Controlling the pandemic during the SARS-CoV-2 vaccination rollout

Modeling vaccination prioritization strategies for post-pandemic COVID-19 in the Republic of Korea accounting for under-reporting and age-structure

BACKGROUND

Vaccination has played a key role in limiting the impacts of COVID-19. Even though the acute phase of the COVID-19 pandemic is now over, the potential for substantial numbers of cases and deaths due to novel SARS-CoV-2 variants remains. In the Republic of Korea, a strategy of vaccinating individuals in high-risk groups annually began in October 2023.

METHODS

We used mathematical modeling to assess the effectiveness of alternative vaccination strategies under different assumptions about the number of available vaccine doses. An age-structured transmission model was developed using vaccination and seropositivity data. Various vaccination scenarios were considered, taking into account the effect of undetected or unreported cases (with different levels of reporting by age group): S1: prioritizing vaccination towards the oldest individuals; S2: prioritizing vaccination towards the youngest individuals; and S3: spreading vaccines among all age groups.

RESULTS

Our analysis reveals three key findings. First, administering vaccines to older age groups reduces the number of deaths, while instead targeting younger individuals reduces the number of infections. Second, with approximately 6,000,000 doses available annually, it is recommended that older age groups are prioritized for vaccination, achieving a substantial reduction in the number of deaths compared to a scenario without vaccination. Finally, since case detection (and subsequent isolation) affects transmission, the number of cumulative cases was found to be affected substantially by changes in the reporting rate.

CONCLUSIONS

In conclusion, vaccination and case detection (facilitated by contact tracing) both play important roles in limiting the impacts of COVID-19. The mathematical modeling approach presented here provides a framework for assessing the effectiveness of different vaccination strategies in scenarios with limited vaccine supply.

Identifying the main drivers of transmission in the early phase of the COVID-19 pandemic in Portugal

In this study, we employed a modeling approach to describe how changes in age-specific epidemiological characteristics, such as behaviour, i.e. contact patterns, susceptibility and infectivity, influence the basic reproduction number R 0 , while accounting for heterogeneity in transmission. We computed sensitivity measures related to R 0 , that describe the relative contribution of each age group towards overall transmission. Additionally, we proposed a new indicator that provides the expected relative change in the number of new infections, given a public health intervention. Studying the outbreak of COVID-19 in Portugal during March 2020, our results show that the main drivers of transmission were individuals 30-59 years old. Furthermore, by studying the impact of imposed changes in susceptibility and infectivity, our results demonstrate that a 10% decrease in susceptibility for the 30-39 years old results in a incidence reduction after 3 generations of approximately 17% in this age group and 4-6% reduction as an indirect effect in the remaining age groups. The presented methodology provides tools to inform the allocation strategy of mitigation measures in an outbreak of an infectious disease. Its inherent versatility enables the easy incorporation of data specific to various populations, facilitating a comparative analysis of epidemic control effects across different countries.

SARS-CoV-2 Immunization Index in the Academic Community: A Retrospective Post-Vaccination Study

BACKGROUND/OBJECTIVES

The COVID-19 pandemic has revolutionized vaccine production and compelled a massive global vaccination campaign. This study aimed to estimate the positivity and levels of SARS-CoV-2 IgG antibodies acquired due to vaccination and infection in the academic population of a Portuguese university.

METHODS

Blood samples were collected and analyzed through the ELISA methodology, and statistical analysis was performed.

RESULTS

A total of 529 volunteers with at least one dose of the vaccine were enrolled in this study. Individuals without a prior COVID-19 diagnosis were divided into two groups: 350, who received a full vaccination, and 114, who received a full vaccination and a booster dose of the same vaccine (81) and mixed vaccines (33). Regarding the individuals who reported a prior SARS-CoV-2 infection, 31 received a full vaccination, and 34 received only one vaccination dose. Data analysis showed a higher level of IgG against SARS-CoV-2 in individuals who were younger, female, who received the Moderna vaccine, with recent post-vaccine administration, a mixed booster dose, and prior SARS-CoV-2 infection.

