Modelling of COVID-19 vaccination strategies and herd immunity, in scenarios of limited and full vaccine supply in NSW, Australia

Socio-demographic differences in citizen' preferences for distributing a scarce, lifesaving resource: A case study using COVID-19 vaccine distribution in Belgium

The COVID-19 pandemic, particularly the period when safe and protective vaccines became available, presented an excellent opportunity to investigate public preferences regarding how to ration a scarce, life-saving resource. It is important to understand which distributive strategies are preferred but, as this is an inherently normative matter, also to which extent different socio-demographic groups hold different opinions. This study assesses the impact of socio-demographic characteristics on preferences for vaccine prioritization during the early stages of the COVID-19 pandemic at the moment when vaccines were not available yet. A representative sample of 2060 Belgians were asked to rank eight prioritization strategies. Based on the rankings, we clustered the eight strategies into five overarching groups. Then, we estimated the potential impact of respondents' socio-demographic characteristics on their preferences towards these five strategies. The ranking exercise shows that the vulnerability strategy (i.e. chronically ill and elderly) is preferred most often (N = 1815) and the market strategy (i.e. individuals who come first or pay the most) by the fewest respondents (N = 116). Preferences for vaccine distribution vary among population subgroups. Women, older or retired individuals, residents of Flanders, those with a positive attitude towards vaccination efforts and/or high vaccination willingness, and those who prefer the government or scientists to decide upon prioritization were more likely to prefer a designated priority strategy (i.e. vulnerability strategy, workers strategy or spreaders). Furthermore, younger respondents, residents of Wallonia, and those previously infected by COVID-19 tended to favour a risk-independent prioritization strategy (i.e. lottery or market strategy) more than others. This study validates the appropriateness of the implemented rationing approaches, by supporting a phased approach in which first vulnerables were vaccinated. However, it also demonstrates that different subgroups had substantially different preferences and the population was in disagreement about what constitutes a fair rationing policy for a scarce, potentially lifesaving resource.

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.

Insights from qualitative and bifurcation analysis of COVID-19 vaccination model in Bangladesh

The unprecedented global impact of the 2019 coronavirus disease (COVID-19) has necessitated a comprehensive understanding of its transmission dynamics and control measures. In this study, we present a detailed analysis of a COVID-19 vaccination model tailored to the context of Bangladesh, incorporating dual-dose vaccination strategies. By employing qualitative and bifurcation analysis techniques, we investigate the equilibrium points, effective reproduction number (R0), and critical thresholds that influence the prevalence and control of COVID-19 in the region. Our findings reveal insights into the effectiveness of vaccination programs and provide a framework for developing targeted control plans. Through a rigorous examination of model parameters and sensitivity analysis, we identify key factors driving COVID-19 transmission dynamics, emphasizing the significance of vaccination rates and other critical parameters. The validation of our model against real-world data underscores its utility in informing evidence-based decision-making for managing the COVID-19 pandemic in Bangladesh and beyond.

A vaccination-based COVID-19 model: Analysis and prediction using Hamiltonian Monte Carlo

Compartmental models have emerged as robust computational frameworks and have yielded remarkable success in the fight against COVID-19. This study proposes a vaccination-based compartmental model for COVID-19 transmission dynamics. The model reflects the specific stages of COVID-19 infection and integrates a vaccination strategy, allowing for a comprehensive analysis of how vaccination rates influence the disease spread. We fit this model to daily confirmed COVID-19 cases in Tennessee, United States of America (USA), from June 4 to November 26, 2021, in a Bayesian inference approach using the Hamiltonian Monte Carlo (HMC) algorithm. First, excluding vaccination dynamics from the model, we estimated key epidemiological parameters like infection, recovery, and disease-induced death rates. This analysis yielded a basic reproduction number (R 0 ) of 1.5. Second, we incorporated vaccination dynamics and estimated the vaccination rate for three vaccines: 0.0051 per day for both Pfizer and Moderna and 0.0059 per day for Janssen. The fitted curves show reductions in the epidemic peak for all three vaccines. Pfizer and Moderna vaccines bring the peak down from 8,029 infected cases to 5,616 infected cases, while the Janssen vaccine reduces it, to 6,493 infected cases. Simulations of the model by varying the vaccination rate and vaccine efficacy were performed. A highly effective vaccine (95% efficacy) with a daily vaccination rate of 0.006 halved COVID-19 infections, reducing cases from 8,029 to around 4,000. The results also show that the model's prediction accuracy for new observations improves with the number of observed data used to train the model.

Indirect effectiveness of a novel SARS-COV-2 vaccine (SCB-2019) in unvaccinated household contacts in the Philippines: A cluster randomised analysis

BACKGROUND

Though observational evidence supports indirect effects of SARS-CoV-2 vaccines, randomised experiments are lacking. To address this gap, the double-blinded, prospective follow-up of the household contacts (HHCs) of Philippine participants of the individually-randomised, placebo-controlled trial of the adjuvanted-subunit protein COVID-19 vaccine, SCB-2019, (EudraCT, 2020-004272-17; ClinicalTrials.gov, NCT04672395) was analyzed in a cluster-randomised fashion.

METHODS

Over an eight-week period, HHCs were followed by rRT-PCR and paired rapid antibody tests (RATs) to detect symptomatic (SCI, primary) and all (ACI, secondary) SARS-CoV-2 infection. A standard analysis estimated the indirect effectiveness of SCB-2019 for each endpoint, excluding HHC RAT-positive at enrollment. A secondary analysis employed enzyme-linked immunosorbent assay (ELISA) results to correct for suspected bias.

FINDINGS

SCB-2019 (N = 3470) and placebo (N = 3225) exposed HHCs contributed to at least one analysis. The standard analysis estimated that SCB-2019 reduced the risk of SCI by 83% (95% confidence/credible interval [CI: 32% to 96%), with no effect against ACI. The bias-corrected relative risk reduction was 97% (95% CI: 74% to 100%) for SCI and 79% (95% CI: 14% to 96%) for ACI, with an estimated one SARS-CoV-2 infection prevented per 4.8 households where one member received SCB-2019.

INTERPRETATION

SCB-2019 demonstrated bias-corrected indirect effectiveness against SARS-CoV-2 infection among HHC, even at a modest coverage level in the household (approximately 25%). Further research into the indirect effects of SARS-CoV-2 vaccines is needed to optimize the impact of limited doses in low and middle-income settings.

Optimization Modeling for Pandemic Vaccine Supply Chain Management: A Review and Future Research Opportunities

During various stages of the COVID-19 pandemic, countries implemented diverse vaccine management approaches, influenced by variations in infrastructure and socio-economic conditions. This article provides a comprehensive overview of optimization models developed by the research community throughout the COVID-19 era, aimed at enhancing vaccine distribution and establishing a standardized framework for future pandemic preparedness. These models address critical issues such as site selection, inventory management, allocation strategies, distribution logistics, and route optimization encountered during the COVID-19 crisis. A unified framework is employed to describe the models, emphasizing their integration with epidemiological models to facilitate a holistic understanding. This article also summarizes evolving nature of literature, relevant research gaps, and authors' perspectives for model selection. Finally, future research scopes are detailed both in the context of modeling and solutions approaches.

Understanding the determinants of vaccine hesitancy in the United States: A comparison of social surveys and social media

The COVID-19 pandemic prompted governments worldwide to implement a range of containment measures, including mass gathering restrictions, social distancing, and school closures. Despite these efforts, vaccines continue to be the safest and most effective means of combating such viruses. Yet, vaccine hesitancy persists, posing a significant public health concern, particularly with the emergence of new COVID-19 variants. To effectively address this issue, timely data is crucial for understanding the various factors contributing to vaccine hesitancy. While previous research has largely relied on traditional surveys for this information, recent sources of data, such as social media, have gained attention. However, the potential of social media data as a reliable proxy for information on population hesitancy, especially when compared with survey data, remains underexplored. This paper aims to bridge this gap. Our approach uses social, demographic, and economic data to predict vaccine hesitancy levels in the ten most populous US metropolitan areas. We employ machine learning algorithms to compare a set of baseline models that contain only these variables with models that incorporate survey data and social media data separately. Our results show that XGBoost algorithm consistently outperforms Random Forest and Linear Regression, with marginal differences between Random Forest and XGBoost. This was especially the case with models that incorporate survey or social media data, thus highlighting the promise of the latter data as a complementary information source. Results also reveal variations in influential variables across the five hesitancy classes, such as age, ethnicity, occupation, and political inclination. Further, the application of models to different MSAs yields mixed results, emphasizing the uniqueness of communities and the need for complementary data approaches. In summary, this study underscores social media data's potential for understanding vaccine hesitancy, emphasizes the importance of tailoring interventions to specific communities, and suggests the value of combining different data sources.

