Importance of COVID-19 vaccine efficacy in older age groups

COVID-19 vaccine hesitancy among the Chinese elderly: A multi-stakeholder qualitative study

The United Nations reported that the mortality risk of Corona Virus Disease 2019 (COVID-19) is five times higher in the elderly than the global average. Although the COVID-19 vaccine effectively prevents infections and reduce mortality among the elderly, vaccine hesitancy among the Chinese elderly poses a significant threat. This study, utilizing the "Confidence, Convenience and Complacency (3 Cs)" vaccine hesitancy model, aimed to explore factors contributing to vaccine hesitancy among the Chinese elderly and assess national countermeasures and potential improvement approaches. Thirteen elderly with vaccine hesitancy and eleven vaccine-related staff participated in semi-structured interviews. Thematic analysis revealed three key determinants of vaccine hesitancy among the elderly: perceived low threat of COVID-19, lack of confidence in COVID-19 vaccine, and poor accessibility to vaccination. China has implemented strategies, including advocacy through diverse channels, joint multi-sectoral promotion vaccination, and enhancing ongoing vaccination services. Recommendations from the vaccine-related staff emphasize improving vaccine awareness among the elderly, and prioritizing the vaccination environment and process. The study underscores the importance of targeted vaccination promotion programs addressing hesitation reasons to improve vaccination rates. Furthermore, existing countermeasures can serve as a foundation for enhancing vaccination strategies, including improved publicity, administration, and management approaches.

Immune aging and infectious diseases

ABSTRACT

The rise in global life expectancy has led to an increase in the older population, presenting significant challenges in managing infectious diseases. Aging affects the innate and adaptive immune systems, resulting in chronic low-grade inflammation (inflammaging) and immune function decline (immunosenescence). These changes would impair defense mechanisms, increase susceptibility to infections and reduce vaccine efficacy in older adults. Cellular senescence exacerbates these issues by releasing pro-inflammatory factors, further perpetuating chronic inflammation. Moreover, comorbidities, such as cardiovascular disease and diabetes, which are common in older adults, amplify immune dysfunction, while immunosuppressive medications further complicate responses to infections. This review explores the molecular and cellular mechanisms driving inflammaging and immunosenescence, focusing on genomic instability, telomere attrition, and mitochondrial dysfunction. Additionally, we discussed how aging-associated immune alterations influence responses to bacterial, viral, and parasitic infections and evaluated emerging antiaging strategies, aimed at mitigating these effects to improve health outcomes in the aging population.

Effect of vaccinations and school restrictions on the spread of COVID-19 in different age groups in Germany

With the emergence of SARS-CoV-2, various non-pharmaceutical interventions were adopted to control virus transmission, including school closures. Subsequently, the introduction of vaccines mitigated not only disease severity but also the spread of SARS-CoV-2. This study leveraged an adapted SIR model and non-linear mixed-effects modeling to quantify the impact of remote learning, school holidays, the emergence of Variants of Concern (VOCs), and the role of vaccinations in controlling SARS-CoV-2 spread across 16 German federal states with an age-stratified approach. Findings highlight a significant inverse correlation (Spearman's ρ = -0.92, p < 0.001) between vaccination rates and peak incidence rates across all age groups. Model-parameter estimation using the observed number of cases stratified by federal state and age allowed to assess the effects of school closure and holidays, considering adjustments for vaccinations and spread of VOCs over time. Here, modeling revealed significant (p < 0.001) differences in the virus's spread among pre-school children (0-4), children (5-11), adolescents (12-17), adults (18-59), and the elderly (60+). The transition to remote learning emerged as a critical measure in significantly reducing infection rates among children and adolescents (p < 0.001), whereas an increased infection risk was noted among the elderly during these periods, suggesting a shift in infection networks due to altered caregiving roles. Conversely, during school holiday periods, infection rates among adolescents mirrored those observed when schools were open. Simulation exercises based on the model provided evidence that COVID-19 vaccinations might serve a dual purpose: they protect the vaccinated individuals and contribute to the broader community's safety.

Dynamics of infectious disease mathematical model through unsupervised stochastic neural network paradigm

The viruses has spread globally and have been impacted lives of people socially and economically, which causes immense suffering throughout the world. Thousands of people died and millions of illnesses were brought, by the outbreak worldwide. In order to control the coronavirus pandemic, mathematical modeling proved to be an invaluable tool for analyzing and determining the potential and severity of the illness. This work proposed and assessed a deterministic six-compartment model with a novel stochastic neural network. The significance of the proposed model was demonstrated by numerical simulation in which the results are agreed with sensitivity analysis. Furthermore, the efficacy of stochastic neural network has been proven with the help of numerical simulations. Some investigations have been conducted through graphs and tables that how the vaccination process is helpful to minimize stress in society. The numerical simulations also focused on preventing the community-wide spread of the disease. The lowest residual errors have been achieved by our proposed stochastic neural network and compared with numerical solvers to assess the accuracy and robustness of the proposed approach.

Cost effectiveness analysis comparing varying booster intervals of vaccination policies to address COVID-19 situation in Thailand, 2023

The COVID-19 booster immunization policy is cost-effective, but evidence on additional booster doses and appropriate strategies is scarce. This research compared the cost-effectiveness of annual, twice-a-year, and biennial booster dose policies. We performed stochastic modeling using compartmental susceptible-exposed-infectious-recovered models and a system dynamic model. We evaluated four policy scenarios: (1) hypothetical no-booster immunization policy; (2) twice-a-year vaccination policy; (3) annual vaccination policy; and (4) biennial vaccination policy. In addition, we conducted a one-way sensitivity analysis by adjusting R0 from 1.8 to 3.0 in all scenarios (epidemic stage) and by decreasing the vaccination cost by 50% at the end of the first year to reflect the current policy direction to enhance domestic vaccine production. Compared to non-booster policies, all three booster strategies reduced the number of cases, hospital admissions, and severe infections remarkably. Without a booster, total cases would reach 16,220,615 (95% confidence interval [CI] 6,726,550-29,661,112) by day 1,460, whereas, with a twice-a-year booster, the total cases would reach 597,901 (95% CI 526,230-694,458) in the same period. Even though the no booster scenario exhibited the lowest cost by approximately the first 500 days, by day 1,460 the biennial booster scenario demonstrated the lowest cost at 72.0 billion baht (95% CI 68.6-79.4 billion). The most cost-saving policy was the biennial booster scenario. The annual booster scenario also stood as a cost-effective option for most outcomes. In the epidemic stage and in an assumption where the vaccination costs dropped, all booster policies became more cost-effective or cost-saving compared with the main assumption. This study underscores the significance of the COVID-19 vaccine booster policy. Implementing policies should take into consideration cost-effectiveness, feasibility, and public communication.

