the optimal strategy for the vaccination programs is a complex problem due to the large number of variables that affect the outcomes. The constructed mathematical model takes into account demographic risk factors such as age, comorbidity status and social contacts of the population. We perform simulations to assess the performance of more than three million vaccination strategies which vary depending on the vaccine priority of each group. This study focuses on the scenario corresponding to the early vaccination period in the USA, but can be extended to other countries. The results of this study show the importance of designing an optimal vaccination strategy in order to save human lives. The problem is extremely complex due to the large amount of factors, high dimensionality and nonlinearities. We found that for low/moderate transmission rates the optimal strategy prioritizes high transmission groups, but for high transmission rates, the optimal strategy focuses on groups with high CFRs. The results provide valuable information for the design of optimal vaccination programs. Moreover, the results help to design scientific vaccination guidelines for future pandemics.

COVID-19 Vaccination and Healthcare Demand

One of the driving concerns during any epidemic is the strain on the healthcare system. As we have seen many times over the globe with the COVID-19 pandemic, hospitals and ICUs can quickly become overwhelmed by cases. While strict periods of public health mitigation have certainly helped decrease incidence and thus healthcare demand, vaccination is the only clear long-term solution. In this paper, we develop a two-module model to forecast the effects of relaxation of non-pharmaceutical intervention and vaccine uptake on daily incidence, and the cascade effects on healthcare demand. The first module is a simple epidemiological model which incorporates non-pharmaceutical intervention, the relaxation of such measures and vaccination campaigns to predict caseloads into the Fall of 2021. This module is then fed into a healthcare module which can forecast the number of doctor visits, the number of occupied hospital beds, number of occupied ICU beds and any excess demand of these. From this module, we can also estimate the length of stay of individuals in ICU. For model verification and forecasting, we use the four most populous Canadian provinces as a case study.

Quantifying the effect of delaying the second COVID-19 vaccine dose in England: a mathematical modelling study

BACKGROUND

The UK was the first country to start national COVID-19 vaccination programmes, initially administering doses 3 weeks apart. However, early evidence of high vaccine effectiveness after the first dose and the emergence of the SARS-CoV-2 alpha variant prompted the UK to extend the interval between doses to 12 weeks. In this study, we aimed to quantify the effect of delaying the second vaccine dose in England.

METHODS

We used a previously described model of SARS-CoV-2 transmission, calibrated to COVID-19 surveillance data from England, including hospital admissions, hospital occupancy, seroprevalence data, and population-level PCR testing data, using a Bayesian evidence-synthesis framework. We modelled and compared the epidemic trajectory in the counterfactual scenario in which vaccine doses were administered 3 weeks apart against the real reported vaccine roll-out schedule of 12 weeks. We estimated and compared the resulting numbers of daily infections, hospital admissions, and deaths. In sensitivity analyses, we investigated scenarios spanning a range of vaccine effectiveness and waning assumptions.

FINDINGS

In the period from Dec 8, 2020, to Sept 13, 2021, the number of individuals who received a first vaccine dose was higher under the 12-week strategy than the 3-week strategy. For this period, we estimated that delaying the interval between the first and second COVID-19 vaccine doses from 3 to 12 weeks averted a median (calculated as the median of the posterior sample) of 58 000 COVID-19 hospital admissions (291 000 cumulative hospitalisations [95% credible interval 275 000-319 000] under the 3-week strategy vs 233 000 [229 000-238 000] under the 12-week strategy) and 10 100 deaths (64 800 deaths [60 200-68 900] vs 54 700 [52 800-55 600]). Similarly, we estimated that the 3-week strategy would have resulted in more infections compared with the 12-week strategy. Across all sensitivity analyses the 3-week strategy resulted in a greater number of hospital admissions. In results by age group, the 12-week strategy led to more hospitalisations and deaths in older people in spring 2021, but fewer following the emergence of the delta variant during summer 2021.

INTERPRETATION

England's delayed-second-dose vaccination strategy was informed by early real-world data on vaccine effectiveness in the context of limited vaccine supplies in a growing epidemic. Our study shows that rapidly providing partial (single-dose) vaccine-induced protection to a larger proportion of the population was successful in reducing the burden of COVID-19 hospitalisations and deaths overall.

FUNDING

UK National Institute for Health Research; UK Medical Research Council; Community Jameel; Wellcome Trust; UK Foreign, Commonwealth and Development Office; Australian National Health and Medical Research Council; and EU.

Modelling non-linear patterns of time-varying intervention effects on recurrent events in infectious disease prevention studies

Protective efficacy of vaccines and pharmaceutical products for prevention of infectious diseases usually vary over time. Information on the trajectory of the level of protection is valuable. We consider a parsimonious, non-linear and non-monotonic function for modelling time-varying intervention effects and compare it with several alternatives. The cumulative effects of multiple doses of intervention over time can be captured by an additive series of the function. We apply it to the Andersen-Gill model for analysis of recurrent time-to-event data. We re-analyze data from a trial of intermittent preventive treatment for malaria to illustrate and evaluate the method by simulation.

