Waning immunity to SARS-CoV-2 following vaccination or infection

Simulation-based validation of a method to detect changes in SARS-CoV-2 reinfection risk

Given the high global seroprevalence of SARS-CoV-2, understanding the risk of reinfection has become increasingly important. Models developed to track trends in reinfection risk should be robust against possible biases arising from imperfect data observation processes. We performed simulation-based validation of an existing catalytic model designed to detect changes in the risk of reinfection by SARS-CoV-2. The catalytic model assumes the risk of reinfection is proportional to observed infections. Validation involved using simulated primary infections, consistent with the number of observed infections in South Africa. To assess the performance of the catalytic model, we simulated reinfection datasets that incorporated different processes that may bias inference, including imperfect observation and mortality. A Bayesian approach was used to fit the model to simulated data, assuming a negative binomial distribution around the expected number of reinfections, and model projections were compared to the simulated data using different magnitudes of change in reinfection risk. We assessed the model's ability to accurately detect changes in reinfection risk when included in the simulations, as well as the occurrence of false positives when reinfection risk remained constant. The model parameters converged in most scenarios leading to model outputs aligning with anticipated outcomes. The model successfully detected changes in the risk of reinfection when such a change was introduced to the data. Low observation probabilities (10%) of both primary- and reinfections resulted in low numbers of observed cases from the simulated data and poor convergence. The model's performance was assessed on simulated data representative of the South African SARS-CoV-2 epidemic, reflecting its timing of waves and outbreak magnitude. Model performance under similar scenarios may be different in settings with smaller epidemics (and therefore smaller numbers of reinfections). Ensuring model parameter convergence is essential to avoid false-positive detection of shifts in reinfection risk. While the model is robust in most scenarios of imperfect observation and mortality, further simulation-based validation for regions experiencing smaller outbreaks is recommended. Caution must be exercised in directly extrapolating results across different epidemiological contexts without additional validation efforts.

Rethinking Optimal Immunogens to Face SARS-CoV-2 Evolution Through Vaccination

SARS-CoV-2, which originated in China in late 2019, quickly fueled the global COVID-19 pandemic, profoundly impacting health and the economy worldwide. A series of vaccines, mostly based on the full SARS-CoV-2 Spike protein, were rapidly developed, showing excellent humoral and cellular responses and high efficacy against both symptomatic infection and severe disease. However, viral evolution and the waning humoral neutralizing responses strongly challenged vaccine long term effectiveness, mainly against symptomatic infection, making necessary a strategy of repeated and updated booster shots. In this repeated vaccination context, antibody repertoire diversification was evidenced, although immune imprinting after booster doses or reinfection was also demonstrated and identified as a major determinant of immunological responses to repeated antigen exposures. Considering that a small domain of the SARS-CoV-2 Spike protein, the receptor binding domain (RBD), is the major target of neutralizing antibodies and concentrates most viral mutations, the following text aims to provide insights into the ongoing debate over the best strategies for vaccine boosters. We address the relevance of developing new booster vaccines that target the evolving RBD, thus focusing on the relevant antigenic sites of the SARS-CoV-2 new variants. A combination of this strategy with immunofusing and computerized approaches could minimize immune imprinting, therefore optimizing neutralizing immune responses and booster vaccine efficacy.

A conceptual health state diagram for modelling the transmission of a (re)emerging infectious respiratory disease in a human population

Mathematical modelling of (re)emerging infectious respiratory diseases among humans poses multiple challenges for modellers, which can arise as a result of limited data and surveillance, uncertainty in the natural history of the disease, as well as public health and individual responses to outbreaks. Here, we propose a COVID-19-inspired health state diagram (HSD) to serve as a foundational framework for conceptualising the modelling process for (re)emerging respiratory diseases, and public health responses, in the early stages of their emergence. The HSD aims to serve as a starting point for reflection on the structure and parameterisation of a transmission model to assess the impact of the (re)emerging disease and the capacity of public health interventions to control transmission. We also explore the adaptability of the HSD to different (re)emerging diseases using the characteristics of three respiratory diseases of historical public health importance. We outline key questions to contemplate when applying and adapting this HSD to (re)emerging infectious diseases and provide reflections on adapting the framework for public health-related interventions.