Assessing the dynamics and impact of COVID-19 vaccination on disease spread: A data-driven approach

The COVID-19 pandemic has significantly impacted global health, social, and economic situations since its emergence in December 2019. The primary focus of this study is to propose a distinct vaccination policy and assess its impact on controlling COVID-19 transmission in Malaysia using a Bayesian data-driven approach, concentrating on the year 2021. We employ a compartmental Susceptible-Exposed-Infected-Recovered-Vaccinated (SEIRV) model, incorporating a time-varying transmission rate and a data-driven method for its estimation through an Exploratory Data Analysis (EDA) approach. While no vaccine guarantees total immunity against the disease, and vaccine immunity wanes over time, it is critical to include and accurately estimate vaccine efficacy, as well as a constant vaccine immunity decay or wane factor, to better simulate the dynamics of vaccine-induced protection over time. Based on the distribution and effectiveness of vaccines, we integrated a data-driven estimation of vaccine efficacy, calculated at 75% for Malaysia, underscoring the model's realism and relevance to the specific context of the country. The Bayesian inference framework is used to assimilate various data sources and account for underlying uncertainties in model parameters. The model is fitted to real-world data from Malaysia to analyze disease spread trends and evaluate the effectiveness of our proposed vaccination policy. Our findings reveal that this distinct vaccination policy, which emphasizes an accelerated vaccination rate during the initial stages of the program, is highly effective in mitigating the spread of COVID-19 and substantially reducing the pandemic peak and new infections. The study found that vaccinating 57-66% of the population (as opposed to 76% in the real data) with a better vaccination policy such as proposed here is able to significantly reduce the number of new infections and ultimately reduce the costs associated with new infections. The study contributes to the development of a robust and informative representation of COVID-19 transmission and vaccination, offering valuable insights for policymakers on the potential benefits and limitations of different vaccination policies, particularly highlighting the importance of a well-planned and efficient vaccination rollout strategy. While the methodology used in this study is specifically applied to national data from Malaysia, its successful application to local regions within Malaysia, such as Selangor and Johor, indicates its adaptability and potential for broader application. This demonstrates the model's adaptability for policy assessment and improvement across various demographic and epidemiological landscapes, implying its usefulness for similar datasets from various geographical regions.

Modelling COVID-19 Vaccination in the UK: Impact of the Autumn 2022 and Spring 2023 Booster Campaigns

INTRODUCTION

The delivery of COVID-19 vaccines was successful in reducing hospitalizations and mortality. However, emergence of the Omicron variant resulted in increased virus transmissibility. Consequently, booster vaccination programs were initiated to decrease the risk of severe disease and death among vulnerable members of the population. This study aimed to estimate the effects of the booster program and alternative vaccination strategies on morbidity and mortality due to COVID-19 in the UK.

METHOD

A Susceptible-Exposed-Infectious-Recovered (SEIR) model was used to assess the impact of several vaccination strategies on severe outcomes associated with COVID-19, including hospitalizations, mortality, National Health Service (NHS) capacity quantified by hospital general ward and intensive care unit (ICU) bed days, and patient productivity. The model accounted for age-, risk- and immunity-based stratification of the UK population. Outcomes were evaluated over a 48-week time horizon from September 2022 to August 2023 considering the actual UK autumn 2022/spring 2023 booster campaigns and six counterfactual strategies.

RESULTS

The model estimated that the autumn 2022/spring 2023 booster campaign resulted in a reduction of 18,921 hospitalizations and 1463 deaths, compared with a no booster scenario. Utilization of hospital bed days due to COVID-19 decreased after the autumn 2022/spring 2023 booster campaign. Expanding the booster eligibility criteria and improving uptake improved all outcomes, including averting twice as many ICU admissions, preventing more than 20% additional deaths, and a sevenfold reduction in long COVID, compared with the autumn 2022/spring 2023 booster campaign. The number of productive days lost was reduced by fivefold indicating that vaccinating a wider population has a beneficial impact on the morbidities associated with COVID-19.

CONCLUSION

Our modelling demonstrates that the autumn 2022/spring 2023 booster campaign reduced COVID-19-associated morbidity and mortality. Booster campaigns with alternative eligibility criteria warrant consideration in the UK, given their potential to further reduce morbidity and mortality as future variants emerge.

The impact of quality-adjusted life years on evaluating COVID-19 mitigation strategies: lessons from age-specific vaccination roll-out and variants of concern in Belgium (2020-2022)

BACKGROUND

When formulating and evaluating COVID-19 vaccination strategies, an emphasis has been placed on preventing severe disease that overburdens healthcare systems and leads to mortality. However, more conventional outcomes such as quality-adjusted life years (QALYs) and inequality indicators are warranted as additional information for policymakers.

METHODS

We adopted a mathematical transmission model to describe the infectious disease dynamics of SARS-COV-2, including disease mortality and morbidity, and to evaluate (non)pharmaceutical interventions. Therefore, we considered temporal immunity levels, together with the distinct transmissibility of variants of concern (VOCs) and their corresponding vaccine effectiveness. We included both general and age-specific characteristics related to SARS-CoV-2 vaccination. Our scenario study is informed by data from Belgium, focusing on the period from August 2021 until February 2022, when vaccination for children aged 5-11 years was initially not yet licensed and first booster doses were administered to adults. More specifically, we investigated the potential impact of an earlier vaccination programme for children and increased or reduced historical adult booster dose uptake.

RESULTS

Through simulations, we demonstrate that increasing vaccine uptake in children aged 5-11 years in August-September 2021 could have led to reduced disease incidence and ICU occupancy, which was an essential indicator for implementing non-pharmaceutical interventions and maintaining healthcare system functionality. However, an enhanced booster dose regimen for adults from November 2021 onward could have resulted in more substantial cumulative QALY gains, particularly through the prevention of elevated levels of infection and disease incidence associated with the emergence of Omicron VOC. In both scenarios, the need for non-pharmaceutical interventions could have decreased, potentially boosting economic activity and mental well-being.

CONCLUSIONS

When calculating the impact of measures to mitigate disease spread in terms of life years lost due to COVID-19 mortality, we highlight the impact of COVID-19 on the health-related quality of life of survivors. Our study underscores that disease-related morbidity could constitute a significant part of the overall health burden. Our quantitative findings depend on the specific setup of the interventions under review, which is open to debate or should be contextualised within future situations.

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.

Mutations make pandemics worse or better: modeling SARS-CoV-2 variants and imperfect vaccination

COVID-19 is a respiratory disease triggered by an RNA virus inclined to mutations. Since December 2020, variants of COVID-19 (especially Delta and Omicron) continuously appeared with different characteristics that influenced death and transmissibility emerged around the world. To address the novel dynamics of the disease, we propose and analyze a dynamical model of two strains, namely native and mutant, transmission dynamics with mutation and imperfect vaccination. It is also assumed that the recuperated individuals from the native strain can be infected with mutant strain through the direct contact with individual or contaminated surfaces or aerosols. We compute the basic reproduction number, R 0 , which is the maximum of the basic reproduction numbers of native and mutant strains. We prove the nonexistence of backward bifurcation using the center manifold theory, and global stability of disease-free equilibrium whenR 0 < 1 , that is, vaccine is effective enough to eliminate the native and mutant strains even if it cannot provide full protection. Hopf bifurcation appears when the endemic equilibrium loses its stability. An intermediate mutation rate ν 1 leads to oscillations. When ν 1 increases over a threshold, the system regains its stability and exhibits an interesting dynamics called endemic bubble. An analytical expression for vaccine-induced herd immunity is derived. The epidemiological implication of the herd immunity threshold is that the disease may effectively be eradicated if the minimum herd immunity threshold is attained in the community. Furthermore, the model is parameterized using the Indian data of the cumulative number of confirmed cases and deaths of COVID-19 from March 1 to September 27 in 2021, using MCMC method. The cumulative cases and deaths can be reduced by increasing the vaccine efficacies to both native and mutant strains. We observe that by considering the vaccine efficacy against native strain as 90%, both cumulative cases and deaths would be reduced by 0.40%. It is concluded that increasing immunity against mutant strain is more influential than the vaccine efficacy against it in controlling the total cases. Our study demonstrates that the COVID-19 pandemic may be worse due to the occurrence of oscillations for certain mutation rates (i.e., outbreaks will occur repeatedly) but better due to stability at a lower infection level with a larger mutation rate. We perform sensitivity analysis using the Latin Hypercube Sampling methodology and partial rank correlation coefficients to illustrate the impact of parameters on the basic reproduction number, the number of cumulative cases and deaths, which ultimately sheds light on disease mitigation.