Modeling the impact of combined use of COVID Alert SA app and vaccination to curb COVID-19 infections in South Africa

The unanticipated continued deep-rooted trend of the Severe Acute Respiratory Syndrome Corona-virus-2 the originator pathogen of the COVID-19 persists posing concurrent anxiety globally. More effort is affixed in the scientific arena via continuous investigations in a prolific effort to understand the transmission dynamics and control measures in eradication of the epidemic. Both pharmaceutical and non-pharmaceutical containment measure protocols have been assimilated in this effort. In this study, we develop a modified SEIR deterministic model that factors in alternative-amalgamation of use of COVID Alert SA app and vaccination against the COVID-19 to the Republic of South Africa's general public in an endeavor to discontinue the chain of spread for the pandemic. We analyze the key properties of the model not limited to positivity, boundedness, and stability. We authenticate the model by fitting it to the Republic of South Africa's cumulative COVID-19 cases reported data utilizing the Maximum Likelihood Estimation algorithm implemented in fitR package. Sensitivity analysis and simulations for the model reveal that simultaneously-gradually increased implementation of the COVID Alert SA app use and vaccination against COVID-19 to the public substantially accelerate reduction in the plateau number of COVID-19 infections across all the observed vaccine efficacy scenarios. More fundamentally, it is discovered that implementing at least 12% app use (mainly for the susceptible population not vaccinated) with simultaneous vaccination of over 12% of the susceptible population majorly not using the app using a vaccine of at least 50% efficacy would be sufficient in eradicating the pandemic over relatively shorter time span.

Dynamic planning of a two-dose vaccination campaign with uncertain supplies

The ongoing COVID-19 pandemic has led public health authorities to face the unprecedented challenge of planning a global vaccination campaign, which for most protocols entails the administration of two doses, separated by a bounded but flexible time interval. The partial immunity already offered by the first dose and the high levels of uncertainty in the vaccine supplies have been characteristic of most of the vaccination campaigns implemented worldwide and made the planning of such interventions extremely complex. Motivated by this compelling challenge, we propose a stochastic optimization framework for optimally scheduling a two-dose vaccination campaign in the presence of uncertain supplies, taking into account constraints on the interval between the two doses and on the capacity of the healthcare system. The proposed framework seeks to maximize the vaccination coverage, considering the different levels of immunization obtained with partial (one dose only) and complete vaccination (two doses). We cast the optimization problem as a convex second-order cone program, which can be efficiently solved through numerical techniques. We demonstrate the potential of our framework on a case study calibrated on the COVID-19 vaccination campaign in Italy. The proposed method shows good performance when unrolled in a sliding-horizon fashion, thereby offering a powerful tool to help public health authorities calibrate the vaccination campaign, pursuing a trade-off between efficacy and the risk associated with shortages in supply.

Efficient social distancing during the COVID-19 pandemic: Integrating economic and public health considerations

Although social distancing can effectively contain the spread of infectious diseases by reducing social interactions, it may have economic effects. Crises such as the COVID-19 pandemic create dilemmas for policymakers because the long-term implementation of restrictive social distancing policies may cause massive economic damage and ultimately harm healthcare systems. This paper proposes an epidemic control framework that policymakers can use as a data-driven decision support tool for setting efficient social distancing targets. The framework addresses three aspects of the COVID-19 pandemic that are related to social distancing or community mobility data: modeling, financial implications, and policy-making. Thus, we explore the COVID-19 pandemic and concurrent economic situation as functions of historical pandemic data and mobility control. This approach allows us to formulate an efficient social distancing policy as a stochastic feedback control problem that minimizes the aggregated risks of disease transmission and economic volatility. We further demonstrate the use of a deep learning algorithm to solve this control problem. Finally, by applying our framework to U.S. data, we empirically examine the efficiency of the U.S. social distancing policy.

The long-term antibody response after SARS-CoV-2 prime-boost vaccination in healthy individuals. The positive influence of extended between-dose intervals and heterologous schedule

Introduction

Anti-COVID vaccination in Argentina was carried out using different protocols and variations in periods between administrations, as well as combinations of different vaccine platforms. Considering the relevance of the antibody response in viral infections, we analyzed anti-S antibodies in healthy people at different points of time following the Sputnik immunization procedure.

Methods

We attended the vaccination centers in the city of Rosario, which had shorter versus longer intervals between both doses. A total of (1021) adults with no COVID-compatible symptoms (throughout the study period) were grouped according to the gap between both vaccine doses: 21 (Group A, n=528), 30 (Group B, n=147), and 70 days (Group C, n=82), as well as an additional group of individuals with heterologous vaccination (Sputnik/Moderna, separated by a 107-day interval, group D, n=264).

Results and conclusions

While there were no between-group differences in baseline levels of specific antibodies, data collected several weeks after administering the second dose showed that group D had the highest amounts of specific antibodies, followed by values recorded in Groups C, B, and A. The same pattern of group differences was seen when measuring anti-S antibodies at 21 or 180 days after the first and second doses, respectively. Delayed between-dose intervals coexisted with higher antibody titers. This happened even more when using a prime-boost heterologous schedule.

COVID-19 vaccine hesitancy among Italian people with multiple sclerosis

INTRODUCTION

Vaccine hesitancy promotes the spread of infectious diseases including COVID-19 virus, limiting the herd immunity. Complications caused by COVID-19 in people with multiple sclerosis forced governments to ensure them prior access to vaccinations. Their propensity to be vaccinated needs to be assessed to promote adhesion to vaccination programs. The aim of this study was to explore the COVID-19 vaccine hesitancy rate in pwMS.