CONCLUSIONS

Assessing vaccination's effectiveness and group immunity is crucial for pandemic management, particularly in academic environments with high individual mobility, in order to define groups at risk and redirect infection control strategies.

Antiseptics: An expeditious third force in the prevention and management of coronavirus diseases

Notably, severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS) and coronavirus disease 2019 (COVID-19) have all had significant negative impact on global health and economy. COVID-19 alone, has resulted to millions of deaths with new cases and mortality still being reported in its various waves. The development and use of vaccines have not stopped the transmission of SARS coronavirus 2 (SARS-CoV-2), the etiological agent of COVID-19, even among vaccinated individuals. The use of vaccines and curative drugs should be supplemented with adoption of simple hygiene preventive measures in the fight against the spread of the virus, especially for healthcare workers. Several virucidal topical antiseptics, such as povidone-iodine (PVP-I), citrox, cyclodextrins among others, have been demonstrated to be efficacious in the inactivation of SARS-CoV-2 and other coronaviruses in both in vitro and in vivo studies. The strategic application of these virucidal formulations could provide the additional impetus needed to effectively control the spread of the virus. We have here presented a simple dimension towards curtailing the dissemination of COVID-19, and other coronaviruses, through the application of effective oral, nasal and eye antiseptics among patients and medical personnel. We have further discussed the mechanism of action of some of these commonly available virucidal solutions while also highlighting some essential controversies in their use.

Comparative Atlas of SARS-CoV-2 Substitution Mutations: A Focus on Iranian Strains Amidst Global Trends

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new emerging coronavirus that caused coronavirus disease 2019 (COVID-19). Whole-genome tracking of SARS-CoV-2 enhanced our understanding of the mechanism of the disease, control, and prevention of COVID-19.

METHODS

we analyzed 3368 SARS-CoV-2 protein sequences from Iran and compared them with 15.6 million global sequences in the GISAID database, using the Wuhan-Hu-1 strain as a reference.

RESULTS

Our investigation revealed that NSP12-P323L, ORF9c-G50N, NSP14-I42V, membrane-A63T, Q19E, and NSP3-G489S were found to be the most frequent mutations among Iranian SARS-CoV-2 sequences. Furthermore, it was observed that more than 94% of the SARS-CoV-2 genome, including NSP7, NSP8, NSP9, NSP10, NSP11, and ORF8, had no mutations when compared to the Wuhan-Hu-1 strain. Finally, our data indicated that the ORF3a-T24I, NSP3-G489S, NSP5-P132H, NSP14-I42V, envelope-T9I, nucleocapsid-D3L, membrane-Q19E, and membrane-A63T mutations might be responsible factors for the surge in the SARS-CoV-2 Omicron variant wave in Iran.

CONCLUSIONS

real-time genomic surveillance is crucial for detecting new SARS-CoV-2 variants, updating diagnostic tools, designing vaccines, and understanding adaptation to new environments.

Psychosocial correlates of parents' willingness to vaccinate their children against COVID-19

BACKGROUND

Public health guidance recommended that children who are 6 months or older be vaccinated against COVID-19 in June of 2022. In the U.S., 56% of children under 17 had not received the COVID-19 vaccination in 2023. We examine parents' willingness to vaccinate their children against COVID-19 using the theory of planned behavior in order to design effective strategies to promote vaccine uptake.

METHODS

The Philadelphia Community Engagement Alliance is part of an NIH community-engaged consortium focused on addressing COVID-19 disparities across the U.S. We surveyed 1,008 Philadelphia parents (mean age 36.86, SD 6.55; 42.3% racial/ethnic minorities) between September 2021 and February 2022, a period when guidance for child vaccination was anticipated. Structural Equation Modeling analysis examined associations between parental willingness and vaccine-related attitudes, norms, and perceived control. Covariates included parents' COVID-19 vaccination status, race/ethnicity, gender, and survey completion post-CDC pediatric COVID-19 vaccination guidelines. Subgroup analyses by race/ethnicity and gender were conducted.