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.

The impact of triple doses vaccination and other interventions for controlling the outbreak of COVID-19 cases and mortality in Australia: A modelling study

COVID-19 is a significant public health problem around the globe, including in Australia. Despite this, Australia's Ministry of Health has expanded COVID-19 control measures widely, logistical trials exist, and the disease burden still needs more clarity. One of the best methods to comprehend the dynamics of disease transmission is by mathematical modeling of COVID-19, which also makes it possible to quantify factors in many places, including Australia. In order to understand the dynamics of COVID-19 in Australia, we examine a mathematical modeling framework for the virus in this study. Australian COVID-19 actual incidence data from January to December 2021 was used to calibrate the model. We also performed a sensitivity analysis of the model parameters and found that the COVID-19 transmission rate was the primary factor in determining the basic reproduction number (R0). Gradually influential intervention policies were established, with accurate effect and coverage regulated with the help of COVID-19 experts in Australia. We simulated data for the period from April 2022 to August 2023. To ascertain which of these outcomes is most effective in lowering the COVID-19 burden, we here assessed the COVID-19 burden (as shown by the number of incident cases and mortality) under a range of intervention scenarios. Regarding the policy of single intervention, the fastest and most efficient way to lower the incidence of COVID-19 is via increasing the first-dose immunization rate, while an improved treatment rate for the afflicted population is also helps to lower mortality in Australia. Furthermore, our results imply that integrating more therapies at the same time increases their efficacy, particularly for mortality, which significantly reduced with a moderate effort, while lowering the number of COVID-19 instances necessitates a major and ongoing commitment.

Assessing the perceived effect of non-pharmaceutical interventions on SARS-Cov-2 transmission risk: an experimental study in Europe

We conduct a large (N = 6567) online experiment to measure the features of non-pharmaceutical interventions (NPIs) that citizens of six European countries perceive to lower the risk of transmission of SARS-Cov-2 the most. We collected data in Bulgaria (n = 1069), France (n = 1108), Poland (n = 1104), Italy (n = 1087), Spain (n = 1102) and Sweden (n = 1097). Based on the features of the most widely adopted public health guidelines to reduce SARS-Cov-2 transmission (mask wearing vs not, outdoor vs indoor contact, short vs 90 min meetings, few vs many people present, and physical distancing of 1 or 2 m), we conducted a discrete choice experiment (DCE) to estimate the public's perceived risk of SARS-CoV-2 transmission in scenarios that presented mutually exclusive constellations of these features. Our findings indicate that participants' perception of transmission risk was most influenced by the NPI attributes of mask-wearing and outdoor meetings and the least by NPI attributes that focus on physical distancing, meeting duration, and meeting size. Differentiating by country, gender, age, cognitive style (reflective or intuitive), and perceived freight of COVID-19 moreover allowed us to identify important differences between subgroups. Our findings highlight the importance of improving health policy communication and citizens' health literacy about the design of NPIs and the transmission risk of SARS-Cov-2 and potentially future viruses.

Priority populations' experiences of isolation, quarantine and distancing for COVID-19: protocol for a longitudinal cohort study (Optimise Study)

INTRODUCTION

Longitudinal studies can provide timely and accurate information to evaluate and inform COVID-19 control and mitigation strategies and future pandemic preparedness. The Optimise Study is a multidisciplinary research platform established in the Australian state of Victoria in September 2020 to collect epidemiological, social, psychological and behavioural data from priority populations. It aims to understand changing public attitudes, behaviours and experiences of COVID-19 and inform epidemic modelling and support responsive government policy.

METHODS AND ANALYSIS

This protocol paper describes the data collection procedures for the Optimise Study, an ongoing longitudinal cohort of ~1000 Victorian adults and their social networks. Participants are recruited using snowball sampling with a set of seeds and two waves of snowball recruitment. Seeds are purposively selected from priority groups, including recent COVID-19 cases and close contacts and people at heightened risk of infection and/or adverse outcomes of COVID-19 infection and/or public health measures. Participants complete a schedule of monthly quantitative surveys and daily diaries for up to 24 months, plus additional surveys annually for up to 48 months. Cohort participants are recruited for qualitative interviews at key time points to enable in-depth exploration of people's lived experiences. Separately, community representatives are invited to participate in community engagement groups, which review and interpret research findings to inform policy and practice recommendations.

ETHICS AND DISSEMINATION

The Optimise longitudinal cohort and qualitative interviews are approved by the Alfred Hospital Human Research Ethics Committee (# 333/20). The Optimise Study CEG is approved by the La Trobe University Human Ethics Committee (# HEC20532). All participants provide informed verbal consent to enter the cohort, with additional consent provided prior to any of the sub studies. Study findings will be disseminated through public website (https://optimisecovid.com.au/study-findings/) and through peer-reviewed publications.

TRIAL REGISTRATION NUMBER

NCT05323799.

Vaccination compartmental epidemiological models for the delta and omicron SARS-CoV-2 variants

We explore the inclusion of vaccination in compartmental epidemiological models concerning the delta and omicron variants of the SARS-CoV-2 virus that caused the COVID-19 pandemic. We expand on our earlier compartmental-model work by incorporating vaccinated populations. We present two classes of models that differ depending on the immunological properties of the variant. The first one is for the delta variant, where we do not follow the dynamics of the vaccinated individuals since infections of vaccinated individuals were rare. The second one for the far more contagious omicron variant incorporates the evolution of the infections within the vaccinated cohort. We explore comparisons with available data involving two possible classes of counts, fatalities and hospitalizations. We present our results for two regions, Andalusia and Switzerland (including the Principality of Liechtenstein), where the necessary data are available. In the majority of the considered cases, the models are found to yield good agreement with the data and have a reasonable predictive capability beyond their training window, rendering them potentially useful tools for the interpretation of the COVID-19 and further pandemic waves, and for the design of intervention strategies during these waves.

Model-informed health and socio-economic benefits of enhancing global equity and access to Covid-19 vaccines

We take a model-informed approach to the view that a global equitable access (GEA) to Covid-19 vaccines is the key to bring this pandemic to an end. We show that the equitable redistribution (proportional to population size) of the currently available vaccines is not sufficient to stop the pandemic, whereas a 60% increase in vaccine access (the global share of vaccinated people) would have allowed the current distribution to stop the pandemic in about a year of vaccination, saving millions of people in poor countries. We then investigate the interplay between access to vaccines and their distribution among rich and poor countries, showing that the access increase to stop the pandemic gets minimized at + 32% by the equitable distribution (- 36% in rich countries and + 60% in poor ones). To estimate the socio-economic benefits of a vaccination campaign with enhanced global equity and access (eGEA), we compare calibrated simulations of the current scenario with a hypothetical, vaccination-intensive scenario that assumes high rollouts (shown however by many rich and poor countries during the 2021-2022 vaccination campaign) and an improved equity from the current 2.5:1 to a 2:1 rich/poor-ratio of the population fractions vaccinated per day. Assuming that the corresponding + 130% of vaccine production is made possible by an Intellectual Property waiver, we show that the money saved on vaccines globally by the selected eGEA scenario overcomes the 5-year profit of the rights holders in the current situation. This justifies compensation mechanisms in exchange for the necessary licensing agreements. The good news is that the benefits of this eGEA scenario are still relevant, were we ready to implement it now.

Comparing the recurrence of audio-vestibular disorders following breakthrough infection of COVID-19 vs. those following vaccine administration

PURPOSE

The term "breakthrough infection" of COVID-19 indicates that subjects who previously received COVID-19 vaccination became infected with COVID-19. This study compared the recurrence of audio-vestibular disorders following breakthrough infection of COVID-19 vs. those following vaccine administration.