METHODS

We conducted an observational study recruiting patients affected by multiple sclerosis followed at MS Clinical and Research Unit of Tor Vergata University, Rome. We invited them to fill in an online survey about their intent to get COVID-19 vaccination. Fisher's exact test and Kruskal-Wallis test were performed to explore differences in sociodemographic, clinical, and emotional variables relative to the opinions about vaccinations. An exploratory factor analysis (EFA) was performed to assess the factorial structure of the questionnaire; Pearson's correlations between the factors and Big Five personality dimensions were also calculated.

RESULTS

Of 276 respondents, 90% was willing to get vaccinated, while only 1.4% was sure to refuse the vaccination. Education level, opinions on safety and efficacy of vaccines, and emotional status were found to be associated to the propensity of getting the COVID-19 vaccination (respectively: p = 0.012, p < 0.001, and p = 0.0001). Moreover, general opinions on healthcare system were related to the intention to get vaccinated.

CONCLUSION

Our results reinforce the importance of a good relationship between doctor and patient and the need to adapt doctors' communication strategy to patients' personalities and beliefs.

Vaccine Allocation and Distribution: A Review with a Focus on Quantitative Methodologies and Application to Equity, Hesitancy, and COVID-19 Pandemic

This review focuses on vaccine distribution and allocation in the context of the current COVID-19 pandemic. The implications discussed are in the areas of equity in vaccine distribution and allocation (at a national level as well as worldwide), vaccine hesitancy, game-theoretic modeling to guide decision-making and policy-making at a governmental level, distribution and allocation barriers (in particular in low-income countries), and operations research (OR) mathematical models to plan and execute vaccine distribution and allocation. To conduct this review, we adopt a novel methodology that consists of three phases. The first phase deploys a bibliometric analysis; the second phase concentrates on a network analysis; and the last phase proposes a refined literature review based on the results obtained by the previous two phases. The quantitative techniques utilized to conduct the first two phases allow describing the evolution of the research in this area and its potential ramifications in future. In conclusion, we underscore the significance of operations research (OR)/management science (MS) research in addressing numerous challenges and trade-offs connected to the current pandemic and its strategic impact in future research.

Determining the Optimal SARS-CoV-2 mRNA Vaccine Dosing Interval for Maximum Immunogenicity

OBJECTIVE

Emerging evidence indicates that longer SARS-CoV-2 vaccine dosing intervals results in an enhanced immune response. However, the optimal vaccine dosing interval for achieving maximum immunogenicity is unclear.

METHODS

This study included samples from adult paramedics in Canada who received two doses of either BNT162b2 or mRNA-1273 vaccines and provided blood samples six months (170 to 190 days) after the first vaccine dose. The main exposure variable was vaccine dosing interval (days), categorized as "short" (first quartile), "moderate" (second quartile), "long" (third quartile), and "longest" interval (fourth quartile). The primary outcome was total spike antibody concentrations, measured using the Elecsys SARS-CoV-2 total antibody assay. Secondary outcomes included spike and receptor-binding domain (RBD) immunoglobulin G (IgG) antibody concentrations, and inhibition of angiotensin-converting enzyme 2 (ACE-2) binding to wild-type spike protein and several different Delta variant spike proteins. We fit a multiple log-linear regression model to investigate the association between vaccine dosing intervals and the antibody concentrations.

RESULTS

A total of 564 adult paramedics (mean age 40 years, SD=10) were included. Compared to "short interval" (≤30 days), vaccine dosing intervals of the long (39-73 days) group (β= 0.31, 95% Confidence interval (CI): 0.10-0.52) and the longest (≥74 days) group (β = 0.82. 95% CI: 0.36-1.28) were associated with increased spike total antibody concentration. Compared to the short interval, the longest interval quartile was associated with higher spike IgG antibodies, while the long and longest intervals were associated with higher RBD IgG antibody concentrations. Similarly, the longest dosing intervals increased inhibition of ACE-2 binding to viral spike protein.

CONCLUSION

Increased mRNA vaccine dosing intervals longer than 38 days result in higher levels of anti-spike antibodies and ACE-2 inhibition when assessed six months after the first COVID-19 vaccine.

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.

Beyond neutralization: Fc-dependent antibody effector functions in SARS-CoV-2 infection

Neutralizing antibodies are known to have a crucial role in protecting against SARS-CoV-2 infection and have been suggested to be a useful correlate of protection for vaccine clinical trials and for population-level surveys. In addition to neutralizing virus directly, antibodies can also engage immune effectors through their Fc domains, including Fc receptor-expressing immune cells and complement. The outcome of these interactions depends on a range of factors, including antibody isotype-Fc receptor combinations, Fc receptor-bearing cell types and antibody post-translational modifications. A growing body of evidence has shown roles for these Fc-dependent antibody effector functions in determining the outcome of SARS-CoV-2 infection. However, measuring these functions is more complicated than assays that measure antibody binding and virus neutralization. Here, we examine recent data illuminating the roles of Fc-dependent antibody effector functions in the context of SARS-CoV-2 infection, and we discuss the implications of these data for the development of next-generation SARS-CoV-2 vaccines and therapeutics.

B-Cell Responses to Sars-Cov-2 mRNA Vaccines

Most vaccines against viral pathogens protect through the acquisition of immunological memory from long-lived plasma cells that produce antibodies and memory B cells that can rapidly respond upon an encounter with the pathogen or its variants. The COVID-19 pandemic and rapid deployment of effective vaccines have provided an unprecedented opportunity to study the immune response to a new yet rapidly evolving pathogen. Here we review the scientific literature and our efforts to understand antibody and B-cell responses to SARS-CoV-2 vaccines, the effect of SARSCoV-2 infection on both primary and secondary immune responses, and how repeated exposures may impact outcomes.