RESULTS

Our model demonstrated good fit (χ2 = 907.37, df = 419, p<0.001; comparative fit index [CFI] = 0.951; non-normed fit index [NNFI] = 0.946; root mean square error of approximation [RMSEA] = 0.034 with 95% CI = 0.030-0.038). Attitudes ([Formula: see text] = 0.447, p<0.001) and subjective norms ([Formula: see text] = 0.309, p = 0.002) were predictors of intention. Racial/ethnic minority parents exhibited weaker vaccination intentions ([Formula: see text] = -0.053, p = 0.028) than non-Hispanic White parents.

CONCLUSIONS

Parents' attitudes and norms influence their vaccination intentions. Despite the survey predating widespread child vaccine availability, findings are pertinent given the need to increase and sustain pediatric vaccinations against COVID-19. Interventions promoting positive vaccine attitudes and prosocial norms are warranted. Tailored interventions and diverse communication strategies for parental subgroups may be useful to ensure comprehensive and effective vaccination initiatives.

Medical knowledge of ChatGPT in public health, infectious diseases, COVID-19 pandemic, and vaccines: multiple choice questions examination based performance

Background

At the beginning of the year 2023, the Chatbot Generative Pre-Trained Transformer (ChatGPT) gained remarkable attention from the public. There is a great discussion about ChatGPT and its knowledge in medical sciences, however, literature is lacking to evaluate the ChatGPT knowledge level in public health. Therefore, this study investigates the knowledge of ChatGPT in public health, infectious diseases, the COVID-19 pandemic, and its vaccines.

Methods

Multiple Choice Questions (MCQs) bank was established. The question's contents were reviewed and confirmed that the questions were appropriate to the contents. The MCQs were based on the case scenario, with four sub-stems, with a single correct answer. From the MCQs bank, 60 MCQs we selected, 30 MCQs were from public health, and infectious diseases topics, 17 MCQs were from the COVID-19 pandemic, and 13 MCQs were on COVID-19 vaccines. Each MCQ was manually entered, and tasks were given to determine the knowledge level of ChatGPT on MCQs.

Results

Out of a total of 60 MCQs in public health, infectious diseases, the COVID-19 pandemic, and vaccines, ChatGPT attempted all the MCQs and obtained 17/30 (56.66%) marks in public health, infectious diseases, 15/17 (88.23%) in COVID-19, and 12/13 (92.30%) marks in COVID-19 vaccines MCQs, with an overall score of 44/60 (73.33%). The observed results of the correct answers in each section were significantly higher (p = 0.001). The ChatGPT obtained satisfactory grades in all three domains of public health, infectious diseases, and COVID-19 pandemic-allied examination.

Conclusion

ChatGPT has satisfactory knowledge of public health, infectious diseases, the COVID-19 pandemic, and its vaccines. In future, ChatGPT may assist medical educators, academicians, and healthcare professionals in providing a better understanding of public health, infectious diseases, the COVID-19 pandemic, and vaccines.

Awareness and Safety of COVID-19 Vaccination in Pregnancy

Background

COVID-19 vaccines are safe in pregnancy, as they do not contain a live attenuated virus. Mass vaccination is a key to control the pandemic. Neonates have been shown to be susceptible to severe illness, so maternal vaccination is important to provide neonatal vaccination.

Methods

The present study was conducted for a period of one year from November 21, 2021 to October 2O, 2022 at the Department of Obstetrics and Gynecology A.S.J.S.A.T.D.S. medical college, Fatehpur. It was a hospital-based cross-sectional study. This study aimed to investigate the efficacy, safety, attitude, side effect and maternal neonatal outcome of COVID-19 vaccination among pregnant women.