Assessing the Impact of COVID-19 Vaccination Programs on the Reduction of COVID-19 Cases: A Systematic Literature Review

Background: Vaccination is the most effective way to prevent serious illness and death from COVID-19 among the various preventive interventions available. Objective: This review aimed to assess the actual effectiveness of COVID-19 vaccines in curbing the transmission and incidence of COVID-19 cases, to examine the role of different vaccine types in controlling the COVID-19 pandemic, as well as to identify the key factors influencing the efficacy of COVID-19 vaccines in containing the spread of the virus. Methods: The suggestions made by the PRISMA Framework were adhered to. To find the publications for the 2020-2023 timeframe, searches were performed through the PubMed databases, EMBASE, Scopus, and ProQuest. For the review, 17 reports satisfied the inclusion requirements. Ad26.CoV2.S or ChAdOx1-S, Gam-COVID-Vac(GAM), Sinovac Life Sciences Co., Oxford-AstraZeneca, Pfizer-BioNTech, and viral vector vaccines are among the vaccines that act on various variations. They dealt with the Delta, B.1.1.519, Omicron, and Alpha variations. Findings: Vaccinations against various Variants resulted in fewer COVID-19 infections, fewer deaths, and fewer hospitalizations. The emergency of the Delta variant, persons over 60, and vaccine hesitancy were the main issues affecting the effectiveness of COVID-19 vaccinations in containing the virus's spread. Conclusion: The collective evidence strongly supports the conclusion that COVID-19 vaccination plays a crucial role in mitigating the spread of the virus and reducing the severity of illness among those who contract the virus.

A systematic review and meta-analysis on the effectiveness of bivalent mRNA booster vaccines against Omicron variants

BACKGROUND

A global shift to bivalent mRNA vaccines is ongoing to counterbalance the diminishing effectiveness of the original monovalent vaccines due to the evolution of SARS-CoV-2 variants, yet substantial variation in the bivalent vaccine effectiveness (VE) exists across studies and a complete picture is lacking.

METHODS

We searched papers evaluating absolute or relative effectiveness of SARS-CoV-2 BA.1 type or BA.4/5 type bivalent mRNA vaccines on eight publication databases published from September 1st, 2022, to November 8th, 2023. Pooled VE against Omicron-associated infection and severe events (hospitalization and/or death) was estimated in reference to unvaccinated, ≥2 original monovalent doses, and ≥ 3 original monovalent doses.

RESULTS

From 630 citations identified, 28 studies were included, involving 55,393,303 individuals. Bivalent boosters demonstrated higher effectiveness against symptomatic or any infection for all ages combined, with an absolute VE of 53.5 % (95 % CI: -22.2-82.3 %) when compared to unvaccinated and relative VE of 30.8 % (95 % CI: 22.5-38.2 %) and 28.4 % (95 % CI: 10.2-42.9 %) when compared to ≥ 2 and ≥ 3 original monovalent doses, respectively. The corresponding VE estimates for adults ≥ 60 years old were 22.5 % (95 % CI: 16.8-39.8 %), 31.4 % (95 % CI: 27.7-35.0 %), and 30.6 % (95 % CI: -13.2-57.5 %). Pooled bivalent VE estimates against severe events were higher, 72.9 % (95 % CI: 60.5-82.4 %), 57.6 % (95 % CI: 42.4-68.8 %), and 62.1 % (95 % CI: 54.6-68.3 %) for all ages, and 72.0 % (95 % CI: 51.4-83.9 %), 63.4 % (95 % CI: 41.0-77.3 %), and 60.7 % (95 % CI: 52.4-67.6 %) for adults ≥ 60 years old, compared to unvaccinated, ≥2 original monovalent doses, and ≥ 3 original monovalent doses, respectively.

CONCLUSIONS

The bivalent boosters demonstrated superior protection against severe outcomes than the original monovalent boosters across age groups, highlighting the critical need for improving vaccine coverage, especially among the vulnerable older subpopulation.

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.

Deep learning framework for epidemiological forecasting: A study on COVID-19 cases and deaths in the Amazon state of Pará, Brazil

Modeling time series has been a particularly challenging aspect due to the need for constant adjustments in a rapidly changing environment, data uncertainty, dependencies between variables, volatile fluctuations, and the need to identify ideal hyperparameters. The present study presents a Framework capable of making projections from time series related to cases and deaths by COVID-19 in the Amazonian state of Pará, in Brazil. For the first time, deep learning models such as TCN, TRANSFORMER, TFT, N-BEATS, and N-HiTS were assessed for this purpose. The ARIMA statistical model was also used in post-processing for residual adjustment and short-term smoothing of the generated forecasts. The Framework generates probabilistic forecasts, with multivariate support, considering the following variables: daily cases per day of the first symptom, cases published daily, the occurrence of deaths, deaths published daily, and percentage of daily vaccination. The generated predictions are statistically evaluated by determining the best model for 7-day moving average projections using evaluating metrics such as MSE, RMSE, MAPE, sMAPE, r2, Coefficient of Variation, and residual analysis. As a result, the generated projections showed an average error of 5.4% for Cases Publication, 8.0% for Cases Symptoms, 11.12% for Deaths Publication, and 4.6% for Deaths Occurrence, with the N-HiTS and N-BEATS models obtaining better results. In general terms, the use of deep learning models to predict cases and deaths from COVID-19 has proven to be a valuable practice for analyzing the spread of the virus, which allows health managers to better understand and respond to this kind of pandemic outbreak.

Vaccination Can Prevent Severe Pulmonary Disease in COVID-19 Positive Patients: A Case-Control Study

BACKGROUND

The COVID-19 pandemic was a global health emergency, which brought lives to a standstill. To combat this deadly virus, two vaccines were deployed widely: COVISHIELD (ChAdOx1 nCoV-19) and COVAXIN (BBV152). These were approved based on the immunological response they elicit in standardized conditions; however, the real-life scenario after deployment was completely different. Only in such situations can the true effectiveness of vaccines be assessed. The primary objective was to assess the effectiveness (VE) of COVAXIN/COVISHIELD in preventing severe pulmonary disease in RT-PCR-positive COVID-19 patients greater than 18 years of age.

MATERIALS AND METHODS

A case-control study was conducted among 260 subjects aged above 18 years, positive for COVID-19 through RT-PCR. 130 cases and 130 controls were enrolled. Radiological findings were obtained and subjects with >50% lung involvement were considered as cases. Subjects were interviewed about their vaccination status. Odds ratio was calculated, and the adjusted odds ratio was estimated for vaccine effectiveness, using the formula (1-adjusted ODDS ratio)*100.

RESULTS

The vaccine effectiveness for a single dose of vaccine was 55.2% (95% C.I. 11.0%-77.5%) and with two doses was 98.0% (95% C.I. 85.0%-99.7%). Hence two doses are highly effective than a single dose of vaccine in reducing lung involvement.

CONCLUSION

Two doses of vaccine are more effective than a single dose vaccine in reducing lung involvement. Since sporadic cases of COVID-19 still persist, it is important to emphasize the role of vaccination in preventing severe COVID-19 infections, particularly in the elderly and those with comorbidities.

Early impact of COVID-19 vaccination on older populations in four countries of the Americas, 2021

Objective

To estimate the early impact of coronavirus disease 2019 (COVID-19) vaccination on cases in older populations in four countries (Chile, Colombia, Guatemala, and the United States of America), and on deaths in Chile and Guatemala.