Bi-objective optimization of a stochastic resilient vaccine distribution network in the context of the COVID-19 pandemic

This paper develops an approach to optimize a vaccine distribution network design through a mixed-integer nonlinear programming model with two objectives: minimizing the total expected number of deaths among the population and minimizing the total distribution cost of the vaccination campaign. Additionally, we assume that a set of input parameters (e.g., death rate, social contacts, vaccine supply, etc.) is uncertain, and the distribution network is exposed to disruptions. We then investigate the resilience of the distribution network through a scenario-based robust-stochastic optimization approach. The proposed model is linearized and finally validated through a real case study of the COVID-19 vaccination campaign in France. We show that the current vaccination strategies are not optimal, and vaccination prioritization among the population and the equity of vaccine distribution depend on other factors than those conceived by health policymakers. Furthermore, we demonstrate that a vaccination strategy mixing the population prioritization and the quarantine restrictions leads to an 8.5% decrease in the total number of deaths.

Progress Update on the Epidemiology of COVID-19 Variants and the Assessment Status of Developed Vaccines

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has spread rapidly and diffused to more than 180 countries at varying severities. This pandemic has accounted for increased mortality and morbidity in developed as well as developing nations. The WHO has announced that there is a persistent need for the evaluation of the COVID-19 vaccine effectiveness (VE) against major outcomes, which include severe diseases, symptomatic COVID-19, and mortalities related to COVID-19. Therefore, mass vaccination programs using vaccines of high effectiveness are among the strategies that have been used by governments worldwide to impede the COVID-19 pandemic transmission. In this regard, massive efforts were made by governments, scientists, biomedical researchers, and healthcare professionals leading to the successful development of various vaccines to bring this pandemic under control. This editorial aims to shed light on the epidemiological status of COVID-19 variants, namely, Delta, Omicron, and Deltacron variants as well as discuss the effectiveness of the currently available COVID-19 vaccines.

COVID-19 vaccination challenges: A mini-review

The emergence of SARS-CoV-2 has led to the infection of many people across the globe, over six million deaths, and has placed an unprecedented burden on public health worldwide. The pandemic has led to the high-speed development and production of vaccines against the COVID-19, as vaccines can end the pandemic. At the beginning of the program, vaccinations were initially targeted only at high-risk groups, such as the elderly, those with comorbidities, or healthcare workers. Although most of the mentioned populations have received the two recommended doses, limited resources have left many authorities with an effective vaccine undersupply. Therefore, policies have been implemented to manage the available doses of the vaccines more efficiently. As there is no universally agreed consensus on this topic, we discuss the different recommendations and guidelines regarding the time interval between the two vaccine doses and explain the different scenarios for applying the two doses.

Modeling COVID-19 transmission between age groups in the United States considering virus mutations, vaccinations, and reinfection

The in-depth understanding of the dynamics of COVID-19 transmission among different age groups is of great interest for governments and health authorities so that strategies can be devised to reduce the pandemic's detrimental effects. We developed the SIRDV-Virulence (Susceptible-Infected-Recovered-Dead-Vaccinated-Virulence) epidemiological model based on a population balance equation to study the effects virus mutants, vaccination strategies, 'Anti/Non Vaxxer' proportions, and reinfection rates to provide methods to mitigate COVID-19 transmission among the United States population. Based on publicly available data, we obtain the key parameters governing the spread of the pandemic. The results show that a large fraction of infected cases comes from the adult and children populations in the presence of a highly infectious COVID-19 mutant. Given the situation at the end of July 2021, the results show that prioritizing children and adult vaccinations over that of seniors can contain the spread of the active cases, thereby preventing the healthcare system from being overwhelmed and minimizing subsequent deaths. The model suggests that the only option to curb the effects of this pandemic is to reduce the population of unvaccinated individuals. A higher fraction of 'Anti/Non-vaxxers' and a higher reinfection rate can both independently lead to the resurgence of the pandemic.

Modelling optimal vaccination strategies against COVID-19 in a context of Gamma variant predominance in Brazil

INTRODUCTION

Brazil experienced moments of collapse in its health system throughout 2021, driven by the emergence of variants of concern (VOC) combined with an inefficient initial vaccination strategy against Covid-19.

OBJECTIVES

To support decision-makers in formulating COVID-19 immunization policy in the context of limited vaccine availability and evolving variants over time, we evaluate optimal strategies for Covid-19 vaccination in Brazil in 2021, when vaccination was rolled out during Gamma variant predominance.

METHODS

Using a discrete-time epidemic model we estimate Covid-19 deaths averted, considering the currently Covid-19 vaccine products and doses available in Brazil; vaccine coverage by target population; and vaccine effectiveness estimates. We evaluated a 5-month time horizon, from early August to the end of December 2021. Optimal vaccination strategies compared the outcomes in terms of averted deaths when varying dose intervals from 8 to 12 weeks, and choosing the minimum coverage levels per age group required prior to expanding vaccination to younger target populations. We also estimated dose availability required over time to allow the implementation of optimal strategies.