PATIENTS AND METHODS

Fifty patients with previous known audio-vestibular disorders visited our clinic due to recurrence of inner ear symptoms following breakthrough infection of COVID-19 and were assigned to Group A. Another 50 patients who had recurrent inner ear symptoms following COVID-19 vaccination were assigned to Group B for comparison. The post-breakthrough infection interval is defined from date of breakthrough infection to the onset of inner ear symptoms, while the post-vaccination interval means the time from date of vaccination to the onset of inner ear symptoms. These two intervals were calculated and then compared.

RESULTS

The time from latest vaccination to the breakthrough infection of COVID-19 was 4 m (median), likely due to waning of IgG response. To the onset of inner ear symptoms, the post-breakthrough infection interval was 40d (median) for Group A, which was significantly longer than 10d (median) of the post-vaccination interval for Group B.

CONCLUSION

The post-breakthrough infection interval (median, 40d) is significantly longer than the post-vaccination interval (median, 10d) to exacerbate pre-existing audio-vestibular disorders. The reason is probably because an interval of 40d is related to IgG peak response following COVID-19 breakthrough infection, while that of 10d is responsible for IgG production after COVID-19 vaccination.

Evaluating the COVID-19 vaccination program in Japan, 2021 using the counterfactual reproduction number

Japan implemented its nationwide vaccination program against COVID-19 in 2021, immunizing more than one million people (approximately 1%) a day. However, the direct and indirect impacts of the program at the population level have yet to be fully evaluated. To assess the vaccine effectiveness during the Delta variant (B.1.617.2) epidemic in 2021, we used a renewal process model. A transmission model was fitted to the confirmed cases from 17 February to 30 November 2021. In the absence of vaccination, the cumulative numbers of infections and deaths during the study period were estimated to be 63.3 million (95% confidence interval [CI] 63.2-63.6) and 364,000 (95% CI 363-366), respectively; the actual numbers of infections and deaths were 4.7 million and 10,000, respectively. Were the vaccination implemented 14 days earlier, there could have been 54% and 48% fewer cases and deaths, respectively, than the actual numbers. We demonstrated the very high effectiveness of COVID-19 vaccination in Japan during 2021, which reduced mortality by more than 97% compared with the counterfactual scenario. The timing of expanding vaccination and vaccine recipients could be key to mitigating the disease burden of COVID-19. Rapid and proper decision making based on firm epidemiological input is vital.

Parents' Concerns, Behavior, Perception, and Hesitancy Regarding COVID-19 Vaccinations for Children in Central Saudi Arabia

In Saudi Arabia, the Ministry of Health (MoH) has implemented strict rules to ensure COVID-19 vaccination uptake by the general public. However, there is hesitancy about COVID-19 vaccination among parents for their children worldwide. We aimed to determine the concerns, behaviors, perceptions, and hesitancy of COVID-19 vaccination among parents for their children in Saudi Arabia. Parents of children aged 5-11 years were included in this cross-sectional study. A total of 1507 responses were obtained using the convenience sampling technique. The data were analyzed using SPSS version 25.0 by applying descriptive and inferential statistics. Of the parents who responded, 74.5% believed that the COVID-19 vaccination could affect the genes of children, and 72.8% believed that the COVID-19 vaccination could have a greater number of positive impacts on the overall health of children. In total, 87% of the parents were satisfied with the vaccination services and effective policies of the MoH, Saudi Arabia. This study concluded that there is a greater need to increase public awareness regarding the beneficial impact of COVID-19 vaccination on the overall health of children. Effective awareness campaigns are also required to provide empirical information to the public that COVID-19 vaccination for children is safe and effective.

Strategies and Trends in COVID-19 Vaccination Delivery: What We Learn and What We May Use for the Future

Vaccination has been the most effective way to control the outbreak of the COVID-19 pandemic. The numbers and types of vaccines have reached considerable proportions, even if the question of vaccine procedures and frequency still needs to be resolved. We have come to learn the necessity of defining vaccination distribution strategies with regard to COVID-19 that could be used for any future pandemics of similar gravity. In fact, vaccine monitoring implies the existence of a strategy that should be measurable in terms of input and output, based on a mathematical model, including death rates, the spread of infections, symptoms, hospitalization, and so on. This paper addresses the issue of vaccine diffusion and strategies for monitoring the pandemic. It provides a description of the importance and take up of vaccines and the links between procedures and the containment of COVID-19 variants, as well as the long-term effects. Finally, the paper focuses on the global scenario in a world undergoing profound social and political change, with particular attention on current and future health provision. This contribution would represent an example of vaccination experiences, which can be useful in other pandemic or epidemiological contexts.

COVID-19 Vaccine Strategy Left Small Primary Care Practices On The Sidelines

We report on the experience of small primary care practices participating in a national clinical registry with COVID-19 vaccines and vaccination data. At the end of 2021, 11.2 percent of these practices' 3.9 million patients had records of COVID-19 vaccination; 43.1 percent of clinics had no record of patients' COVID-19 vaccinations, but 93.4 percent of clinics had provided or recorded other routine vaccinations.

Wastewater-based epidemiology predicts COVID-19-induced weekly new hospital admissions in over 150 USA counties

Although the coronavirus disease (COVID-19) emergency status is easing, the COVID-19 pandemic continues to affect healthcare systems globally. It is crucial to have a reliable and population-wide prediction tool for estimating COVID-19-induced hospital admissions. We evaluated the feasibility of using wastewater-based epidemiology (WBE) to predict COVID-19-induced weekly new hospitalizations in 159 counties across 45 states in the United States of America (USA), covering a population of nearly 100 million. Using county-level weekly wastewater surveillance data (over 20 months), WBE-based models were established through the random forest algorithm. WBE-based models accurately predicted the county-level weekly new admissions, allowing a preparation window of 1-4 weeks. In real applications, periodically updated WBE-based models showed good accuracy and transferability, with mean absolute error within 4-6 patients/100k population for upcoming weekly new hospitalization numbers. Our study demonstrated the potential of using WBE as an effective method to provide early warnings for healthcare systems.

COVID-19 vaccine hesitancy and attitudes in Pakistan: a cross-sectional phone survey of major urban cities

BACKGROUND

COVID-19 mass vaccination is the only hopeful savior to curb the pandemic. Vaccine distribution to achieve herd immunity is hindered by hesitance and negative attitude of the public against COVID-19 vaccination. This study aims to evaluate the vaccine hesitancy and attitudes in major cities in Pakistan as well as their determinants.

METHODS

A cross-sectional telephonic survey was conducted in June 2021 in major cities of Pakistan including Karachi, Lahore, Islamabad, Peshawar, and Gilgit, from unvaccinated urban population aged 18 years or older. Random Digit Dialing through multi-stage stratified random sampling was used to ensure representation of each target city and socio-economic classes. Questionnaire collected information on socio-demographics, COVID-19-related experiences, risk perception of infection, and receptivity of COVID-19 vaccination. Multivariate logistic regression analyses were performed to identify key determinants of vaccine hesitancy and acceptance.

RESULTS

The prevalence of vaccinated population in this survey was 15%. Of the 2270 respondents, 65% respondents were willing to vaccinate, while only 19% were registered for vaccination. Factors significantly associated with vaccine willingness were older age (aOR: 6.48, 95% CI: 1.94-21.58), tertiary education (aOR: 2.02, 95% CI: 1.36, 3.01), being employed (aOR: 1.34, 95% CI: 1.01, 1.78), perceived risk of COVID-19 (aOR: 4.38, 95% CI: 2.70, 7.12), and higher compliance with standard operating procedures (aOR: 1.72, 95% CI: 1.26, 2.35). The most common vaccine hesitancy reasons were 'no need' (n = 284, 36%) and concerns with 'vaccine safety and side effects' (n = 251, 31%), while most reported vaccine motivation reasons were 'health safety' (n = 1029, 70%) and 'to end the pandemic' (n = 357, 24%).

CONCLUSIONS

Although our study found 35% hesitancy rate of COVID-19 vaccine, there were noticeable demographic differences that suggest tailored communication strategy to address concerns held by most hesitant subpopulation. Use of mobile vaccination facilities particularly for less mobile and disadvantaged, and implementation and evaluation of social mobilization strategy should be considered to increase overall COVID-19 vaccination acceptance and coverage.