SARS-CoV-2 Vaccines: The Advantage of Mucosal Vaccine Delivery and Local Immunity

Immunity against respiratory pathogens is often short-term, and, consequently, there is an unmet need for the effective prevention of such infections. One such infectious disease is coronavirus disease 19 (COVID-19), which is caused by the novel Beta coronavirus SARS-CoV-2 that emerged around the end of 2019. The World Health Organization declared the illness a pandemic on 11 March 2020, and since then it has killed or sickened millions of people globally. The development of COVID-19 systemic vaccines, which impressively led to a significant reduction in disease severity, hospitalization, and mortality, contained the pandemic's expansion. However, these vaccines have not been able to stop the virus from spreading because of the restricted development of mucosal immunity. As a result, breakthrough infections have frequently occurred, and new strains of the virus have been emerging. Furthermore, SARS-CoV-2 will likely continue to circulate and, like the influenza virus, co-exist with humans. The upper respiratory tract and nasal cavity are the primary sites of SARS-CoV-2 infection and, thus, a mucosal/nasal vaccination to induce a mucosal response and stop the virus' transmission is warranted. In this review, we present the status of the systemic vaccines, both the approved mucosal vaccines and those under evaluation in clinical trials. Furthermore, we present our approach of a B-cell peptide-based vaccination applied by a prime-boost schedule to elicit both systemic and mucosal immunity.

Evolution of serious and life-threatening COVID-19 pneumonia as the SARS-CoV-2 pandemic progressed: an observational study of mortality to 60 days after admission to a 15-hospital US health system

OBJECTIVE

In order to predict at hospital admission the prognosis of patients with serious and life-threatening COVID-19 pneumonia, we sought to understand the clinical characteristics of hospitalised patients at admission as the SARS-CoV-2 pandemic progressed, document their changing response to the virus and its variants over time, and identify factors most importantly associated with mortality after hospital admission.

DESIGN

Observational study using a prospective hospital systemwide COVID-19 database.

SETTING

15-hospital US health system.

PARTICIPANTS

26 872 patients admitted with COVID-19 to our Northeast Ohio and Florida hospitals from 1 March 2020 to 1 June 2022.

MAIN OUTCOME MEASURES

60-day mortality (highest risk period) after hospital admission analysed by random survival forests machine learning using demographics, medical history, and COVID-19 vaccination status, and viral variant, symptoms, and routine laboratory test results obtained at hospital admission.

RESULTS

Hospital mortality fell from 11% in March 2020 to 3.7% in March 2022, a 66% decrease (p<0.0001); 60-day mortality fell from 17% in May 2020 to 4.7% in May 2022, a 72% decrease (p<0.0001). Advanced age was the strongest predictor of 60-day mortality, followed by admission laboratory test results. Risk-adjusted 60-day mortality had all patients been admitted in March 2020 was 15% (CI 3.0% to 28%), and had they all been admitted in May 2022, 12% (CI 2.2% to 23%), a 20% decrease (p<0.0001). Dissociation between observed and predicted decrease in mortality was related to temporal change in admission patient profile, particularly in laboratory test results, but not vaccination status or viral variant.

CONCLUSIONS

Hospital mortality from COVID-19 decreased substantially as the pandemic evolved but persisted after hospital discharge, eclipsing hospital mortality by 50% or more. However, after accounting for the many, even subtle, changes across the pandemic in patients' demographics, medical history and particularly admission laboratory results, a patient admitted early in the pandemic and predicted to be at high risk would remain at high risk of mortality if admitted tomorrow.