RESULTS

To maximize the number of averted deaths, vaccine coverage of at least 80 % should be reached in older age groups before starting vaccination into subsequent younger age groups. When evaluating varying dose intervals for AZD1222, reducing the dose interval from 12 to 8 weeks for the primary schedule would result in fewer COVID-19 deaths, but this can only be implemented if accompanied by an increase in vaccine supply of at least 50 % over the coming six-months in Brazil.

CONCLUSION

Covid-19 immunization strategies should be tailored to local vaccine product availability and supply over time, circulating variants of concern, and vaccine coverage in target population groups. Modelling can provide valuable and timely evidence to support the implementation of vaccination strategies considering the local context, yet following international and regional technical evidence-based guidance.

Effect of Vaccination Time Intervals on SARS-COV-2 Omicron Variant Strain Infection in Guangzhou: A Real-World Matched Case–Control Study

In April 2022, a COVID-19 outbreak caused by the Omicron variant emerged in Guangzhou. A case–control study was conducted to explore the relationship between vaccination intervals and SARS-CoV-2 infection in the real world. According to the vaccination dose and age information of the cases, a 1:4 matched case–control sample was established, finally including n = 242 for the case group and n = 968 for the control group. The results indicated that among the participants who received three vaccine doses, those with an interval of more than 300 days between the receipt of the first vaccine dose and infection (or the first contact with a confirmed case) were less likely to be infected with SARS-CoV-2 than those with an interval of less than 300 days (OR = 0.67, 95% CI = 0.46–0.99). After age-stratified analysis, among participants aged 18–40 years who received two doses of vaccine, those who received the second dose more than 30 days after the first dose were less likely to be infected with SARS-CoV-2 (OR = 0.53, 95% CI = 0.30–0.96). Our findings suggest that we need to extend the interval between the first dose and the second dose and further explore the optimal interval between the first and second and between the second and third doses in order to improve vaccine efficacy.

A simulation-deep reinforcement learning (SiRL) approach for epidemic control optimization

In this paper, we address the controversies of epidemic control planning by developing a novel Simulation-Deep Reinforcement Learning (SiRL) model. COVID-19 reminded constituents over the world that government decision-making could change their lives. During the COVID-19 pandemic, governments were concerned with reducing fatalities as the virus spread but at the same time also maintaining a flowing economy. In this paper, we address epidemic decision-making regarding the interventions necessary given of the epidemic based on the purpose of the decision-maker. Further, we intend to compare different vaccination strategies, such as age-based and random vaccination, to shine a light on who should get priority in the vaccination process. To address these issues, we propose a simulation-deep reinforcement learning (DRL) framework. This framework is composed of an agent-based simulation model and a governor DRL agent that can enforce interventions in the agent-based simulation environment. Computational results show that our DRL agent can learn effective strategies and suggest optimal actions given a specific epidemic situation based on a multi-objective reward structure. We compare our DRL agent's decisions to government interventions at different periods of time during the COVID-19 pandemic. Our results suggest that more could have been done to control the epidemic. In addition, if a random vaccination strategy that allows super-spreaders to get vaccinated early were used, infections would have been reduced by 32% at the expense of 4% more deaths. We also show that a behavioral change of fully quarantining 10% of the risky individuals and using a random vaccination strategy leads to a reduction of the death toll by 14% and 27% compared to the age-based vaccination strategy that was implemented and the New Jersey reported data, respectively. We have also demonstrated the flexibility of our approach to be applied to other locations by validating and applying our model to the COVID-19 case in the state of Kansas.

Optimizing global COVID-19 vaccine allocation: An agent-based computational model of 148 countries

BACKGROUND

Based on the principles of equity and effectiveness, the World Health Organization and COVAX formulate vaccine allocation as a mathematical optimization problem. This study aims to solve the optimization problem using agent-based simulations.

METHODS

We built open-sourced agent-based models to simulate virus transition among a demographically representative sample of 198 million people in 148 countries using advanced computational services. All countries continuing their current vaccine progress is defined as the baseline scenario. Comparison scenarios include achieving minimum vaccination rates and allocating vaccines based on pandemic levels.

FINDINGS

The simulations are fitted using the pandemic data from 148 countries from January 2020 to June 2021. Under the baseline scenario, the world will add 24.36 million cases and 468,945 deaths during the projection period of three months. Inoculating at least 10%, 20%, and 26% of populations in all countries requires 1.12, 3.31, and 5.00 million additional vaccine doses every day, respectively. Achieving these benchmarks reduces new cases by 0.56, 2.74, and 3.32 million, respectively. If allocated by the current global distribution, 5.00 million additional vaccine doses will only avert 1.45 million new cases. If those 5.00 million vaccines are allocated based on projected cases in each country, the averted cases will increase more than six-fold to 9.20 million. Similar differences between allocation methods are observed in averted deaths.

CONCLUSION

The global distribution of COVID-19 vaccines can be optimized to achieve better outcomes in terms of both equity and effectiveness. Alternative vaccine allocation methods may avert several times more cases and deaths than the current global distribution. With reasonable requirements on additional vaccines, COVAX could adopt alternative allocation strategies that reduce cross-country inequity and save more lives.

Age and product dependent vaccine effectiveness against SARS-CoV-2 infection and hospitalisation among adults in Norway: a national cohort study, July-November 2021

BACKGROUND

COVID-19 vaccines have been crucial in the pandemic response and understanding changes in vaccines effectiveness is essential to guide vaccine policies. Although the Delta variant is no longer dominant, understanding vaccine effectiveness properties will provide essential knowledge to comprehend the development of the pandemic and estimate potential changes over time.