Assessing parameter sensitivity in a university campus COVID-19 model with vaccinations

From the beginning of the COVID-19 pandemic, universities have experienced unique challenges due to their dual nature as a place of education and residence. Current research has explored non-pharmaceutical approaches to combating COVID-19, including representing in models different categories such as age groups. One key area not currently well represented in models is the effect of pharmaceutical preventative measures, specifically vaccinations, on COVID-19 spread on college campuses. There remain key questions on the sensitivity of COVID-19 infection rates on college campuses to potentially time-varying vaccine immunity. Here we introduce a compartment model that decomposes a campus population into constituent subpopulations and implements vaccinations with time-varying efficacy. We use this model to represent a campus population with both vaccinated and unvaccinated individuals, and we analyze this model using two metrics of interest: maximum isolation population and symptomatic infection. We demonstrate a decrease in symptomatic infections occurs for vaccinated individuals when the frequency of testing for unvaccinated individuals is increased. We find that the number of symptomatic infections is insensitive to the frequency of testing of the unvaccinated subpopulation once about 80% or more of the population is vaccinated. Through a Sobol' global sensitivity analysis, we characterize the sensitivity of modeled infection rates to these uncertain parameters. We find that in order to manage symptomatic infections and the maximum isolation population campuses must minimize contact between infected and uninfected individuals, promote high vaccine protection at the beginning of the semester, and minimize the number of individuals developing symptoms.

Corona virus 19(COVID-19) Conceptual Modeling a Single-Center Prospective: Cross-Sectional Study

BACKGROUND

Conceptual models are abstract representations of the real world. They are used to refine medical and non-medical healthcare scopes of service. During the covid 19 pandemic numerous analytic predictive models were generated aiming to evaluate the impact of policies implemented on the mitigating of COVID-19 pandemic, the psycho-social factors that might govern general population adherence to these policies, identify factors that might affect COVID-19 vaccine uptake and allocation. The outcomes of these analytic models helped set priorities when vaccines were available, and predicted readiness to resume non-COVID-19 healthcare services.

OBJECTIVE

The objective of our research was to implement a descriptive-analytical conceptual model that analyzes the data of all COVID-19-positive cases admitted to our hospital 1st of March to the 31st of May 2020, the initial wave of the pandemic, the time interval during which local policies and clinical guidelines were constantly updated to mitigate the local effects of SARS-CoV-2, minimize mortality, ICU admission, and ensure the safety of healthcare providers. The primary outcome of interest was to identify factors that might affect mortality and ICU admission, and the impact of the policy implemented on SARS-CoV-2 positivity among healthcare providers. The secondary outcome of interest was to evaluate the sensitivity of the SARS-coV-2 visual score implemented by the Saudi MOH for COVID-19- risk assessment as well as CURB-65 scores in predicting ICU admission or mortality among the study population.

METHODS

This was a cross-sectional study. The relevant attributes were constructed based on research findings from the first wave of the pandemic and were electronically retrieved from the hospital database. Analysis of the conceptual model was based on the International Society for Pharmacoeconomics and Outcomes Research guidelines and the Society for Medical Decision-Making.

RESULTS

275 were SARS-CoV-2- positive within the study design interval. The conceptualization model revealed a low-risk population based on the following attributes: the mean age was 42 ± 19.2 years, 19% of the study population were senior adults ≥ 60 years, 80% had a CURB-65 score < 4, 53% had no comorbidities, 5% had extreme obesity, and 2% had a significant hematological abnormality. The overall rate of ICU admission for the study population was 5%, with a 1.5% overall mortality. The multivariate correlation analysis revealed that high selectivity was adopted, wherein patients with complex medical problems were not sent to MOH isolation facilities. Furthermore, 5% of healthcare providers were SARS-CoV-2-positive, and none were healthcare providers allocated to the COVID-19 screening areas indicating the effectiveness of the policy implemented to ensure the safety of healthcare providers.

CONCLUSIONS

Based on the conceptual model outcome, the selectivity applied to retaining high-risk populations within the hospital might have contributed to the low mortality rate observed without increasing the risk to attending healthcare providers.

CLINICALTRIAL

Not applicable.

The association of care burden with motivation of vaccine acceptance among caregivers of stroke patients during the COVID-19 pandemic: mediating roles of problematic social media use, worry, and fear

BACKGROUND

The aim of the present study was to investigate the relationship between care burden and motivation of COVID-19 vaccine acceptance among caregivers of patients who have experienced a stroke and to explore the mediating roles of social media use, fear of COVID-19, and worries about infection in this relationship.

METHODS

A cross-sectional survey study with 172 caregivers of patients who had experienced a stroke took part in a Taiwan community hospital. All participants completed the Zarit Burden Interview, Bergen Social Media Addiction Scale, Worry of Infection Scale, Fear of COVID-19 Scale, and Motors of COVID-19 Vaccine Acceptance Scale. Multiple linear regression model was applied to construct and explain the association among the variables. Hayes Process Macro (Models 4 and 6) was used to explain the mediation effects.

RESULTS

The proposed model significantly explained the direct association of care burden with motivation of COVID-19 vaccine acceptance. Despite the increased care burden associated with decreased vaccine acceptance, problematic social media use positively mediated this association. Moreover, problematic social media use had sequential mediating effects together with worry of infection or fear of COVID-19 in the association between care burden and motivation of vaccine acceptance. Care burden was associated with motivation of vaccine acceptance through problematic social media use followed by worry of infection.

CONCLUSIONS

Increased care burden among caregivers of patients who have experienced a stroke may lead to lower COVID-19 vaccines acceptance. Moreover, problematic social media use was positively associated with their motivation to get COVID-19 vaccinated. Therefore, health experts and practitioners should actively disseminate accurate and trustworthy factual information regarding COVID-19, while taking care of the psychological problems among caregivers of patients who have experienced a stroke.

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.

Assessing the Impact of Vaccination on the Dynamics of COVID-19 in Africa: A Mathematical Modeling Study

Several effective COVID-19 vaccines are administered to combat the COVID-19 pandemic globally. In most African countries, there is a comparatively limited deployment of vaccination programs. In this work, we develop a mathematical compartmental model to assess the impact of vaccination programs on curtailing the burden of COVID-19 in eight African countries considering SARS-CoV-2 cumulative case data for each country for the third wave of the COVID-19 pandemic. The model stratifies the total population into two subgroups based on individual vaccination status. We use the detection and death rates ratios between vaccinated and unvaccinated individuals to quantify the vaccine's effectiveness in reducing new COVID-19 infections and death, respectively. Additionally, we perform a numerical sensitivity analysis to assess the combined impact of vaccination and reduction in the SARS-CoV-2 transmission due to control measures on the control reproduction number (Rc). Our results reveal that on average, at least 60% of the population in each considered African country should be vaccinated to curtail the pandemic (lower the Rc below one). Moreover, lower values of Rc are possible even when there is a low (10%) or moderate (30%) reduction in the SARS-CoV-2 transmission rate due to NPIs. Combining vaccination programs with various levels of reduction in the transmission rate due to NPI aids in curtailing the pandemic. Additionally, this study shows that vaccination significantly reduces the severity of the disease and death rates despite low efficacy against COVID-19 infections. The African governments need to design vaccination strategies that increase vaccine uptake, such as an incentive-based approach.

Indirect Dispersion of SARS-CoV-2 Live-Attenuated Vaccine and Its Contribution to Herd Immunity

It has been 34 months since the beginning of the SARS-CoV-2 coronavirus pandemic, which causes the COVID-19 disease. In several countries, immunization has reached a proportion near what is required to reach herd immunity. Nevertheless, infections and re-infections have been observed even in vaccinated persons. That is because protection conferred by vaccines is not entirely effective against new virus variants. It is unknown how often booster vaccines will be necessary to maintain a good level of protective immunity. Furthermore, many individuals refuse vaccination, and in developing countries, a large proportion of the population has not yet been vaccinated. Some live-attenuated vaccines against SARS-CoV-2 are being developed. Here, we analyze the indirect dispersion of a live-attenuated virus from vaccinated individuals to their contacts and the contribution that this phenomenon could have to reaching Herd Immunity.