Factors Associated with SARS-CoV-2 Infection in Fully Vaccinated Nursing Home Residents and Workers

Persons living or working in nursing homes faced a higher risk of SARS-CoV-2 infections during the pandemic, resulting in heightened morbidity and mortality among older adults despite robust vaccination efforts. This prospective study evaluated the humoral and cellular immunity in fully vaccinated residents and workers from two nursing homes in Madrid, Spain, from 2020 to 2021. Measurements of IgG levels were conducted in August 2020 (pre-vaccination) and June and September 2021 (post-vaccination), alongside assessments of neutralizing antibodies and cellular responses in September 2021 among the most vulnerable individuals. Follow-up extended until February 2022 to identify risk factors for SARS-CoV-2 infection or mortality, involving 267 residents (mean age 87.6 years, 81.3% women) and 302 workers (mean age 50.7 years, 82.1% women). Residents exhibited a significantly higher likelihood of experiencing COVID-19 before June 2021 compared with nursing staff (OR [95% CI], 7.2 [3.0 to 17.2], p < 0.01). Participants with a history of previous COVID-19 infection showed more significant increases in IgG levels in August 2020, June 2021 and September 2021, alongside an increased proportion of neutralizing antibodies in the most vulnerable individuals. However, IgG decay remained the same between June and September 2021 based on the previous COVID-19 status. During the Omicron variant wave, residents and staff showed a similar rate of SARS-CoV-2 infection. Notably, preceding clinical or immunological factors before receiving three vaccination doses did not demonstrate associations with COVID-19 infection or overall mortality in our participant cohort.

Predicting COVID-19 pandemic waves including vaccination data with deep learning

Introduction

During the recent COVID-19 pandemics, many models were developed to predict the number of new infections. After almost a year, models had also the challenge to include information about the waning effect of vaccines and by infection, and also how this effect start to disappear.

Methods

We present a deep learning-based approach to predict the number of daily COVID-19 cases in 30 countries, considering the non-pharmaceutical interventions (NPIs) applied in those countries and including vaccination data of the most used vaccines.

Results

We empirically validate the proposed approach for 4 months between January and April 2021, once vaccination was available and applied to the population and the COVID-19 variants were closer to the one considered for developing the vaccines. With the predictions of new cases, we can prescribe NPIs plans that present the best trade-off between the expected number of COVID-19 cases and the social and economic cost of applying such interventions.

Discussion

Whereas, mathematical models which include the effect of vaccines in the spread of the SARS-COV-2 pandemic are available, to the best of our knowledge we are the first to propose a data driven method based on recurrent neural networks that considers the waning effect of the immunization acquired either by vaccine administration or by recovering from the illness. This work contributes with an accurate, scalable, data-driven approach to modeling the pandemic curves of cases when vaccination data is available.

Pseudotyping Improves the Yield of Functional SARS-CoV-2 Virus-like Particles (VLPs) as Tools for Vaccine and Therapeutic Development

Virus-like particles (VLPs) have been proposed as an attractive tool in SARS-CoV-2 vaccine development, both as (1) a vaccine candidate with high immunogenicity and low reactogenicity and (2) a substitute for live virus in functional and neutralization assays. Though multiple SARS-CoV-2 VLP designs have already been explored in Sf9 insect cells, a key parameter ensuring VLPs are a viable platform is the VLP spike yield (i.e., spike protein content in VLP), which has largely been unreported. In this study, we show that the common strategy of producing SARS-CoV-2 VLPs by expressing spike protein in combination with the native coronavirus membrane and/or envelope protein forms VLPs, but at a critically low spike yield (~0.04-0.08 mg/L). In contrast, fusing the spike ectodomain to the influenza HA transmembrane domain and cytoplasmic tail and co-expressing M1 increased VLP spike yield to ~0.4 mg/L. More importantly, this increased yield translated to a greater VLP spike antigen density (~96 spike monomers/VLP) that more closely resembles that of native SARS-CoV-2 virus (~72-144 Spike monomers/virion). Pseudotyping further allowed for production of functional alpha (B.1.1.7), beta (B.1.351), delta (B.1.617.2), and omicron (B.1.1.529) SARS-CoV-2 VLPs that bound to the target ACE2 receptor. Finally, we demonstrated the utility of pseudotyped VLPs to test neutralizing antibody activity using a simple, acellular ELISA-based assay performed at biosafety level 1 (BSL-1). Taken together, this study highlights the advantage of pseudotyping over native SARS-CoV-2 VLP designs in achieving higher VLP spike yield and demonstrates the usefulness of pseudotyped VLPs as a surrogate for live virus in vaccine and therapeutic development against SARS-CoV-2 variants.