Methods

Data were obtained from national databases of confirmed COVID-19 cases and deaths and vaccinations between 1 July 2020 and 31 August 2021. In each country, pre- and post-vaccination incidence ratios were calculated for COVID-19 cases and deaths in prioritized groups (50-59, 60-69, and ≥70 years) compared with those in the reference group (<50 years). Vaccination effect was calculated as the percentage change in incidence ratios between pre- and post-vaccination periods.

Results

The ratio of COVID-19 cases in those aged ≥50 years to those aged <50 years decreased significantly after vaccine implementation by 9.8% (95% CI: 9.5 to 10.1%) in Chile, 22.5% (95% CI: 22.0 to 23.1%) in Colombia, 20.8% (95% CI: 20.6 to 21.1%) in Guatemala, and 7.8% (95% CI: 7.6 to 7.9%) in the USA. Reductions in the ratio were highest in adults aged ≥70 years. The effect of vaccination on deaths, with time lags incorporated, was highest in the age group ≥70 years in both Chile and Guatemala: 14.4% (95% CI: 11.4 to 17.4%) and 37.3% (95% CI: 30.9 to 43.7%), respectively.

Conclusions

COVID-19 vaccination significantly reduced morbidity in the early post-vaccination period in targeted groups. In the context of a global pandemic with limited vaccine availability, prioritization strategies are important to reduce the burden of disease in high-risk age groups.

Constructing a Learning Curve to Discuss the Medical Treatments and the Effect of Vaccination of COVID-19

Acknowledging the extreme risk COVID-19 poses to humans, this paper attempted to analyze and compare case fatality rates, identify the existence of learning curves for COVID-19 medical treatments, and examine the impact of vaccination on fatality rate reduction. Confirmed cases and deaths were extracted from the "Daily Situation Report" provided by the World Health Organization. The results showed that low registration and low viral test rates resulted in low fatality rates, and the learning curve was significant for all countries except China. Treatment for COVID-19 can be improved through repeated experience. Vaccinations in the U.K. and U.S.A. are highly effective in reducing fatality rates, but not in other countries. The positive impact of vaccines may be attributed to higher vaccination rates. In addition to China, this study identified the existence of learning curves for the medical treatment of COVID-19 that can explain the effect of vaccination rates on fatalities.

Levels and Determinants of COVID-19 Vaccination at a Later Phase among Chinese Older People Aged 60 Years or Older: A Population-Based Survey

The early attainment of high COVID-19 vaccination rates can minimize avoidable hospitalizations/deaths. The fifth wave COVID-19 outbreak in Hong Kong caused >9000 deaths, and most of them were unvaccinated older people. This study hence investigated determinants of taking the first dose vaccination at a later phase (Phase 3: during the fifth wave outbreak, i.e., February-July 2022) versus two earlier phases (Phase 1: first six months since vaccine rollout, i.e., February-July 2021; Phase 2: six months prior to the outbreak, i.e., August 2021-January 2022) via a random telephone survey among 386 ever-vaccinated Hong Kong older people aged ≥60 (June/July 2022). A total of 27.7%, 51.1%, and 21.3% took the first dose at Phase 1, Phase 2, and Phase 3, respectively. Unfavorable perceptions related to COVID-19/vaccination, exposure to conflicting/counter-information about the suitability of older people's vaccination from various sources, unsupportive family influences prior to the outbreak, and depressive symptoms were significantly associated with taking the first dose at Phase 3 instead of Phase 1 and Phase 2. To speed up COVID-19 vaccination and avoid unnecessary deaths, the government and health professionals should rectify misinformation, provide clear/consistent information for older people and their family members, and focus on those having depressive symptoms at an earlier stage of the pandemic.

COVID-19 vaccine communication and advocacy strategy: a social marketing campaign for increasing COVID-19 vaccine uptake in South Korea

Research evidence suggests that communication is a powerful tool for influencing public opinion and attitudes toward various health-related issues, such as vaccine reluctance, provided it is well-designed and thoughtfully conducted. In particular, social marketing techniques that alter the target audience's behaviors for the public good can substantially improve vaccine uptake if adopted as a communication strategy in immunization programs to counter public hesitancy. This study presents evidence from the Korean government's current coronavirus disease 2019 (COVID-19) vaccination campaign, which successfully applied a social marketing approach. By the end of August 2022, South Korea had achieved high vaccine coverage, with 94.8% of the population (12+) receiving a second dose, 71.3% a third dose, and a fourth dose drive currently underway. There are five crucial factors to consider when preparing official communication for an immunization program: (i) a high degree of proactiveness, (ii) credibility, (iii) fighting misinformation, (iv) emphasizing social norms and prosocial behavior, and (v) coherence. Although using social marketing strategies may not be successful in all circumstances, the lessons learned and current implementation in Korea suggest their efficacy in fostering vaccine acceptance. This study offers valuable insights for government agencies and global public health practitioners to develop effective targeted campaign strategies that enhance the target population's vaccination intention.

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.

Predictors of SARS-CoV-2 Vaccine Uptake among Health Professionals: A Cross-Sectional Study in Ghana

COV-2 SARs has disproportionately affected low- and middle-income countries such as Ghana, where the healthcare system was not prepared enough to provide care, drugs, and equipment. This study was carried out to assess predictors of COVID-19 vaccine acceptance among health professionals in the Bono region of Ghana. A facility-based cross-sectional study was conducted among 424 health professionals recruited through simple random sampling. Univariate and multivariate logistic regression models were utilized to identify the predictors of COVID-19 vaccine acceptance presented as an odds ratio (OR) with a 95% confidence interval (CI). All respondents had heard about the COVID-19 vaccine. The most common source of information was the media (45.8%). The proportion of health professionals who accepted the COVID-19 vaccine was 73.6%. Among those who did not take the vaccine, 64.3% were willing to take it in the future. The key predictors of taking the COVID-19 vaccine included: age 25 to 45 years (AOR = 1.96, 95% CI: 1.14-3.35), age older than 45 years (AOR = 5.30, 95% CI: 2.59-10.87), males (AOR = 4.09, 95% CI: 2.34-7.15), Christians (AOR = 3.10, 95% CI: 1.44-7.72), and at least three years of experience (AOR = 1.74, 95% CI: 1.033-2.93). Reasons for not taking vaccines included: vaccines were rapidly developed and approved (41.0%), immediate side effects (39.2%), and unforeseen future effects (37.5%). This study showed that most participants had received their first dose of COVID-19 vaccination, and most of those hesitant about the vaccine were willing to receive it in the future. This is a positive finding for policy makers since it reflects that fewer resources will be needed for behavioural change initiatives. In addition, it would present a chance to focus on minority individuals who are unwilling to take the vaccine and offer targeted community mobilisation.