Comparison of Immunogenicity and Reactogenicity of Five Primary Series of COVID-19 Vaccine Regimens against Circulating SARS-CoV-2 Variants of Concern among Healthy Thai Populations

To compare immunogenicity and reactogenicity of five COVID-19 vaccine regimens against wild-type SARS-CoV-2 and variants of concern (VoCs) among Thai populations, a prospective cohort study was conducted among healthy participants aged ≥18 years who had never been infected with COVID-19 and were scheduled to get one of the five primary series of COVID-19 vaccine regimens, including CoronaVac/CoronaVac, AZD1222/AZD1222, CoronaVac/AZD1222, AZD1222/BNT162b2, and BNT162b2/BNT162b2. Anti-receptor binding domain (anti-RBD-WT) IgG and neutralizing antibody (NAb-WT) against wild-type SARS-CoV-2 were measured at pre-prime, post-prime, and post-boost visits. NAb against VoCs (NAb-Alpha, NAb-Beta, NAb-Delta, and NAb-Omicron) were assessed at the post-boost visit. Adverse events (AEs) following vaccination were recorded. A total of 901 participants (CoronaVac/CoronaVac: 332, AZD1222/AZD1222: 221, CoronaVac/AZD1222: 110, AZD1222/BNT162b2: 128, and BNT162b2/BNT162b2: 110) were enrolled. Anti-RBD-WT IgG and NAb-WT levels increased substantially after each vaccine dose. At the post-boost visit, BNT162b2/BNT162b2 induced the highest GMC of anti-RBD-WT IgG level (1698 BAU/mL), whereas AZD1222/BNT162b2 induced the highest median NAb-WT level (99% inhibition). NAb levels against VoCs, particularly the Omicron strain, were markedly attenuated for all vaccine regimens (p < 0.001). Overall, no serious AEs following vaccination were observed. All five primary series of COVID-19 vaccine regimens were well-tolerated and elicited robust antibody responses against wild-type SARS-CoV-2 but had attenuated responses against VoCs, particularly the Omicron strain, among healthy Thai populations.

Mother’s Pregnancy Trimester Does Not Affect the Differences of IgG SARS-COV-2 Antibody Levels in Pregnant Women after mRNA and Inactivated Coronavirus Disease 2019 Vaccination

BACKGROUND: Since pregnancy increases the risk of coronavirus disease 2019 (COVID-19) and its morbidity in pregnant women, it is necessary and recommended to prevent COVID-19 in pregnant women by vaccination such as by messenger RNA (mRNA) and inactivated vaccines. SARS-CoV-2 antibodies produced from vaccination have different results according to the type of vaccine given. The previous studies showed that IgG SARS-CoV-2 antibody levels were influenced by various factors such as gestational weeks at the time when vaccines were given. Moreover, there have been no previous studies on the effect of gestational age on quantitative IgG levels after the second dose of the vaccine especially in Indonesia during this pandemic due to some restrictions on daily activities. AIM: The aim of this study is to see the effect of giving the COVID-19 vaccine based on maternal gestational age (in trimester units) on maternal immunity (IgG SARS-CoV-2) in Dr. Hasan Sadikin General Hospital Bandung, Bandung Kiwari Hospital and Dr. Slamet Hospital, Garut. METHODS: This was a retrospective and cohort study by taking secondary data using consecutive sampling from the previous tests on the levels of SARS-CoV-2 IgG antibodies after two doses of inactivated vaccine and mRNA. Healthy pregnant women 14–34 weeks at the Department of Obstetrics and Gynecology, Dr. Hasan Sadikin (RSHS) Bandung, Bandung Kiwari Hospital, and Dr. Slamet Hospital for the period October 2021 to January 2022 were the target population of this study. Based on inclusion and exclusion criteria, 103 samples met the criteria. Examination of Maternal SARS-CoV-2 IgG Antibody Levels procedures was carried out using Chemiluminescent Microparticle Immunoassay. Statistical analysis was done using IBM SPSS 28.00 and p < 0.05 was considered statistically significant. RESULTS: There was no significant difference (p = 0.236, p > 0.05) between the mean maternal age in the mRNA and inactivated vaccine groups. The mRNA and inactivated vaccine groups also had no significant difference in the gestational age category (0.70). There was a significant difference (p = 0.0001) between the levels of SARS-CoV-2 IgG antibodies after the vaccine in the mRNA and inactivated vaccine groups. There was no significant difference in the levels of SARS-CoV-2 IgG antibodies in the gestational age group after the mRNA vaccine (p = 0.426) and after the inactivated vaccine (p = 0.293). There was a significant difference (p < 0.05) in the subgroup analysis in each gestational age group (second trimester and third trimester) between SARS-CoV-2 IgG antibody levels after the mRNA vaccine compared to inactivated vaccine. DISCUSSIONS: The mRNA vaccine is based on the principle that mRNA is an intermediate messenger to be translated to an antigen after delivery to the host cell via various routes. However, inactivated vaccines contain viruses whose genetic material has been destroyed by heat, chemicals, or radiation, so they cannot infect cells and replicate but can still trigger an immune response. The administration of the vaccine in the second and third trimesters of pregnancy has the same results in increasing levels of SARS-CoV-2 IgG antibodies after mRNA and inactivated vaccination in this study. CONCLUSIONS: mRNA vaccination in pregnant women is better than inactivated vaccines based on the levels of IgG SARS-CoV-2 antibodies after vaccination. The maternal trimester of pregnancy was not a factor influencing the levels of SARS-CoV-2 IgG antibodies after either mRNA or inactivated COVID-19 vaccinations in this study.

The race Against Time to Save Human Lives During the COVID-19 With Vaccines: Global Evidence

Voluminous vaccine campaigns have been used globally, since the COVID-19 pandemic has brought devastating mortality and destructively unprecedented consequences to different aspects of economies. This study aimed to identify how the numbers of new deaths and new cases per million changed after half of the population had been vaccinated. This paper used actual pandemic consequence variables (death and infected rates) together with vaccination uptake rates from 127 countries to shed new light on the efficacy of COVID-19 vaccines. The 50% uptake rate was chosen as the threshold to estimate the real benefits of vaccination campaigns for reducing COVID-19 infection and death cases using the difference-in-differences (DiD) imputation estimator. In addition, a number of control variables, such as government interventions and people's mobility patterns during the pandemic, were also included in the study. The number of new deaths per million significantly decreased after half of the population was vaccinated, but the number of new cases did not change significantly. We found that the effects were more pronounced in Europe and North America than in other continents. Our results remain robust after using other proxies and testing the sensitivity of the vaccinated proportion. We show the causal evidence of significantly lower death rates in countries where half of the population is vaccinated globally. This paper expresses the importance of vaccine campaigns in saving human lives during the COVID-19 pandemic, and its results can be used to communicate the benefits of vaccines and to fight vaccine hesitancy.

Competing control scenarios in probabilistic SIR epidemics on social-contact networks

A probabilistic approach to the epidemic evolution on realistic social-contact networks allows for characteristic differences among subjects, including the individual number and structure of social contacts, and the heterogeneity of the infection and recovery rates according to age or medical preconditions. Within our probabilistic Susceptible-Infectious-Removed (SIR) model on social-contact networks, we evaluate the infection load or activation margin of various control scenarios; by confinement, by vaccination, and by their combination. We compare the epidemic burden for subpopulations that apply competing or cooperative control strategies. The simulation experiments are conducted on randomized social-contact graphs that are designed to exhibit realistic person-person contact characteristics and which follow near homogeneous or block-localized subpopulation spreading. The scalarization method is used for the multi-objective optimization problem in which both the infection load is minimized and the extent to which each subpopulation's control strategy preference ranking is adhered to is maximized. We obtain the compounded payoff matrices for two subpopulations that impose contrasting control strategies, each according to their proper ranked control strategy preferences. The Nash equilibria, according to each subpopulation's compounded objective, and according to their proper ranking intensity, are discussed. Finally, the interaction effects of the control strategies are discussed and related to the type of spreading of the two subpopulations.

Factors Associated with COVID-19 Vaccination Acceptance Among Muslim High School Students in Jakarta Metropolitan Area, Indonesia

Purpose

This study aims to identify the factors associated with COVID-19 vaccination uptake and the intention to receive the COVID-19 vaccine among Muslim high school students in Jakarta, Indonesia.