METHODS

In this population-based cohort study, we estimated the vaccine effectiveness of Comirnaty (Pfizer/BioNTech; BNT162b2), Spikevax (Moderna; mRNA-1273), Vaxzevria (AstraZeneca; ChAdOx nCoV-19; AZD1222), or a combination against SARS-CoV-2 infections, hospitalisations, intensive care admissions, and death using Cox proportional hazard models, across different vaccine product regimens and age groups, between 15 July and 31 November 2021 (Delta variant period). Vaccine status is included as a time-varying covariate and all models were adjusted for age, sex, comorbidities, county of residence, country of birth, and living conditions. Data from the entire adult Norwegian population were collated from the National Preparedness Register for COVID-19 (Beredt C19).

RESULTS

The overall adjusted vaccine effectiveness against infection decreased from 81.3% (confidence interval (CI): 80.7 to 81.9) in the first 2 to 9 weeks after receiving a second dose to 8.6% (CI: 4.0 to 13.1) after more than 33 weeks, compared to 98.6% (CI: 97.5 to 99.2) and 66.6% (CI: 57.9 to 73.6) against hospitalisation respectively. After the third dose (booster), the effectiveness was 75.9% (CI: 73.4 to 78.1) against infection and 95.0% (CI: 92.6 to 96.6) against hospitalisation. Spikevax or a combination of mRNA products provided the highest protection, but the vaccine effectiveness decreased with time since vaccination for all vaccine regimens.

CONCLUSIONS

Even though the vaccine effectiveness against infection waned over time, all vaccine regimens remained effective against hospitalisation after the second vaccine dose. For all vaccine regimens, a booster facilitated recovery of effectiveness. The results from this support the use of heterologous schedules, increasing flexibility in vaccination policy.

Third booster vaccination and stopping the Omicron, a new variant of concern

The SARS-CoV-2 omicron variant is recent member of variant of concerns that confer neutralizing antibodies and escape immune system due to harboring more than 40 mutations. Current evidences suggest two dosages SARS-CoV-2 vaccine dose not efficient protects against new variants of SARS-CoV-2; however, recent studies declare that the third booster vaccination can elicit higher antibodies concentration as well as cross-reaction between neutralizing antibodies and new SARS-CoV-2 variants. On the other hand, although a third booster vaccination seems to be benefit in some immunocompromised patients such as recipients of solid-organ transplants or hemodialysis patients, but in other immunosuppressed patients, for instance patients with B cell lymphoproliferative disease partially respond to SARS-CoV-2. Herein, we evaluate the effectiveness of the third booster vaccination against Omicron variant using comprehensive literature review.

Antibodies against SARS-CoV-2 Alpha, Beta, and Gamma Variants in Pregnant Women and Their Neonates under Antenatal Vaccination with Moderna (mRNA-1273) Vaccine

The aim of the study was to examine the impact of COVID-19 vaccination on the anti-SARS-CoV-2 spike receptor binding domain IgG antibody (SRBD IgG) binding ratio (SBR) from Alpha, Beta, and Gamma variants of SARS-CoV-2 in pregnant women and neonates. The impact of antenatal influenza (flu) and pertussis (Tdap) vaccines was also studied. We enrolled pregnant women vaccinated with the Moderna (mRNA-1273) vaccine during pregnancy and collected maternal plasma (MP) and neonatal cord blood (CB) during delivery to determine the SBR via enzyme-linked immunosorbent assays (ELISA). A total of 78 samples were collected from 39 pregnant women. The SBR was higher for Alpha variants compared to Beta/Gamma variants (MP: 63.95% vs. 47.91% vs. 43.48%, p = 0.0001; CB: 72.14% vs. 56.78% vs. 53.66%, p = 0.006). Pregnant women receiving two doses of the COVID-19 vaccine demonstrated a better SBR against SARS-CoV-2 Alpha, Beta, and Gamma variants than women receiving just a single dose. Women who received the Tdap/flu vaccines demonstrated a better SBR when two COVID-19 vaccine doses were < 6 weeks apart. A better SBR was detected among women who had more recently received their second COVID-19 vaccine dose. Two doses of the COVID-19 vaccine provided recipients with a better SBR for Alpha/Beta/Gamma variants. Although Tdap/flu vaccines may affect the efficacy of the COVID-19 vaccine, different vaccination timings can improve the SBR.

A Higher Antibody Response Is Generated With a 6- to 7-Week (vs Standard) Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Vaccine Dosing Interval

The optimal dosing interval for severe acute respiratory syndrome coronavirus 2 vaccines remains controversial. In this prospective study, we compared serology results of paramedics vaccinated with mRNA vaccines at the recommended short (17-28 days) vs long (42-49 days) interval. We found that a long dosing interval resulted in higher spike, receptor binding domain, and spike N terminal domain antibody concentrations.