Results

Out of 3320 pregnant women delivered, only 1170 (35.24%) received at least one dose of COVID-19 vaccine. 69.23% were unaware of the type of COVID-19 vaccine. 66.15% were vaccinated for both the doses before pregnancy. 12.30% of women had taken only the first dose of COVID vaccine before pregnancy. Majority had fever with chills after the first dose. Fatigue was most common side effect after second dose, and no one had any rash or allergic reaction. 56.15% delivered vaginally, 37.69% had LSCS for different obstetric indications, and 6.15% had instrumental delivery. During the antenatal period, 38.46% developed anemia, 11.54% had preterm labor, 2.05% had gestational diabetes, 2.30% developed preeclampsia, and 3.85% developed hypothyroidism. 3.07% prolonged labor in intrapartum period, and 6.92% women developed PPH. 50.77% newborns were between 2.5 and 2.9 kg, and majority 71.54% newborns had an APGAR score of 7 or more at 5 minutes. 14.62% newborns had meconium aspiration syndrome, 3.84% had respiratory distress syndrome, and 20.34% needed NICU admission more than 24 hours.

Conclusion

Available data do not support increased risk of adverse outcome following COVID-19 vaccination. We recommend vaccination during pregnancy as benefit outweigh the potential risk.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13224-023-01918-w.

Estimating the population effectiveness of interventions against COVID-19 in France: A modelling study

BACKGROUND

Non-pharmaceutical interventions (NPIs) and vaccines have been widely used to manage the COVID-19 pandemic. However, uncertainty persists regarding the effectiveness of these interventions due to data quality issues, methodological challenges, and differing contextual factors. Accurate estimation of their effects is crucial for future epidemic preparedness.

METHODS

To address this, we developed a population-based mechanistic model that includes the impact of NPIs and vaccines on SARS-CoV-2 transmission and hospitalization rates. Our statistical approach estimated all parameters in one step, accurately propagating uncertainty. We fitted the model to comprehensive epidemiological data in France from March 2020 to October 2021. With the same model, we simulated scenarios of vaccine rollout.

RESULTS

The first lockdown was the most effective, reducing transmission by 84 % (95 % confidence interval (CI) 83-85). Subsequent lockdowns had diminished effectiveness (reduction of 74 % (69-77) and 11 % (9-18), respectively). A 6 pm curfew was more effective than one at 8 pm (68 % (66-69) vs. 48 % (45-49) reduction), while school closures reduced transmission by 15 % (12-18). In a scenario without vaccines before November 2021, we predicted 159,000 or 168 % (95 % prediction interval (PI) 70-315) more deaths and 1,488,000 or 300 % (133-492) more hospitalizations. If a vaccine had been available after 100 days, over 71,000 deaths (16,507-204,249) and 384,000 (88,579-1,020,386) hospitalizations could have been averted.

CONCLUSION

Our results highlight the substantial impact of NPIs, including lockdowns and curfews, in controlling the COVID-19 pandemic. We also demonstrate the value of the 100 days objective of the Coalition for Epidemic Preparedness Innovations (CEPI) initiative for vaccine availability.

Impact of Vaccination and Nonpharmaceutical Interventions With Possible COVID-19 Viral Evolutions Using an Agent-Based Simulation

Introduction

The COVID-19 pandemic continues with highly contagious variants and waning immunity. As the virus keeps evolving to be more infectious and immune evasive, some question whether the COVID-19 pandemic can be managed through sustainable public health measures.

Methods

We developed an agent-based simulation to explore the impact of COVID-19 mutations, periodic vaccinations, and nonpharmaceutical interventions on reducing COVID-19 deaths. The model is calibrated to the greater Seattle area by observing local epidemic data. We perform scenario analyses on viral mutations that change infectiousness, disease severity, and immune evasiveness from previous infections and vaccination every 6 months. The simulation is run until the end of year 2023.

Results

Variants with increased infectivity or increased immune evasion dominate previous strains. With enhanced immune protection from a pancoronavirus vaccine, the most optimistic periodic vaccination rate reduces average total deaths by 44.6% compared with the most pessimistic periodic vaccination rate. A strict threshold nonpharmaceutical intervention policy reduces average total deaths by 71.3% compared with an open society, whereas a moderate nonpharmaceutical intervention policy results in a 33.6% reduction.