Patients and Methods

A cross-sectional study using an online survey was conducted for high school students. The population in this study were Muslim students grade 11 and 12 at secondary high school living and studying in Jakarta Metropolitan area. A total of 440 samples have been selected using non-probability sampling. In this study, the independent variables were factors associated with vaccination behaviors that were derived from the HBM and TPB theories, while the dependent variables were vaccination uptake and vaccination intention. Factors associated with the dependent variables have been identified using chi-square and Mann-Whitney tests.

Results

About 65% respondents had received COVID-19 vaccine and 72% of those who had not received COVID-19 vaccine had the intention to receive the vaccine. Some of the behavioral model variables such as the perceived susceptibility score, perceived severity score, perceived benefits score, perceived barriers score, self-efficacy score, attitude score, and social norms score were significantly associated with COVID-19 vaccine uptake among high school students. Furthermore, all of the behavioral model variables, ie, perceived susceptibility score, perceived severity score, perceived benefits score, perceived barriers score, cues to action score, self-efficacy score, attitude score, social norms score, and perceived behavioral control score were significantly associated with an intention of being vaccinated.

Conclusion

The vaccination for students can be set in more affordable locationsfor example, in schools. In addition, efforts to increase student knowledge regarding the effectiveness and safety of vaccines and the dangers of COVID-19 should be sustained.

Detection of multiple waves for COVID-19 and its optimal control through media awareness and vaccination: study based on some Indian states

COVID-19 is a highly infectious disease, and in very recent times, it has shown a massive impact throughout the globe. Several countries faced the COVID-19 infection waves multiple times. These later waves are more aggressive than the first wave and drastically impact social and economic factors. We developed a mechanistic model with imperfect lockdown effect, reinfection, transmission variability between symptomatic & asymptomatic, and media awareness to focus on the early detection of multiple waves and their control measures. Using daily COVID-19 cases data from six states of India, we estimated several important model parameters. Moreover, we estimated the home quarantine, community, and basic reproduction numbers. We developed an algorithm to carry out global sensitivity analysis (Sobol) of the parameters that influence the number of COVID-19 waves ( W C ) and the average number of COVID-19 cases in a wave ( A W ). We have identified some critical controlling parameters that mainly influenced W C and A W . Our study also revealed the best COVID-19 control strategy/strategies among vaccination, media awareness, and their combination using an optimal cost-effective study. The detailed analysis suggests that the severity of asymptomatic transmission is around 10% to 29% of that of symptomatic transmission in all six locations. About 1% to 4% of the total population under lockdown may contribute to new COVID-19 infection in all six locations. Optimal cost-effective analysis based on interventions, namely only vaccination (VA), only media awareness (ME), and a combination of vaccination & media (VA+ME), are projected for the period March 14, 2020, to August 31, 2021, for all the six locations. We have found that a large percentage of the population (26% to 45%) must be vaccinated from February 13 to August 31, 2021, to avert an optimal number of COVID-19 cases in these six locations.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11071-022-07887-5.

Modelling herd immunity requirements in Queensland: impact of vaccination effectiveness, hesitancy and variants of SARS-CoV-2

Long-term control of SARS-CoV-2 outbreaks depends on the widespread coverage of effective vaccines. In Australia, two-dose vaccination coverage of above 90% of the adult population was achieved. However, between August 2020 and August 2021, hesitancy fluctuated dramatically. This raised the question of whether settings with low naturally derived immunity, such as Queensland where less than [Formula: see text] of the population is known to have been infected in 2020, could have achieved herd immunity against 2021's variants of concern. To address this question, we used the agent-based model Covasim. We simulated outbreak scenarios (with the Alpha, Delta and Omicron variants) and assumed ongoing interventions (testing, tracing, isolation and quarantine). We modelled vaccination using two approaches with different levels of realism. Hesitancy was modelled using Australian survey data. We found that with a vaccine effectiveness against infection of 80%, it was possible to control outbreaks of Alpha, but not Delta or Omicron. With 90% effectiveness, Delta outbreaks may have been preventable, but not Omicron outbreaks. We also estimated that a decrease in hesitancy from 20% to 14% reduced the number of infections, hospitalizations and deaths by over 30%. Overall, we demonstrate that while herd immunity may not be attainable, modest reductions in hesitancy and increases in vaccine uptake may greatly improve health outcomes. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Current clinical status of new COVID-19 vaccines and immunotherapy

COVID-19, caused by SARS-CoV-2, is a positive-strand RNA belonging to Coronaviridae family, along with MERS and SARS. Since its first report in 2019 in Wuhan, China, it has affected over 530 million people and led to 6.3 million deaths worldwide until June 2022. Despite eleven vaccines being used worldwide already, new variants are of concern. Therefore, the governing bodies are re-evaluating the strategies for achieving universal vaccination. Initially, the WHO expected that vaccines showing around 50-80% efficacy would develop in 1-2 years. However, US-FDA announced emergency approval of the two m-RNA vaccines within 11 months of vaccine development, which enabled early vaccination for healthcare workers in many countries. Later, in January 2021, 63 vaccine candidates were under human clinical trials and 172 under preclinical development. Currently, the number of such clinical studies is still increasing. In this review, we have summarized the updates on the clinical status of the COVID-19 and the available treatments. Additionally, COVID-19 had created negative impacts on world's economy; affected agriculture, industries, and tourism service sectors; and majorly affected low-income countries. The review discusses the clinical outcomes, latest statistics, socio-economic impacts of pandemic and treatment approaches against SARS-CoV-2, and strategies against the new variant of concern. The review will help understand the current status of vaccines and other therapies while also providing insights about upcoming vaccines and therapies for COVID-19 management.

Impact of incentives on COVID-19 vaccination; A systematic review

Introduction

Although vaccination is the most effective way to limit and overcome the COVID-19 pandemic, a considerable fraction of them are not intended to get vaccinated. This study aims to investigate the existing research evidence and evaluate the effectiveness and consequences of all incentives provided for increasing the uptake of COVID-19 vaccination.

Methods

A systematic search in PubMed, Web of Science (WoS), and SCOPUS from 2020 until October 10, 2021, was conducted on experimental studies evaluating the effects of incentives including cash, lottery voucher, and persuasive messages on COVID-19 vaccination intention and uptake. The study selection process, data extraction, and quality assessment were conducted independently by two investigators using Consolidated Standards of Reporting Trials (CONSORT 2010) checklist.

Results

Twenty-four records were included in the qualitative analysis. Most of the included studies assessed the effect of financial incentives. In 14 studies (58%) the assessed outcome was vaccination uptake and in nine (37.5%) others it was vaccination intention. One study considered self-reported vaccination status as the outcome. This study shows that high financial incentives and the Vax-a-million lottery are attributed to a higher vaccination rate, while the low amount of financial incentives, other lotteries, and persuasive messages have small or non-significant effects.

Conclusion

Paying a considerable amount of cash and Vax-a-million lottery are attributed to a higher vaccination. Nevertheless, there is a controversy over the effect of other incentives including other lotteries, low amount of cash, and messages on vaccination. It is noteworthy that, inconsistency and imprecision of included studies should be considered.

Trends in Confirmed COVID-19 Cases in the Korean Military Before and After the Emergence of the Omicron Variant

BACKGROUND

Due to the higher transmissibility and increased immune escape of the omicron variant of severe acute respiratory syndrome coronavirus 2, the number of patients with coronavirus disease 2019 (COVID-19) has skyrocketed in the Republic of Korea. Here, we analyzed the change in trend of the number of confirmed COVID-19 cases in the Korean military after the emergence of the omicron variant on December 5, 2021.

METHODS

An interrupted time-series analysis was performed of the daily number of newly confirmed COVID-19 cases in the Korean military from September 1, 2021 to April 10, 2022, before and after the emergence of the omicron variant. Moreover, the daily number of newly confirmed COVID-19 cases in the Korean military and in the population of Korean civilians adjusted to the same with military were compared.

RESULTS

The trends of COVID-19 occurrence in the military after emergence of the omicron variant was significantly increased (regression coefficient, 23.071; 95% confidence interval, 16.122-30.020; P < 0.001). The COVID-19 incidence rate in the Korean military was lower than that in the civilians, but after the emergence of the omicron variant, the increased incidence rate in the military followed that of the civilian population.