Association between Vaccination Status for COVID-19 and the Risk of Severe Symptoms during the Endemic Phase of the Disease

The global health emergency caused by COVID-19 concluded in May 2023, marking the beginning of an endemic phase. This study aimed to evaluate the association between vaccination status and other patient characteristics and the risk of severe disease during this new endemic period. A nationwide cohort study was conducted in Mexico, where we analyzed data from 646 adults who had received positive confirmation of COVID-19 through PCR testing from May to August 2023. The overall risk of severe symptoms in the study sample was 5.3%. The average time elapsed from the last vaccine shot to symptom onset was over six months in all the immunized groups (1, 2 or 3 vaccine doses). Compared to unvaccinated patients, those with three vaccine doses showed an elevated risk of severe symptoms. Advancing age and various chronic comorbidities (specifically cardiovascular, kidney, and obstructive pulmonary conditions) were associated with a heightened risk of severe COVID-19 manifestations. These findings underscore the ongoing seriousness of COVID-19, even in an endemic phase, underscoring the urgent need for tailored interventions aimed at high-risk patients.

Survival Analysis and Contributing Factors among PCR-Confirmed Adult Inpatients during the Endemic Phase of COVID-19

In May 2023, the global health emergency status of COVID-19 concluded, marking the onset of an endemic era. This study assessed survival rates among PCR-confirmed adult inpatients during this phase and determined contributing factors. Employing a survival analysis approach, this investigation utilized a nationwide Mexican cohort encompassing 152 adult inpatients. Survival rates were computed using the Kaplan-Meier method, and a proportional Cox model identified mortality risk factors. Survival rates remained above 65% on day 14 after admission. Vaccination status, including the number of doses administered, was not significantly associated with fatal outcomes. Chronic kidney disease or a history of immunosuppression (due to any cause) increased mortality risk. Our findings underscore the persistent severity of COVID-19 beyond the global health emergency, emphasizing the necessity for tailored interventions for vulnerable patients.

Assessing the Influence of COVID-19 Vaccination Coverage on Excess Mortality across 178 Countries: A Cross-Sectional Study

The COVID-19 pandemic has had a devastating impact on global health, necessitating urgent and effective strategies to mitigate its consequences. Vaccination programs have been implemented worldwide to combat virus transmission and reduce the disease burden. This study aimed to investigate the relationship between COVID-19 vaccination coverage and all-cause excess mortality in 178 nations during the first two years of the pandemic. Multiple regression analysis, after adjusting for life expectancy at birth, confirmed a significant association between higher vaccination coverage and lower all-cause mortality rates (β = -106.8, 95% CI -175.4 to -38.2, p = 0.002). These findings underscore the importance of vaccination campaigns in reducing overall mortality during the COVID-19 pandemic. Evidence-based decision making and resource allocation can benefit from this information, facilitating the optimization of vaccination strategies for maximal impact on mortality reduction. Further research and continuous monitoring are crucial to understanding the long-term effects of vaccination coverage on population health in the ongoing pandemic.