Conclusions

Our findings highlight the potential benefits of pancoronavirus vaccines that offer enhanced and longer-lasting immunity. We emphasize the crucial role of nonpharmaceutical interventions in reducing COVID-19 deaths regardless of virus mutation scenarios. Owing to highly immune evasive and contagious SARS-CoV-2 variants, most scenarios in this study fail to reduce the mortality of COVID-19 to the level of influenza and pneumonia. However, our findings indicate that periodic vaccinations and a threshold nonpharmaceutical intervention policy may succeed in achieving this goal. This indicates the need for caution and vigilance in managing a continuing COVID-19 epidemic.

Age-specific transmission dynamics of SARS-CoV-2 during the first 2 years of the pandemic

During its first 2 years, the SARS-CoV-2 pandemic manifested as multiple waves shaped by complex interactions between variants of concern, non-pharmaceutical interventions, and the immunological landscape of the population. Understanding how the age-specific epidemiology of SARS-CoV-2 has evolved throughout the pandemic is crucial for informing policy decisions. In this article, we aimed to develop an inference-based modeling approach to reconstruct the burden of true infections and hospital admissions in children, adolescents, and adults over the seven waves of four variants (wild-type, Alpha, Delta, and Omicron BA.1) during the first 2 years of the pandemic, using the Netherlands as the motivating example. We find that reported cases are a considerable underestimate and a generally poor predictor of true infection burden, especially because case reporting differs by age. The contribution of children and adolescents to total infection and hospitalization burden increased with successive variants and was largest during the Omicron BA.1 period. However, the ratio of hospitalizations to infections decreased with each subsequent variant in all age categories. Before the Delta period, almost all infections were primary infections occurring in naive individuals. During the Delta and Omicron BA.1 periods, primary infections were common in children but relatively rare in adults who experienced either reinfections or breakthrough infections. Our approach can be used to understand age-specific epidemiology through successive waves in other countries where random community surveys uncovering true SARS-CoV-2 dynamics are absent but basic surveillance and statistics data are available.

Equilibrium points and their stability of COVID-19 in US

This study aims to develop an advanced mathematic model and investigate when and how will the COVID-19 in the US be evolved to endemic. We employed a nonlinear ordinary differential equations-based model to simulate COVID-19 transmission dynamics, factoring in vaccination efforts. Multi-stability analysis was performed on daily new infection data from January 12, 2021 to December 12, 2022 across 50 states in the US. Key indices such as eigenvalues and the basic reproduction number were utilized to evaluate stability and investigate how the pandemic COVD-19 will evolve to endemic in the US. The transmissional, recovery, vaccination rates, vaccination effectiveness, eigenvalues and reproduction numbers ([Formula: see text] and [Formula: see text]) in the endemic equilibrium point were estimated. The stability attractor regions for these parameters were identified and ranked. Our multi-stability analysis revealed that while the endemic equilibrium points in the 50 states remain unstable, there is a significant trend towards stable endemicity in the US. The study's stability analysis, coupled with observed epidemiological waves in the US, suggested that the COVID-19 pandemic may not conclude with the virus's eradication. Nevertheless, the virus is gradually becoming endemic. Effectively strategizing vaccine distribution is pivotal for this transition.

Understanding attitudes and beliefs regarding COVID-19 vaccines among transitional-aged youth with mental health concerns: a youth-led qualitative study

BACKGROUND

Transitional-aged youth (16-29 years) with mental health concerns have experienced a disproportionate burden of the COVID-19 pandemic. Vaccination is limited in this population; however, determinants of its vaccine hesitancy are not yet thoroughly characterised.

OBJECTIVES

This study aimed to answer the following research question: What are the beliefs and attitudes of youth with mental illness about COVID-19 vaccines, and how do these perspectives affect vaccine acceptance? The study aims to generate findings to inform the development of vaccine resources specific to youth with mental health concerns.