A generalized distributed delay model of COVID-19: An endemic model with immunity waning

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been spreading worldwide for over two years, with millions of reported cases and deaths. The deployment of mathematical modeling in the fight against COVID-19 has recorded tremendous success. However, most of these models target the epidemic phase of the disease. The development of safe and effective vaccines against SARS-CoV-2 brought hope of safe reopening of schools and businesses and return to pre-COVID normalcy, until mutant strains like the Delta and Omicron variants, which are more infectious, emerged. A few months into the pandemic, reports of the possibility of both vaccine- and infection-induced immunity waning emerged, thereby indicating that COVID-19 may be with us for longer than earlier thought. As a result, to better understand the dynamics of COVID-19, it is essential to study the disease with an endemic model. In this regard, we developed and analyzed an endemic model of COVID-19 that incorporates the waning of both vaccine- and infection-induced immunities using distributed delay equations. Our modeling framework assumes that the waning of both immunities occurs gradually over time at the population level. We derived a nonlinear ODE system from the distributed delay model and showed that the model could exhibit either a forward or backward bifurcation depending on the immunity waning rates. Having a backward bifurcation implies that $ R_c < 1 $ is not sufficient to guarantee disease eradication, and that the immunity waning rates are critical factors in eradicating COVID-19. Our numerical simulations show that vaccinating a high percentage of the population with a safe and moderately effective vaccine could help in eradicating COVID-19.

COVID-19 vaccination coverage in patients with chronic obstructive pulmonary disease - A cross-sectional study in Hungary

INTRODUCTION

Coronavirus infection is a particular risk for patients with chronic obstructive pulmonary disease (COPD), because they are much more likely to become severely ill due to oxygen supply problems. Primary prevention, including COVID-19 vaccination is of paramount importance in this disease group. The aim of our study was to assess COVID-19 vaccination coverage in COPD patients during the first vaccination campaign of the COVID-19 pandemic.

METHODS

A cross-sectional observational study (CHANCE) has been conducted in COPD patients in the eastern, western and central regions of Hungary from 15th November 2021. The anthropometric, respiratory function test results and vaccination status of 1,511 randomly selected patients were recorded who were aged 35 years and older.

RESULTS

The median age was 67 (61-72) years, for men: 67 (62-73) and for women: 66 (60-72) years, with 47.98 % men and 52.02 % women in our sample. The prevalence of vaccination coverage for the first COVID-19 vaccine dose was 88.62 %, whereas 86.57 % of the patients received the second vaccine dose. When unvaccinated (n = 172) and double vaccinated (n = 1308) patients were compared, the difference was significant both in quality of life (CAT: 17 (12-23) vs 14 (10-19); p < 0.001) and severity of dyspnea (mMRC: 2 (2-2) vs 2 (1-2); p = 0.048). The COVID-19 infection rate between double vaccinated and unvaccinated patients was 1.61 % vs 22.67 %; p < 0.001 six months after vaccination. The difference between unvaccinated and vaccinated patients was significant (8.14 % vs 0.08 %; p < 0.001) among those with acute COVID-19 infection hospitalized. In terms of post-COVID symptoms, single or double vaccinated patients had significantly fewer outpatient hospital admissions than unvaccinated patients (7.56 vs 0 %; p < 0.001).

CONCLUSION

The COVID-19 vaccination coverage was satisfactory in our sample. The uptake of COVID-19 vaccines by patients with COPD is of utmost importance because they are much more likely to develop severe complications.

Mathematical modeling of vaccination as a control measure of stress to fight COVID-19 infections

The world experienced the life-threatening COVID-19 disease worldwide since its inversion. The whole world experienced difficult moments during the COVID-19 period, whereby most individual lives were affected by the disease socially and economically. The disease caused millions of illnesses and hundreds of thousands of deaths worldwide. To fight and control the COVID-19 disease intensity, mathematical modeling was an essential tool used to determine the potentiality and seriousness of the disease. Due to the effects of the COVID-19 disease, scientists observed that vaccination was the main option to fight against the disease for the betterment of human lives and the world economy. Unvaccinated individuals are more stressed with the disease, hence their body's immune system are affected by the disease. In this study, the S V E I H R deterministic model of COVID-19 with six compartments was proposed and analyzed. Analytically, the next-generation matrix method was used to determine the basic reproduction number ( R 0 ). Detailed stability analysis of the no-disease equilibrium ( E 0 ) of the proposed model to observe the dynamics of the system was carried out and the results showed that E 0 is stable if R 0 < 1 and unstable when R 0 > 1 . The Bayesian Markov Chain Monte Carlo (MCMC) method for the parameter identifiability was discussed. Moreover, the sensitivity analysis of R 0 showed that vaccination was an essential method to control the disease. With the presence of a vaccine in our S V E I H R model, the results showed that R 0 = 0 . 208 , which means COVID-19 is fading out of the community and hence minimizes the transmission. Moreover, in the absence of a vaccine in our model, R 0 = 1 . 7214 , which means the disease is in the community and spread very fast. The numerical simulations demonstrated the importance of the proposed model because the numerical results agree with the sensitivity results of the system. The numerical simulations also focused on preventing the disease to spread in the community.

Efficacy and Safety of COVID-19 Vaccination in Older Adults: A Systematic Review and Meta-Analysis

Objective: To analyze the differences in efficacy and safety of different types of novel coronavirus pneumonia (COVID-19) vaccines in different age groups (young adults and elderly). Methods: Randomized controlled trials (RCTs) on COVID-19 vaccine in PubMed, Embase, Web of Science, and Cochrane library were searched by computer, and eight eligible studies were analyzed. Meta-analysis was performed using Stata 16.0 and RevMan5.4 software. Results: The mean geometric titer (GMT) of the virus in the elderly was significantly higher than that in the placebo group (SMD = 0.91, 95% CI (0.68, 1.15), p < 0.01), presenting no obvious difference compared with the young adults (SMD = 0.19, 95% CI (0.38, 0.01), p = 0.06). Meanwhile, the effect of multiple vaccinations was better than that of single vaccination (SMD = 0.83, 95% CI (0.33, 1.34), p < 0.01). However, the number of adverse events (AEs) in the elderly was lower than that in the young adults (OR = 0.35, 95% CI (0.29, 0.42), p < 0.01). Conclusions: The immunization effect of COVID-19 vaccine in the elderly is obvious, especially after multiple vaccinations, and the incidence of AEs in the elderly is low, which proves that the vaccination of the elderly is safe and effective.

The Deltacron conundrum: Its origin and potential health risks

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), since its outbreak in December 2019, has been capable of continuing the pandemic by mutating itself into different variants. Mass vaccinations, antibiotic treatment therapy, herd immunity, and preventive measures have reduced the disease's severity from the emerging variants. However, the virus is undergoing recombination among the current two variants: Delta and Omicron, resulting in a new variant, informally known as "Deltacron," which was controversial as it might be a product of lab contamination between Omicron and Delta samples. However, the proclamation was proved wrong, and the experts are putting more effort into better understanding the variant's epidemiological characteristics to control potential outbreaks. This review has discussed the potential mutations in the novel variant and prospective risk factors and therapeutic options in the context of this new variant. This study could be used as a guide for implementing appropriate controls in a sudden outbreak of this new variant.