The effect of shortening the quarantine period and lifting the indoor mask mandate on the spread of COVID-19: a mathematical modeling approach

In this paper, we present a mathematical model to assess the impact of reducing the quarantine period and lifting the indoor mask mandate on the spread of Coronavirus Disease 2019 (COVID-19) in Korea. The model incorporates important epidemiological parameters, such as transmission rates and mortality rates, to simulate the transmission of the virus under different scenarios. Our findings reveal that the impact of mask wearing fades in the long term, which highlights the crucial role of quarantine in controlling the spread of the disease. In addition, balancing the confirmed cases and costs, the lifting of mandatory indoor mask wearing is cost-effective; however, maintaining the quarantine period remains essential. A relationship between the disease transmission rate and vaccine efficiency was also apparent, with higher transmission rates leading to a greater impact of the vaccine efficiency. Moreover, our findings indicate that a higher disease transmission rate exacerbates the consequences of early quarantine release.

Predictors of ICU Admission in Children with COVID-19: Analysis of a Large Mexican Population Dataset

Children, although mostly affected mildly or asymptomatically, have also developed severe coronavirus disease 2019 (COVID-19). This study aims to assess potential predictors of intensive care unit (ICU) admission in a large population (n = 21,121) of children aged 0-9 years with laboratory-confirmed disease. We performed a cross-sectional analysis of a publicly available dataset derived from the normative epidemiological surveillance of COVID-19 in Mexico. The primary binary outcome of interest was admission to the ICU due to respiratory failure. Results showed that immunosuppressed children and those with a personal history of cardiovascular disease had a higher likelihood of being admitted to the ICU, while increasing age and the pandemic duration were associated with a lower likelihood of admission. The study's results have the potential to inform clinical decision-making and enhance management and outcomes for children affected by COVID-19 in Mexico.

Optimal time for COVID-19 vaccination in rituximab-treated dermatologic patients

Background

By depleting circulating B lymphocytes, rituximab time-dependently suppresses coronavirus disease 2019 (COVID-19) vaccines’ humoral immunogenicity for a prolonged period. The optimal time to vaccinate rituximab-exposed immune-mediated dermatologic disease (IMDD) patients is currently unclear.

Objective

To estimate the vaccination timeframe that equalized the occurrence of humoral immunogenicity outcomes between rituximab-exposed and rituximab-naïve IMDD patients.

Methods

This retrospective cohort study recruited rituximab-exposed and age-matched rituximab-naïve subjects tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific immunity post-vaccination. Baseline clinical and immunological data (i.e., immunoglobulin levels, lymphocyte immunophenotyping) and SARS-CoV-2-specific immunity levels were extracted. The outcomes compared were the percentages of subjects who produced neutralizing antibodies (seroconversion rates, SR) and SARS-CoV-2-specific IgG levels among seroconverters. The outcomes were first analyzed using multiple regressions adjusted for the effects of corticosteroid use, steroid-spearing agents, and pre-vaccination immunological status (i.e., IgM levels, the percentages of the total, naïve, and memory B lymphocytes) to identify rituximab-related immunogenicity outcomes. The rituximab-related outcome differences with a 95% confidence interval (CI) between groups were calculated, starting by including every subject and then narrowing down to those with longer rituximab-to-vaccination intervals (≥3, ≥6, ≥9, ≥12 months). The desirable cut-off performances were &lt;25% outcome inferiority observed among rituximab-exposed subgroups compared to rituximab-naïve subjects, and the positive likelihood ratio (LR+) for the corresponding outcomes ≥2.

Findings

Forty-five rituximab-exposed and 90 rituximab-naive subjects were included. The regression analysis demonstrated a negative association between rituximab exposure status and SR but not with SARS-CoV-2-specific IgG levels. Nine-month rituximab-to-vaccination cut-off fulfilled our prespecified diagnostic performance (SR difference between rituximab-exposed and rituximab-naïve group [95%CI]: -2.6 [-23.3, 18.1], LR+: 2.6) and coincided with the repopulation of naïve B lymphocytes in these patients.