CONCLUSION

The outbreak of the omicron variant occurred in the Korean military despite maintaining high vaccination coverage and intensive non-pharmacological interventions.

Modelling vaccination capacity at mass vaccination hubs and general practice clinics: a simulation study

Background

COVID-19 mass vaccination programs place an additional burden on healthcare services. We aim to model the queueing process at vaccination sites to inform service delivery.

Methods

We use stochastic queue network models to simulate queue dynamics in larger mass vaccination hubs and smaller general practice (GP) clinics. We estimate waiting times and daily capacity based on a range of assumptions about appointment schedules, service times and staffing and stress-test these models to assess the impact of increased demand and staff shortages. We also provide an interactive applet, allowing users to explore vaccine administration under their own assumptions.

Results

Based on our assumed service times, the daily throughput for an eight-hour clinic at a mass vaccination hub ranged from 500 doses for a small hub to 1400 doses for a large hub. For GP clinics, the estimated daily throughput ranged from about 100 doses for a small practice to almost 300 doses for a large practice. What-if scenario analysis showed that sites with higher staff numbers were more robust to system pressures and mass vaccination sites were more robust than GP clinics.

Conclusions

With the requirement for ongoing COVID-19 booster shots, mass vaccination is likely to be a continuing feature of healthcare delivery. Different vaccine sites are useful for reaching different populations and maximising coverage. Stochastic queue networks offer a flexible and computationally efficient approach to simulate vaccination queues and estimate waiting times and daily throughput to inform service delivery.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-08447-8.

From Policy to Prediction: Forecasting COVID-19 Dynamics Under Imperfect Vaccination

Understanding the joint impact of vaccination and non-pharmaceutical interventions on COVID-19 development is important for making public health decisions that control the pandemic. Recently, we created a method in forecasting the daily number of confirmed cases of infectious diseases by combining a mechanistic ordinary differential equation (ODE) model for infectious classes and a generalized boosting machine learning model (GBM) for predicting how public health policies and mobility data affect the transmission rate in the ODE model (Wang et al. in Bull Math Biol 84:57, 2022). In this paper, we extend the method to the post-vaccination period, accordingly obtain a retrospective forecast of COVID-19 daily confirmed cases in the US, and identify the relative influence of the policies used as the predictor variables. In particular, our ODE model contains both partially and fully vaccinated compartments and accounts for the breakthrough cases, that is, vaccinated individuals can still get infected. Our results indicate that the inclusion of data on non-pharmaceutical interventions can significantly improve the accuracy of the predictions. With the use of policy data, the model predicts the number of daily infected cases up to 35 days in the future, with an average mean absolute percentage error of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$20.15\%$$\end{document}20.15%, which is further improved to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$14.88\%$$\end{document}14.88% if combined with human mobility data. Moreover, the most influential predictor variables are the policies of restrictions on gatherings, testing and school closing. The modeling approach used in this work can help policymakers design control measures as variant strains threaten public health in the future.

Bi-objective optimization for a multi-period COVID-19 vaccination planning problem

This work investigates a new multi-period vaccination planning problem that simultaneously optimizes the total travel distance of vaccination recipients (service level) and the operational cost. An optimal plan determines, for each period, which vaccination sites to open, how many vaccination stations to launch at each site, how to assign recipients from different locations to opened sites, and the replenishment quantity of each site. We formulate this new problem as a bi-objective mixed-integer linear program (MILP). We first propose a weighted-sum and an ϵ -constraint methods, which rely on solving many single-objective MILPs and thus lose efficiency for practical-sized instances. To this end, we further develop a tailored genetic algorithm where an improved assignment strategy and a new dynamic programming method are designed to obtain good feasible solutions. Results from a case study indicate that our methods reduce the operational cost and the total travel distance by up to 9.3% and 36.6%, respectively. Managerial implications suggest enlarging the service capacity of vaccination sites can help improve the performance of the vaccination program. The enhanced performance of our heuristic is due to the newly proposed assignment strategy and dynamic programming method. Our findings demonstrate that vaccination programs during pandemics can significantly benefit from formal methods, drastically improving service levels and decreasing operational costs.

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.

Riding the Pandemic Waves—Lessons to Be Learned from the COVID-19 Crisis Management in Romania

In our analysis, we assessed how Romania dealt with the numerous challenges presented by the COVID-19 pandemic during 2021. In that year, the government had to deal with two waves of COVID-19 pandemics caused by the new variants, the low vaccination rate of the population, the overload of the healthcare system and political instability at the same time. Based on publicly available databases and international literature, we evaluated government measures aimed at reducing the spread of the pandemic and ensure the operation of the healthcare workforce and infrastructure. In addition, we evaluated measures to provide health services effectively and the government’s pandemic responses regarding excess mortality in 2021. In the absence of a complex monitoring system, limited information was available on the spread of the pandemic or the various risk factors at play. Due to incomplete and inadequate management systems, the government was unable to implement timely and adequate measures. Our analysis concludes that the management of a pandemic can only be successful if data are collected and evaluated using complex systems in a timely manner, and if members of society adhere to clearly communicated government measures due to high levels of trust in the government.

Risk-based cost-benefit analysis of alternative vaccines against COVID-19 in Brazil: Coronavac vs. Astrazeneca vs. Pfizer

We propose a probabilistic model to quantify the cost-benefit of mass Vaccination Scenarios (VSs) against COVID-19. Through this approach, we conduct a six-month simulation, from August 31st, 2021 to March 3rd, 2022, of nine VSs, i.e., the three primary vaccine brands in Brazil (CoronaVac, AstraZeneca and Pfizer), each with three different vaccination rates (2nd doses per week). Since each vaccine has different individual-level effectiveness, we measure the population-level benefit as the probability of reaching herd immunity (HI). We quantify and categorize the cost-benefit of VSs through risk graphs that show: (i) monetary cost vs. probability of reaching HI; and (ii) number of new deaths vs. probability of reaching HI. Results show that AstraZeneca has the best cost-benefit when prioritizing acquisition costs, while Pfizer is the most cost-beneficial when prioritizing the number of deaths. This work provides helpful information that can aid public health authorities in Brazil to better plan VSs. Furthermore, our approach is not restricted to Brazil, the COVID-19 pandemic, or the mentioned vaccine brands. Indeed, the method is flexible so that this study can be a valuable reference for future cost-benefit analyses in other countries and pandemics, especially in the early stages of vaccination, when data is scarce and uncertainty is high.

Predicting the Effects of Waning Vaccine Immunity Against COVID-19 through High-Resolution Agent-Based Modeling

The potential waning of the vaccination immunity to COVID-19 could pose threats to public health, as it is tenable that the timing of such waning would synchronize with the near-complete restoration of normalcy. Should also testing be relaxed, a resurgent COVID-19 wave in winter 2021/2022 might be witnessed. In response to this risk, an additional vaccine dose, the booster shot, is being administered worldwide. A projected study with an outlook of 6 months explores the interplay between the rate at which boosters are distributed and the extent to which testing practices are implemented, using a highly granular agent-based model tuned on a medium-sized US town. Theoretical projections indicate that the administration of boosters at the rate at which the vaccine is currently administered could yield a severe resurgence of the pandemic. Projections suggest that the peak levels of mid-spring 2021 in the vaccination rate may prevent such a scenario to occur, although exact agreement between observations and projections should not be expected due to the continuously evolving nature of the pandemic. This study highlights the importance of testing, especially to detect asymptomatic individuals in the near future, as the release of the booster reaches full speed.

Simulating a Hockey Hub COVID-19 Mass Vaccination Facility

Mass vaccination is proving to be the most effective method of disease control, and several methods have been developed for the operation of mass vaccination clinics to administer vaccines safely and quickly. One such method is known as the hockey hub model, a relatively new method that involves isolating vaccine recipients in individual cubicles for the entire duration of the vaccination process. Healthcare staff move between the cubicles and administer vaccines. This allows for faster vaccine delivery and less recipient contact. In this paper we present a simulation tool which has been created to model the operation of a hockey hub clinic. This tool was developed using AnyLogic and simulates the process of individuals moving through a hockey hub vaccination clinic. To demonstrate this model, we simulate six scenarios comprising three different arrival rates with and without physical distancing. Findings demonstrate that the hockey hub method of vaccination clinic can function at a large capacity with minimal impact on wait times.