Different patterns of excess all-cause mortality by age and sex in Hungary during the 2nd and 3rd waves of the COVID-19 pandemic

It is well accepted that COVID-19-related mortality shows a strong age dependency. However, temporal changes in the age distribution of excess relative mortality between waves of the pandemic are less frequently investigated. We aimed to assess excess absolute mortality and the age-distribution of all-cause mortality during the second and third waves of the COVID-19 pandemic in Hungary compared to the same periods of non-pandemic years. Rate ratios for excess all-cause mortality with 95% confidence intervals and the number of excess deaths for the second (week 41 of 2020 through week 4 of 2021) and third waves (weeks 7-21 of 2021) of the COVID pandemic for the whole of Hungary compared to the same periods of the pre-pandemic years were estimated for 10-year age strata using Poisson regression. Altogether, 9771 (95% CI: 9554-9988) excess deaths were recorded during the second wave of the pandemic, while it was lower, 8143 (95% CI: 7953-8333) during the third wave. During the second wave, relative mortality peaked for ages 65-74 and 75-84 (RR 1.37, 95%CI 1.33-1.41, RR 1.38, 95%CI 1.34-1.42). Conversely, during the third wave, relative mortality peaked for ages 35-44 (RR 1.43, 95%CI 1.33-1.55), while those ≥65 had substantially lower relative risks compared to the second wave. The reduced relative mortality among the elderly during the third wave is likely a consequence of the rapidly increasing vaccination coverage of the elderly coinciding with the third wave. The hugely increased relative mortality of those 35-44 could point to non-biological causes, such as less stringent adherence to non-pharmaceutical measures in this population.

Impact of age structure and vaccine prioritization on COVID-19 in West Africa

The ongoing COVID-19 pandemic has been a major global health challenge since its emergence in 2019. Contrary to early predictions that sub-Saharan Africa (SSA) would bear a disproportionate share of the burden of COVID-19 due to the region's vulnerability to other infectious diseases, weak healthcare systems, and socioeconomic conditions, the pandemic's effects in SSA have been very mild in comparison to other regions. Interestingly, the number of cases, hospitalizations, and disease-induced deaths in SSA remain low, despite the loose implementation of non-pharmaceutical interventions (NPIs) and the low availability and administration of vaccines. Possible explanations for this low burden include epidemiological disparities, under-reporting (due to limited testing), climatic factors, population structure, and government policy initiatives. In this study, we formulate a model framework consisting of a basic model (in which only susceptible individuals are vaccinated), a vaccine-structured model, and a hybrid vaccine-age-structured model to assess the dynamics of COVID-19 in West Africa (WA). The framework is trained with a portion of the confirmed daily COVID-19 case data for 16 West African countries, validated with the remaining portion of the data, and used to (i) assess the effect of age structure on the incidence of COVID-19 in WA, (ii) evaluate the impact of vaccination and vaccine prioritization based on age brackets on the burden of COVID-19 in the sub-region, and (iii) explore plausible reasons for the low burden of COVID-19 in WA compared to other parts of the world. Calibration of the model parameters and global sensitivity analysis show that asymptomatic youths are the primary drivers of the pandemic in WA. Also, the basic and control reproduction numbers of the hybrid vaccine-age-structured model are smaller than those of the other two models indicating that the disease burden is overestimated in the models which do not account for age-structure. This result is confirmed through the vaccine-derived herd immunity thresholds. In particular, a comprehensive analysis of the basic (vaccine-structured) model reveals that if 84%(73%) of the West African populace is fully immunized with the vaccines authorized for use in WA, vaccine-derived herd immunity can be achieved. This herd immunity threshold is lower (68%) for the hybrid model. Also, all three thresholds are lower (60% for the basic model, 51% for the vaccine-structured model, and 48% for the hybrid model) if vaccines of higher efficacies (e.g., the Pfizer or Moderna vaccine) are prioritized, and higher if vaccines of lower efficacy are prioritized. Simulations of the models show that controlling the COVID-19 pandemic in WA (by reducing transmission) requires a proactive approach, including prioritizing vaccination of more youths or vaccination of more youths and elderly simultaneously. Moreover, complementing vaccination with a higher level of mask compliance will improve the prospects of containing the pandemic. Additionally, simulations of the model predict another COVID-19 wave (with a smaller peak size compared to the Omicron wave) by mid-July 2022. Furthermore, the emergence of a more transmissible variant or easing the existing measures that are effective in reducing transmission will result in more devastating COVID-19 waves in the future. To conclude, accounting for age-structure is important in understanding why the burden of COVID-19 has been low in WA and sustaining the current vaccination level, complemented with the WHO recommended NPIs is critical in curbing the spread of the disease in WA.

From pandemic to a new normal: Strategies to optimise governmental interventions in Indonesia based on an SVEIQHR-type mathematical model

There are five different forms of intervention presently realised by the Indonesian government in an effort to end the COVID-19 pandemic: vaccinations, social restrictions, tracings, testings, and treatments. In this paper, we construct a mathematical model of type SVEIQHR (susceptible-vaccinated-exposed-infected-quarantined-hospitalised-recovered) for the disease's spread in the country, which incorporates as parameters the rates of the above interventions, as well as the vaccine's efficacy. We determine the model's equilibria and basic reproduction number. Using the model, we formulate strategies by which the interventions should be realised in order to optimise their impact. The results show that, in a disease-free state, when the number of new cases rises, the best strategy is to implement social restrictions, whereas in an endemic state, if a near-lockdown policy is undesirable, carrying out vaccinations is the best strategy; however, efforts should be aimed not primarily towards increasing the vaccination rate, but towards the use of high-efficacy vaccines.

A Bayesian network analysis quantifying risks versus benefits of the Pfizer COVID-19 vaccine in Australia

The Pfizer COVID-19 vaccine is associated with increased myocarditis incidence. Constantly evolving evidence regarding incidence and case fatality of COVID-19 and myocarditis related to infection or vaccination, creates challenges for risk-benefit analysis of vaccination. Challenges are complicated further by emerging evidence of waning vaccine effectiveness, and variable effectiveness against variants. Here, we build on previous work on the COVID-19 Risk Calculator (CoRiCal) by integrating Australian and international data to inform a Bayesian network that calculates probabilities of outcomes for the delta variant under different scenarios of Pfizer COVID-19 vaccine coverage, age groups (≥12 years), sex, community transmission intensity and vaccine effectiveness. The model estimates that in a population where 5% were unvaccinated, 5% had one dose, 60% had two doses and 30% had three doses, there was a substantially greater probability of developing (239–5847 times) and dying (1430–384,684 times) from COVID-19-related than vaccine-associated myocarditis (depending on age and sex). For one million people with this vaccine coverage, where transmission intensity was equivalent to 10% chance of infection over 2 months, 68,813 symptomatic COVID-19 cases and 981 deaths would be prevented, with 42 and 16 expected cases of vaccine-associated myocarditis in males and females, respectively. These results justify vaccination in all age groups as vaccine-associated myocarditis is generally mild in the young, and there is unequivocal evidence for reduced mortality from COVID-19 in older individuals. The model may be updated to include emerging best evidence, data pertinent to different countries or vaccines and other outcomes such as long COVID.

Path to normality: Assessing the level of social-distancing measures relaxation against antibody-resistant SARS-CoV-2 variants in a partially-vaccinated population

Introduction

Two years into the coronavirus 2019 (COVID-19) pandemic, populations with less built-up immunity continued to devise ways to optimize social distancing measures (SDMs) relaxation levels for outbreaks triggered by SARS-CoV-2 and its variants to resume minimal economics activities while avoiding hospital system collapse.