METHODS

A qualitative methodology with a youth engagement focus was used to conduct in-depth semistructured interviews with transitional-aged youth aged 16-29 years with one or more self-reported mental health diagnoses or concerns. Mental health concerns encompassed a wide range of symptoms and diagnoses, including mood disorders, anxiety disorders, neurodevelopmental disorders and personality disorders. Participants were recruited from seven main mental health clinical and support networks across Canada. Transcripts from 46 youth and 6 family member interviews were analysed using thematic analysis.

RESULTS

Two major themes were generated: (1) factors affecting trust in COVID-19 vaccines and (2) mental health influences and safety considerations in vaccine decision-making. Subthemes included trust in vaccines, trust in healthcare providers, trust in government and mistreatment towards racialised populations, and direct and indirect influences of mental health.

CONCLUSIONS

Our analysis suggests how lived experiences of mental illness affected vaccine decision-making and related factors that can be targeted to increase vaccine uptake. Our findings provide new insights into vaccine attitudes among youth with mental health concerns, which is highly relevant to ongoing vaccination efforts for new COVID-19 strains as well as other transmissible diseases and future pandemics. Next steps include cocreating youth-specific public health and clinical resources to encourage vaccination in this population.

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.

Modeling geographic vaccination strategies for COVID-19 in Norway

Vaccination was a key intervention in controlling the COVID-19 pandemic globally. In early 2021, Norway faced significant regional variations in COVID-19 incidence and prevalence, with large differences in population density, necessitating efficient vaccine allocation to reduce infections and severe outcomes. This study explored alternative vaccination strategies to minimize health outcomes (infections, hospitalizations, ICU admissions, deaths) by varying regions prioritized, extra doses prioritized, and implementation start time. Using two models (individual-based and meta-population), we simulated COVID-19 transmission during the primary vaccination period in Norway, covering the first 7 months of 2021. We investigated alternative strategies to allocate more vaccine doses to regions with a higher force of infection. We also examined the robustness of our results and highlighted potential structural differences between the two models. Our findings suggest that early vaccine prioritization could reduce COVID-19 related health outcomes by 8% to 20% compared to a baseline strategy without geographic prioritization. For minimizing infections, hospitalizations, or ICU admissions, the best strategy was to initially allocate all available vaccine doses to fewer high-risk municipalities, comprising approximately one-fourth of the population. For minimizing deaths, a moderate level of geographic prioritization, with approximately one-third of the population receiving doubled doses, gave the best outcomes by balancing the trade-off between vaccinating younger people in high-risk areas and older people in low-risk areas. The actual strategy implemented in Norway was a two-step moderate level aimed at maintaining the balance and ensuring ethical considerations and public trust. However, it did not offer significant advantages over the baseline strategy without geographic prioritization. Earlier implementation of geographic prioritization could have more effectively addressed the main wave of infections, substantially reducing the national burden of the pandemic.

Is there a Possible Association between Multiple Myeloma Relapse and Coronavirus Disease 2019 Vaccination? A Case Report

Due to the high morbidity and mortality of the coronavirus disease 2019 (COVID-19) in patients with malignancy, the necessity of vaccination in this group of patients became particularly important. Although a large number of studies have reported the safety of COVID-19 vaccination in multiple myeloma (MM) patients, the effect of the COVID-19 vaccine on MM relapse has not yet been reported. Here, we report a case of a possible association between relapse of MM and COVID-19 vaccination with Sinopharm®, an inactivated virus vaccine, in a patient with MM who has remained in complete remission for about 4 years. The MM relapse in the patient was diagnosed by both clinical findings and laboratory workup including serum protein electrophoresis, bone marrow aspiration, and biopsy. Despite this possible association between COVID-19 vaccination and MM relapse in the patient, given its importance in reducing mortality and having an acceptable safety profile, the COVID-19 vaccine should be administered to all cancer patients. However, careful monitoring and follow-up are recommended in patients with MM after COVID-19 vaccination.