Conclusions

Nine months of rituximab-to-vaccination interval maximize the immunological benefits of COVID-19 vaccines while avoiding unnecessary delay in vaccination and rituximab treatment for IMDD patients.

Predictors of Recurrent Laboratory-Confirmed Symptomatic SARS-CoV-2 Infections in a Cohort of Healthcare Workers

Background: Repeated SARS-CoV-2 infections are plausible and related published data are scarce. We aimed to identify factors associated with the risk of recurrent (three episodes) laboratory-confirmed symptomatic SARS-CoV-2 infections. Methods: A retrospective cohort study was conducted, and 1,700 healthcare workers were enrolled. We used risk ratios (RR) and 95% confidence intervals (CI) to evaluate the factors associated with symptomatic SARS-CoV-2 infections. Results: We identified 14 participants with recurrent illness episodes. Therefore, the incidence rate was 8.5 per 10,000 person months. In a multiple-model study, vaccinated adults (vs. unvaccinated, RR = 1.05 [1.03–1.06]) and those with a severe first illness episode (vs. mild disease, RR = 1.05 [1.01–1.10]) were at increased risk for repeated symptomatic SARS-CoV-2 reinfections. Increasing age showed a protective effect (per each additional year of age: RR = 0.98 [0.97–0.99]). Conclusions: Our results suggest that recurrent SARS-CoV-2 infections are rare events in adults, and they seem to be determined, partially, by vaccination status and age.

Attitudes toward the pandemic and COVID-19 vaccination intention among German university students and the general population: Results from two cross-sectional surveys

Background

The COVID-19 pandemic has had an impact on nearly all people. Vaccines provide an effective tool to combat the pandemic, however, vaccination hesitancy remains an issue. This study aims to investigate (a) students' attitudes toward the pandemic, (b) potential differences in attitudes between university students and the general population, and (c) to examine predictors of vaccination intention in both samples.

Methods

In this cross-sectional study data from two research projects were analyzed and compared. First, attitudes toward the COVID-19 pandemic in German university students were assessed within a cross-sectional anonymous online survey (March-April 2021, N = 5,639) and analyzed quantitatively and also qualitatively (free text field answers examined positive and negative aspects of the pandemic). Second, data from a cross-sectional survey within the COVID-19 Snapshot Monitoring project (COSMO; 29^th wave of data collection, December 2020, N = 1,387) in the German general population was analyzed. Both samples, were compared in sharedly used variables, regarding attitudes toward the pandemic and vaccination intention, and factors associated with vaccination (logistic regression analyses).

Results

In comparison to the general population, university students were significantly more likely to report being worried about/thinking about the coronavirus and to perceive the coronavirus as overrepresented in the media (all p < 0.001). University students reported a more supportive attitude toward vaccinations in general (students: M = 4.57, SD = 0.85; general population: M = 3.92, SD = 1.27) and a significantly higher vaccination intention (students: n = 4,438, 78.7%; general population: n = 635, 47.7%) than the general population (p < 0.001). Regression analyses revealed that in university students, vaccination intention was significantly predicted by not having children, a supporting attitude toward vaccinations in general, the belief that the coronavirus is overrepresented in the media, and less thinking about/worrying about the coronavirus (all p < 0.05). In the general population, vaccination intention was significantly associated with male gender, higher age, not having children, a supporting attitude toward vaccinations in general, and the belief that the coronavirus is overrepresented in the media (p < 0.05). The qualitative analysis among university students revealed that the most frequently stated positive aspect of the pandemic was to be more flexible due to digitalization (n = 1,301 statements, 22.2%) and the most frequently stated negative aspect was restriction in social life (n = 3,572 statements, 24.2%).

Conclusion

The results indicate differences in the attitudes toward the pandemic between university students and the general population. In addition, differences regarding factors associated with vaccination intention were found in both samples. These results could be important to be considered when designing and targeting vaccination campaigns aiming at informing different population or age groups.

Study registration

DRKS00022424.