Method

An age-stratified compartmental model featuring social mixing patterns was first fitted the incidence data in second wave in Hong Kong. Hypothetical scenario analysis was conducted by varying population mobility and vaccination coverages (VCs) to predict the number of hospital and intensive-care unit admissions in outbreaks initiated by ancestral strain and its variants (Alpha, Beta, Gamma, Delta and Omicron). Scenarios were "unsustainable" if either of admissions was larger than the maximum of its occupancy.

Results

At VC of 65%, scenarios of full SDMs relaxation (mean daily social encounters prior to COVID-19 pandemic = 14.1 contacts) for outbreaks triggered by ancestral strain, Alpha and Beta were sustainable. Restricting levels of SDMs was required such that the optimal population mobility had to be reduced to 0.9, 0.65 and 0.37 for Gamma, Delta and Omicron associated outbreaks respectively. VC improvement from 65% to 75% and 95% allowed complete SDMs relaxation in Gamma-, and Delta-driven epidemic respectively. However, this was not supported for Omicron-triggered epidemic.

Discussion

To seek a path to normality, speedy vaccine and booster distribution to the majority across all age groups is the first step. Gradual or complete SDMs lift could be considered if the hybrid immunity could be achieved due to high vaccination coverage and natural infection rate among vaccinated or the COVID-19 case fatality rate could be reduced similar to that for seasonal influenza to secure hospital system sustainability.

Immunogenicity, safety, and antiphospholipid antibodies after SARS-CoV-2 vaccine in patients with primary antiphospholipid syndrome

Objective

Coronavirus disease 19 (COVID-19) has an increased risk of coagulopathy with high frequency of antiphospholipid antibodies (aPL). Recent reports of thrombosis associated with adenovirus-based vaccines raised concern that SARS-CoV-2 immunization in primary antiphospholipid syndrome (PAPS) patients may trigger clotting complications. Our objectives were to assess immunogenicity, safety, and aPL production in PAPS patients, after vaccinating with Sinovac-CoronaVac, an inactivated virus vaccine against COVID-19.

Methods

This prospective controlled phase-4 study of PAPS patients and a control group (CG) consisted of a two-dose Sinovac-CoronaVac (D0/D28) and blood collection before vaccination (D0), at D28 and 6 weeks after second dose (D69) for immunogenicity/aPL levels. Outcomes were seroconversion (SC) rates of anti-SARS-CoV-2 S1/S2 IgG and/or neutralizing antibodies (NAb) at D28/D69 in naïve participants. Safety and aPL production were also assessed.

Results

We included 44 PAPS patients (31 naïve) and 132 CG (108 naïve) with comparable age (p=0.982) and sex (p>0.999). At D69, both groups had high and comparable SC (83.9% vs. 93.5%, p=0.092), as well as NAb positivity (77.4% vs. 78.7%, p=0.440), and NAb-activity (64.3% vs. 60.9%, p=0.689). Thrombotic events up to 6 months or other moderate/severe side effects were not observed. PAPS patients remained with stable aPL levels throughout the study at D0 vs. D28 vs. D69: anticardiolipin (aCL) IgG (p=0.058) and IgM (p=0.091); anti-beta-2 glycoprotein I (aβ2GPI) IgG (p=0.513) and IgM (p=0.468).

Conclusion

We provided novel evidence that Sinovac-CoronaVac has high immunogenicity and safety profile in PAPS. Furthermore, Sinovac-CoronaVac did not trigger thrombosis nor induced changes in aPL production.

COVID-19 Vaccine Uptake in the Context of the First Delta Outbreak in China During the Early Summer of 2021: The Role of Geographical Distance and Vaccine Talk

Purpose

Vaccination is essential to control the prevalence of COVID-19. However, vaccine hesitancy has been a major issue globally. Some studies have suggested that community outbreaks might boost vaccine uptake. Consistent with that idea, vaccination rates increased dramatically during the first outbreak of the COVID-19 Delta variant in Guangdong, China, in June 2021. Based on the risk perception attitude theory, this study attempted to explore the joint effect of geographical distance to the outbreak and the frequency of talking about the COVID-19 vaccine (vaccine talk) on people’s COVID-19 vaccine uptake.

Methods

An anonymous self-report online questionnaire was completed by citizens living in Guangdong Province, China, from June 6 to 11, 2021, during the Delta variant outbreak in that region. The relationship between COVID-19 vaccine uptake, geographical distance to the epicenter of the outbreak, and vaccine talk was analyzed using logistic regression analysis.

Results

Data from 350 respondents were included in the final analysis. Results showed a negative association between geographical distance and COVID-19 vaccine uptake. Furthermore, the relationship was moderated by vaccine talk. Specifically, when individuals infrequently discussed vaccine talk with others, close distance to the epicenter of the outbreak served as a motivator for getting vaccinated, whereas for people who frequently discussed the vaccine, geographical distance might have played less of a role in motivating them to get vaccinated.

Conclusion

This research highlights the joint effect of geographical distance to the outbreak of COVID-19 and vaccine talk in COVID-19 vaccine uptake. While the findings may only be a starting point for launching a public health awareness campaign, encouraging people to engage in more conversations about vaccines may be a promising solution for future health emergencies, especially among people far from the outbreak.

Mathematical modeling of COVID-19 in British Columbia: An age-structured model with time-dependent contact rates

Following the emergence of COVID-19 at the end of 2019, several mathematical models have been developed to study the transmission dynamics of this disease. Many of these models assume homogeneous mixing in the underlying population. However, contact rates and mixing patterns can vary dramatically among individuals depending on their age and activity level. Variation in contact rates among age groups and over time can significantly impact how well a model captures observed trends. To properly model the age-dependent dynamics of COVID-19 and understand the impacts of interventions, it is essential to consider heterogeneity arising from contact rates and mixing patterns. We developed an age-structured model that incorporates time-varying contact rates and population mixing computed from the ongoing BC Mix COVID-19 survey to study transmission dynamics of COVID-19 in British Columbia (BC), Canada. Using a Bayesian inference framework, we fit four versions of our model to weekly reported cases of COVID-19 in BC, with each version allowing different assumptions of contact rates. We show that in addition to incorporating age-specific contact rates and mixing patterns, time-dependent (weekly) contact rates are needed to adequately capture the observed transmission dynamics of COVID-19. Our approach provides a framework for explicitly including empirical contact rates in a transmission model, which removes the need to otherwise model the impact of many non-pharmaceutical interventions. Further, this approach allows projection of future cases based on clear assumptions of age-specific contact rates, as opposed to less tractable assumptions regarding transmission rates.

Mortality rates among COVID-19 patients hospitalised during the first three waves of the epidemic in Milan, Italy: A prospective observational study

Introduction

This paper describes how mortality among hospitalised COVID-19 patients changed during the first three waves of the epidemic in Italy.