Lower vaccine-acquired immunity in the elderly population following two-dose BNT162b2 vaccination is alleviated by a third vaccine dose

Understanding the impact of age on vaccinations is essential for the design and delivery of vaccines against SARS-CoV-2. Here, we present findings from a comprehensive analysis of multiple compartments of the memory immune response in 312 individuals vaccinated with the BNT162b2 SARS-CoV-2 mRNA vaccine. Two vaccine doses induce high antibody and T cell responses in most individuals. However, antibody recognition of the Spike protein of the Delta and Omicron variants is less efficient than that of the ancestral Wuhan strain. Age-stratified analyses identify a group of low antibody responders where individuals ≥60 years are overrepresented. Waning of the antibody and cellular responses is observed in 30% of the vaccinees after 6 months. However, age does not influence the waning of these responses. Taken together, while individuals ≥60 years old take longer to acquire vaccine-induced immunity, they develop more sustained acquired immunity at 6 months post-vaccination. A third dose strongly boosts the low antibody responses in the older individuals against the ancestral Wuhan strain, Delta and Omicron variants.

COVID-19 vaccine distribution planning using a congested queuing system-A real case from Australia

Crisis-induced vaccine supply chain management has recently drawn attention to the importance of immediate responses to a crisis (e.g., the COVID-19 pandemic). This study develops a queuing model for a crisis-induced vaccine supply chain to ensure efficient coordination and distribution of different COVID-19 vaccine types to people with various levels of vulnerability. We define a utility function for queues to study the changes in arrival rates related to the inventory level of vaccines, the efficiency of vaccines, and a risk aversion coefficient for vaccinees. A multi-period queuing model considering congestion in the vaccination process is proposed to minimise two contradictory objectives: (i) the expected average wait time of vaccinees and (ii) the total investment in the holding and ordering of vaccines. To develop the bi-objective non-linear programming model, the goal attainment algorithm and the non-dominated sorting genetic algorithm (NSGA-II) are employed for small- to large-scale problems. Several solution repairs are also implemented in the classic NSGA-II algorithm to improve its efficiency. Four standard performance metrics are used to investigate the algorithm. The non-parametric Friedman and Wilcoxon signed-rank tests are applied on several numerical examples to ensure the privilege of the improved algorithm. The NSGA-II algorithm surveys an authentic case study in Australia, and several scenarios are created to provide insights for an efficient vaccination program.

Longitudinal humoral response in MS patients treated with cladribine tablets after receiving the second and third doses of SARS-CoV-2 mRNA vaccine

BACKGROUND

Multiple sclerosis (MS) patients receive immunomodulatory treatments which can influence their ability to maintain vaccine specific serological response overtime. MS patients treated with cladribine tablets developed a positive serology response following two doses of mRNA COVID-19 vaccine. However, there is only limited data regarding the effect of cladribine tablets on long-term humoral response after the second and the third booster.

METHODS

Serology response to SARS-CoV-2 was tested in healthy controls (HCs) and MS patients treated with cladribine tablets 6 and 9-12 months after the second dose, and 1 and 3-6 months following the third booster-dose of the BTN162b2 mRNA vaccine.

RESULTS

Thirty-five out of 36 MS patients treated with cladribine tablets and 100% (46/46) of HCs had a positive serology response up to 10 months after the second vaccine dose. In addition, all cladribine tablets -treated MS patients (22/22) and HCs (24/24) had a positive robust serology response following the third vaccine with a positive humoral response sustain up to 6 months. One month after the third vaccine dose IgG levels were significantly lower in patients treated with cladribine tablets compared to HCs (15,598+11,313 vs 26,394+11,335, p<0.01). Six-month post second vaccine and 3-6 months post third vaccine there was no difference in IgG levels between the groups (1088.0 ± 1072.0 vs 1153.0 ± 997.1, p = 0.79; 5234+4097 vs 11,198+14,679, p = 0.4).

CONCLUSION AND RELEVANCE

MS patients treated with cladribine tablets have sustained positive vaccine specific serology response following the second and third SARS-CoV-2 vaccine dose.

Should we allocate more COVID-19 vaccine doses to non-vaccinated individuals?

Following the approval by the FDA of two COVID-19 vaccines, which are administered in two doses three to four weeks apart, we simulate the effects of various vaccine distribution policies on the cumulative number of infections and deaths in the United States in the presence of shocks to the supply of vaccines. Our forecasts suggest that allocating more than 50% of available doses to individuals who have not received their first dose can significantly increase the number of lives saved and significantly reduce the number of COVID-19 infections. We find that a 50% allocation saves on average 33% more lives, and prevents on average 32% more infections relative to a policy that guarantees a second dose within the recommended time frame to all individuals who have already received their first dose. In fact, in the presence of supply shocks, we find that the former policy would save on average 8, 793 lives and prevents on average 607, 100 infections while the latter policy would save on average 6, 609 lives and prevents on average 460, 743 infections.

Effectiveness of delayed second dose of AZD1222 vaccine in patients with autoimmune rheumatic disease

There is paucity of data on extended dosing interval between two doses of AZD1222 (AstraZeneca) in patients with Autoimmune Rheumatic Diseases (AIRD). We aimed to study the humoral response and rate of breakthrough infections between the two groups who had received the second dose of vaccine at 4 weeks (Group 1) and 10–14 weeks (Group 2). From established cohort [COVID-19 vaccination cohort from CARE(CVCC)] of vaccinated patients with AIRD, those who had received AZD1222 were included and divided into two groups. Anti-Receptor Binding Domain (RBD) antibodies (IU/ml) were measured 1 month after the second dose. Its predictors and rate of breakthrough infections were studied. Four hundred ninety-five patients with AIRD were included in this study. Group 2 had higher anti-RBD antibody titres [1310.6 (±977.8) and [736 (±864.7), p = 0.0001. On univariate analysis, presence of Diabetes Mellitus; use of Methotrexate, Sulfasalazine, and Mycophenolate Mofetil; and vaccine interval were significantly associated with anti-RBD antibodies. Diabetes Mellitus and vaccine interval were independent predictors on multivariate analysis. Breakthrough infections were higher in Group 1 numerically on survival analysis but the difference was not significant (7.5% and 4.5%; log rank test: p = 0.25). In conclusion, increasing the gap between doses of the AZD1222 vaccine from 4 week to 10–14 weeks was found to be more beneficial in terms of antibody response in patients with AIRD. There was a trend towards higher breakthrough infections in the short interval group, supporting the antibody data.