Methods

This prospective cohort study used the Kaplan-Meier method to analyse the time-dependent probability of death of all of the patients admitted to a COVID-19 referral centre in Milan, Italy, during the three consecutive periods of: 21 February-31 July 2020 (first wave, W1), 1 August 2020–31 January 2021 (second wave, W2), and 1 February-30 April 2021 (third wave, W3). Cox models were used to examine the association between death and the period of admission after adjusting for age, biological sex, the time from symptom onset to admission, disease severity upon admission, obesity, and the comorbidity burden.

Results

Of the 2,023 COVID-19 patients admitted to our hospital during the study period, 553 (27.3%) were admitted during W1, 838 (41.5%) during W2, and 632 (31.2%) during W3. The crude mortality rate during W1, W2 and W3 was respectively 21.3%, 23.7% and 15.8%. After adjusting for potential confounders, hospitalisation during W2 or W3 was independently associated with a significantly lower risk of death than hospitalisation during W1 (adjusted hazard ratios [AHRs]: 0.75, 95% confidence interval [CI] 0.59–0.95, and 0.58, 95% CI 0.44–0.77). Among the patients aged >75 years, there was no significant difference in the probability of death during the three waves (AHRs during W2 and W3 vs W1: 0.93, 95% CI 0.65–1.33, and 0.88, 95% CI 0.59–1.32), whereas those presenting with critical disease during W2 and W3 were at significantly lower risk of dying than those admitted during W1 (AHRs 0.61, 95% CI 0.43–0.88, and 0.44, 95% CI 0.28–0.70).

Conclusions

Hospitalisation during W2 and W3 was associated with a reduced risk of COVID-19 death in comparison with W1, but there was no difference in survival probability in patients aged >75 years.

Risk assessment of COVID-19 epidemic resurgence in relation to SARS-CoV-2 variants and vaccination passes

The introduction of COVID-19 vaccination passes (VPs) by many countries coincided with the Delta variant fast becoming dominant across Europe. A thorough assessment of their impact on epidemic dynamics is still lacking. Here, we propose the VAP-SIRS model that considers possibly lower restrictions for the VP holders than for the rest of the population, imperfect vaccination effectiveness against infection, rates of (re-)vaccination and waning immunity, fraction of never-vaccinated, and the increased transmissibility of the Delta variant. Some predicted epidemic scenarios for realistic parameter values yield new COVID-19 infection waves within two years, and high daily case numbers in the endemic state, even without introducing VPs and granting more freedom to their holders. Still, suitable adaptive policies can avoid unfavorable outcomes. While VP holders could initially be allowed more freedom, the lack of full vaccine effectiveness and increased transmissibility will require accelerated (re-)vaccination, wide-spread immunity surveillance, and/or minimal long-term common restrictions.

Assessing the impact of vaccines, other public health measures, and declining immunity on SARS-CoV-2 control is challenging. This is particularly true in the context of vaccination passes, whereby vaccinated individuals have more freedom of making contacts than unvaccinated ones. Here, we use a mathematical model to simulate various scenarios and investigate the likelihood of containing COVID-19 outbreaks in example European countries. We demonstrate that both Alpha and Delta SARS-CoV-2 variants inevitably lead to recurring outbreaks when measures are lifted for vaccination pass holders. High re-vaccination rates and a lowered fraction of the unvaccinated population increase the benefit of vaccination passes. These observations are important for policy making, highlighting the need for continued vigilance, even where the epidemic is under control, especially when new variants of concern emerge.

Krueger, Gogolewski, and Bodych et al. assess the risk of COVID-19 epidemic resurgence in relation to SARS-CoV-2 variants and vaccination passes. Their model predicts that new COVID-19 infection waves within two years from the onset of the vaccination program are possible but that suitable adaptive policies can help to avoid unfavorable outcomes.

Successive Pandemic Waves with Different Virulent Strains and the Effects of Vaccination for SARS-CoV-2

One hundred years after the flu pandemic of 1918, the world faces an outbreak of a new severe acute respiratory syndrome, caused by a novel coronavirus. With a high transmissibility, the pandemic has spread worldwide, creating a scenario of devastation in many countries. By the middle of 2021, about 3% of the world population had been infected and more than 4 million people had died. Different from the H1N1 pandemic, which had a deadly wave and ceased, the new disease is maintained by successive waves, mainly produced by new virus variants and the small number of vaccinated people. In the present work, we create a version of the SIR model using the spatial localization of persons, their movements, and considering social isolation probabilities. We discuss the effects of virus variants, and the role of vaccination rate in the pandemic dynamics. We show that, unless a global vaccination is implemented, we will have continuous waves of infections.

Death after the Administration of COVID-19 Vaccines Approved by EMA: Has a Causal Relationship Been Demonstrated?

More than eight billion doses of COVID-19 vaccines have been administered globally so far and 44.29% of people are fully vaccinated. Pre-authorization clinical trials were carried out and the safety of vaccines is still continuously monitored through post-commercialization surveillance. However, some people are afraid of vaccine side effects, claiming they could lead to death, and hesitate to get vaccinated. Herein, a literature review of COVID-19-vaccine-related deaths has been carried out according to the PRISMA standards to understand if there is a causal relationship between vaccination and death and to highlight the real extent of such events. There have been 55 cases of death after COVID-19 vaccination reported and a causal relationship has been excluded in 17 cases. In the remaining cases, the causal link between the vaccine and the death was not specified (8) or considered possible (15), probable (1), or very probable/demonstrated (14). The causes of deaths among these cases were: vaccine-induced immune thrombotic thrombocytopenia (VITT) (32), myocarditis (3), ADEM (1), myocardial infarction (1), and rhabdomyolysis (1). In such cases, the demonstration of a causal relationship is not obvious, and more studies, especially with post-mortem investigations, are needed to deepen understanding of the possible pathophysiological mechanisms of fatal vaccine side effects. In any event, given the scarcity of fatal cases, the benefits of vaccination outweigh the risks and the scientific community needs to be cohesive in asserting that vaccination is fundamental to containing the spread of SARS-CoV-2.

Assessing the potential impact of immunity waning on the dynamics of COVID-19 in South Africa: an endemic model of COVID-19

We developed an endemic model of COVID-19 to assess the impact of vaccination and immunity waning on the dynamics of the disease. Our model exhibits the phenomenon of backward bifurcation and bi-stability, where a stable disease-free equilibrium coexists with a stable endemic equilibrium. The epidemiological implication of this is that the control reproduction number being less than unity is no longer sufficient to guarantee disease eradication. We showed that this phenomenon could be eliminated by either increasing the vaccine efficacy or by reducing the disease transmission rate (adhering to non-pharmaceutical interventions). Furthermore, we numerically investigated the impacts of vaccination and waning of both vaccine-induced immunity and post-recovery immunity on the disease dynamics. Our simulation results show that the waning of vaccine-induced immunity has more effect on the disease dynamics relative to post-recovery immunity waning and suggests that more emphasis should be on reducing the waning of vaccine-induced immunity to eradicate COVID-19.