Key Points • There is paucity of data on effectiveness of increased dosing interval from 4-6 to 10-14 weeks for AZD1222 in patients with AIRDs • We observed a better humoral response with increased dosing interval with the interval and Diabetes Mellitus being independent predictors of the anti-RBD antibody levels • Breakthrough infections were numerically higher in the short interval group but the difference wasn't significant

Dosing interval strategies for two-dose COVID-19 vaccination in 13 middle-income countries of Europe: Health impact modelling and benefit-risk analysis

Background

In settings where the COVID-19 vaccine supply is constrained, extending the intervals between the first and second doses of the COVID-19 vaccine may allow more people receive their first doses earlier. Our aim is to estimate the health impact of COVID-19 vaccination alongside benefit-risk assessment of different dosing intervals in 13 middle-income countries (MICs) of Europe.

Methods

We fitted a dynamic transmission model to country-level daily reported COVID-19 mortality in 13 MICs in Europe (Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Georgia, Republic of Moldova, Russian Federation, Serbia, North Macedonia, Turkey, and Ukraine). A vaccine product with characteristics similar to those of the Oxford/AstraZeneca COVID-19 (AZD1222) vaccine was used in the base case scenario and was complemented by sensitivity analyses around efficacies similar to other COVID-19 vaccines. Both fixed dosing intervals at 4, 8, 12, 16, and 20 weeks and dose-specific intervals that prioritise specific doses for certain age groups were tested. Optimal intervals minimise COVID-19 mortality between March 2021 and December 2022. We incorporated the emergence of variants of concern (VOCs) into the model and conducted a benefit-risk assessment to quantify the tradeoff between health benefits versus adverse events following immunisation.

Findings

In all countries modelled, optimal strategies are those that prioritise the first doses among older adults (60+ years) or adults (20+ years), which lead to dosing intervals longer than six months. In comparison, a four-week fixed dosing interval may incur 10.1% [range: 4.3% - 19.0%; n = 13 (countries)] more deaths. The rapid waning of the immunity induced by the first dose (i.e. with means ranging 60-120 days as opposed to 360 days in the base case) resulted in shorter optimal dosing intervals of 8-20 weeks. Benefit-risk ratios were the highest for fixed dosing intervals of 8-12 weeks.

Interpretation

We infer that longer dosing intervals of over six months could reduce COVID-19 mortality in MICs of Europe. Certain parameters, such as rapid waning of first-dose induced immunity and increased immune escape through the emergence of VOCs, could significantly shorten the optimal dosing intervals.

Funding

World Health Organization.

Impact of non-pharmaceutical interventions and vaccination on COVID-19 outbreaks in Nunavut, Canada: a Canadian Immunization Research Network (CIRN) study

Background

Nunavut, the northernmost Arctic territory of Canada, experienced three community outbreaks of the coronavirus disease 2019 (COVID-19) from early November 2020 to mid-June 2021. We sought to investigate how non-pharmaceutical interventions (NPIs) and vaccination affected the course of these outbreaks.

Methods

We used an agent-based model of disease transmission to simulate COVID-19 outbreaks in Nunavut. The model encapsulated demographics and household structure of the population, the effect of NPIs, and daily number of vaccine doses administered. We fitted the model to inferred, back-calculated infections from incidence data reported from October 2020 to June 2021. We then compared the fit of the scenario based on case count data with several counterfactual scenarios without the effect of NPIs, without vaccination, and with a hypothetical accelerated vaccination program whereby 98% of the vaccine supply was administered to eligible individuals.

Results

We found that, without a territory-wide lockdown during the first COVID-19 outbreak in November 2020, the peak of infections would have been 4.7 times higher with a total of 5,404 (95% CrI: 5,015—5,798) infections before the start of vaccination on January 6, 2021. Without effective NPIs, we estimated a total of 4,290 (95% CrI: 3,880—4,708) infections during the second outbreak under the pace of vaccination administered in Nunavut. In a hypothetical accelerated vaccine rollout, the total infections during the second Nunavut outbreak would have been 58% lower, to 1,812 (95% CrI: 1,593—2,039) infections. Vaccination was estimated to have the largest impact during the outbreak in April 2021, averting 15,196 (95% CrI: 14,798—15,591) infections if the disease had spread through Nunavut communities. Accelerated vaccination would have further reduced the total infections to 243 (95% CrI: 222—265) even in the absence of NPIs.

Conclusions

NPIs have been essential in mitigating pandemic outbreaks in this large, geographically distanced and remote territory. While vaccination has the greatest impact to prevent infection and severe outcomes, public health implementation of NPIs play an essential role in the short term before attaining high levels of immunity in the population.

Supplementary information

The online version contains supplementary material available at 10.1186/s12889-022-13432-1.