Coronavirus disease 2019 (Covid-19) vaccination recommendations in special populations and patients with existing comorbidities

Vaccination against severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is a crucial step in ending the current worldwide pandemic. However, several particularly vulnerable groups in the population were not included in sufficient numbers in coronavirus disease 2019 (Covid‐19) vaccine trials. Therefore, as science advances, the advice for vaccinating these special populations against Covid‐19 will continue to evolve. This focused review provides the latest recommendations and considerations for these special populations (i.e., patients with rheumatologic and autoimmune disorders, cancer, transplant recipients, chronic liver diseases, end‐stage renal disease, neurologic disorders, psychiatric disorders, diabetes mellitus, obesity, cardiovascular diseases, chronic obstructive pulmonary disease, human immunodeficiency virus, current smokers, pregnant and breastfeeding women, the elderly, children, and patients with allergic reactions) using the currently available research evidence.

COVID-19 Vaccination Attitudes With Neuromyelitis Optica Spectrum Disorders: Vaccine Hesitancy and Coping Style

Background: Vaccination is an important method by which to stop the spread of coronavirus disease 2019 (COVID-19) in a population. Patients with neuromyelitis optica spectrum disorders (NMOSD) have unstable immune function and receive immunosuppressive therapy frequently, so they are hardly to make a decision to receive vaccination. Our study investigated the vaccine hesitancy and coping styles in patients with NMOSD to analyze the relationship between vaccine hesitancy and coping styles, and elucidate the factors influencing vaccine hesitancy. Methods: A convenient sampling method was used to recruit participants. The Adult Vaccine Hesitancy Scale and Medical Coping Modes Questionnaire were used to measure the vaccine hesitancy and coping style of the participants. Pearson correlation, multiple stepwise, linear regression, and one-way analysis of variance were used to analyze the data. Results: A total of 262 NMOSD patients were investigated. The score of vaccine hesitancy in NMOSD patients is lower (21.13 ± 4.355) than 25 points which indicated the patient is not considered to have vaccine hesitancy. The score for vaccine hesitancy was negatively correlated with the confrontation and avoidance coping styles (r = -0.481 and r = 0.423). That adoption of the coping styles of confrontation and avoidance as well as the residence of the patient were predictors of vaccine hesitation in NMOSD patients (R ²(adj) = 0.29, P < 0.001). In addition, the vaccine hesitancy scores of NMOSD patients residing in rural areas were significantly higher than those of patients living in urban areas (P < 0.01). Comparing with each level of education, the scores were not statistically significant in vaccine hesitancy and coping styles (P > 0.05). Conclusions: This study reveals that the NMOSD patients is not considered to have vaccine hesitancy, Patients who tend to adopt confrontation and avoidance coping styles have less vaccine hesitancy. Health authorities and medical specialist teams should strengthen effective vaccination information for patients with NMOSD, such as expert consensus or guidelines through various media to help them with decision-making. The significance of vaccination, the safety and side effects of COVID-19 vaccination and predicting of epidemiological trends of COVID-19 should be emphasized. More attention should be paid to NMOSD patients who living in rural areas.

Interpreting estimates of coronavirus disease 2019 (COVID-19) vaccine efficacy and effectiveness to inform simulation studies of vaccine impact: a systematic review

Background: The multiple efficacious vaccines authorised for emergency use worldwide represent the first preventative intervention against coronavirus disease 2019 (COVID-19) that does not rely on social distancing measures. The speed at which data are emerging and the heterogeneities in study design, target populations, and implementation make it challenging to interpret and assess the likely impact of vaccine campaigns on local epidemics. We reviewed available vaccine efficacy and effectiveness studies to generate working estimates that can be used to parameterise simulation studies of vaccine impact. Methods: We searched MEDLINE, the World Health Organization’s Institutional Repository for Information Sharing, medRxiv, and vaccine manufacturer websites for studies that evaluated the emerging data on COVID-19 vaccine efficacy and effectiveness. Studies providing an estimate of the efficacy or effectiveness of a COVID-19 vaccine using disaggregated data against SARS-CoV-2 infection, symptomatic disease, severe disease, death, or transmission were included. We extracted information on study population, variants of concern (VOC), vaccine platform, dose schedule, study endpoints, and measures of impact. We applied an evidence synthesis approach to capture a range of plausible and consistent parameters for vaccine efficacy and effectiveness that can be used to inform and explore a variety of vaccination strategies as the COVID-19 pandemic evolves. Results: Of the 602 articles and reports identified, 53 were included in the analysis. The availability of vaccine efficacy and effectiveness estimates varied by vaccine and were limited for VOCs. Estimates for non-primary endpoints such as effectiveness against infection and onward transmission were sparse. Synthesised estimates were relatively consistent for the same vaccine platform for wild-type, but was more variable for VOCs. Conclusions: Assessment of efficacy and effectiveness of COVID-19 vaccines is complex. Simulation studies must acknowledge and capture the uncertainty in vaccine effectiveness to robustly explore and inform vaccination policies and policy around the lifting of non-pharmaceutical interventions.

An engineering model of the COVID-19 trajectory to predict the success of isolation initiatives

Much media and societal attention is today focused on how to best control the spread of coronavirus (COVID-19). Every day brings us new data, and policy makers are implementing different strategies in different countries to manage the impact of COVID-19. To respond to the first 'wave' of infection, several countries, including the UK, opted for isolation/lockdown initiatives, with different degrees of rigour. Data showed that these initiatives have yielded the expected results in terms of containing the rapid trajectory of the virus. When this article was first prepared (April 2020), the affected societies were wondering when the isolation/lockdown initiatives should be lifted. While detailed epidemiological, economic as well as social studies would be required to answer this question completely, here we employ a simple engineering model. Albeit simple, the model is capable of reproducing the main features of the data reported in the literature concerning the COVID-19 trajectory in different countries, including the increase in cases in countries following the initially successful isolation/lockdown initiatives. Keeping in mind the simplicity of the model, we attempt to draw some conclusions, which seem to suggest that a decrease in the number of infected individuals after the initiation of isolation/lockdown initiatives does not necessarily guarantee that the virus trajectory is under control. Within the limit of this model, it would seem that rigid isolation/lockdown initiatives for the medium term would lead to achieving the desired control over the spread of the virus. This observation seems consistent with the 2020 summer months, during which the COVID-19 trajectory seemed to be almost under control across most European countries. Consistent with the results from our simple model, winter 2020 data show that the virus trajectory was again on the rise. Because the optimal solution will achieve control over the spread of the virus while minimising negative societal impacts due to isolation/lockdown, which include but are not limited to economic and mental health aspects, the engineering model presented here is not sufficient to provide the desired answer. However, the model seems to suggest that to keep the COVID-19 trajectory under control, a series of short-to-medium term isolation measures should be put in place until one or more of the following scenarios is achieved: a cure has been developed and has become accessible to the population at large; a vaccine has been developed, tested and distributed to large portions of the population; a sufficiently large portion of the population has developed resistance to the COVID-19 virus; or the virus itself has become less aggressive. It is somewhat remarkable that an engineering model, despite all its approximations, provides suggestions consistent with advanced epidemiological models developed by several experts in the field. The model proposed here is however not expected to be able to capture the emergence of variants of the virus, which seem to be responsible for significant outbreaks, notably in India, in the spring of 2021, it cannot describe the effectiveness of vaccine strategies, as it does not differentiate among different age groups within the population, nor does it allow us to consider the duration of the immunity achieved after infection or vaccination.