Trends in SARS-CoV-2 seroprevalence in Albania during the 2021-2022 pandemic year

Background

Monitoring SARS-CoV-2 seroprevalence dynamics during the COVID-19 pandemic is crucial for understanding population immunity and providing insights into public health policies. Limited data exist on this from Albania and other Eastern European countries. This study aimed to investigate SARS-CoV-2 seroprevalence in Albania, comparing August 2021 and August 2022 data from two representative samples of the general population. The objective was to understand the temporal dynamics of SARS-CoV-2 antibodies across age groups and assess the impacts of natural infection and vaccination on population immunity.

Methods

This longitudinal study was conducted in two consecutive cross-sectional assessments 12 months apart in Albania's urban all-ages population. IgG anti-Spike-1 and anti-Nucleoprotein SARS-CoV-2 antibodies were measured using ELISA, focusing on seropositivity rates and antibody levels.

Methods

The study encompassed 2143 and 2183 individuals in August 2021 and 2022, respectively, with the anti-S1-IgG seropositivity rate escalating from 70.9 ​% to 92.1 ​%. In 2021, seroprevalence ranged from 49.6 ​% (0-15 years) to 82 ​% (>60 years). By August 2022, it surpassed 90 ​% in most age groups, except 0-15 years (73.8 ​%). "Hybrid" immunity (COVID-19+ and Vaccine+) reached 56.6 ​% in 2022, or 2.8 times higher than in 2021, exhibiting the highest antibody levels compared to the only vaccinated or previously COVID-19-infected individuals.

Conclusion

This study highlights an overall 94 % seroprevalence in the Albanian population in August 2022 and robust "hybrid" immunity, suggesting substantial protective immunity against SARS-CoV-2. The lower immunity in the 0-15 age group underscores the necessity for youth-targeted vaccine campaigns. These findings provide valuable insights for shaping healthcare measures and vaccination policies.

Detection of COVID-19 epidemic outbreak using machine learning

Background

The coronavirus disease (COVID-19) pandemic has spread rapidly across the world, creating an urgent need for predictive models that can help healthcare providers prepare and respond to outbreaks more quickly and effectively, and ultimately improve patient care. Early detection and warning systems are crucial for preventing and controlling epidemic spread.

Objective

In this study, we aimed to propose a machine learning-based method to predict the transmission trend of COVID-19 and a new approach to detect the start time of new outbreaks by analyzing epidemiological data.

Methods

We developed a risk index to measure the change in the transmission trend. We applied machine learning (ML) techniques to predict COVID-19 transmission trends, categorized into three labels: decrease (L0), maintain (L1), and increase (L2). We used Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB) as ML models. We employed grid search methods to determine the optimal hyperparameters for these three models. We proposed a new method to detect the start time of new outbreaks based on label 2, which was sustained for at least 14 days (i.e., the duration of maintenance). We compared the performance of different ML models to identify the most accurate approach for outbreak detection. We conducted sensitivity analysis for the duration of maintenance between 7 days and 28 days.

Results

ML methods demonstrated high accuracy (over 94%) in estimating the classification of the transmission trends. Our proposed method successfully predicted the start time of new outbreaks, enabling us to detect a total of seven estimated outbreaks, while there were five reported outbreaks between March 2020 and October 2022 in Korea. It means that our method could detect minor outbreaks. Among the ML models, the RF and XGB classifiers exhibited the highest accuracy in outbreak detection.

Conclusion

The study highlights the strength of our method in accurately predicting the timing of an outbreak using an interpretable and explainable approach. It could provide a standard for predicting the start time of new outbreaks and detecting future transmission trends. This method can contribute to the development of targeted prevention and control measures and enhance resource management during the pandemic.

Short-term and long-term behavioral effects of vaccination mandates

Current COVID-19 vaccination levels are insufficient to achieve herd immunity. To implement effective interventions toward ending the pandemic, it is essential to understand why people are motivated and willing to receive vaccination. The study aims to evaluate attitudes toward COVID-19 vaccination mandates and the impact of policies on future vaccine uptake and behavior utilizing self-determination theory. We conducted an online survey (n = 569) in the U.S. and Turkey to investigate a relationship between respondents' psychological needs and their willingness and motivation to receive COVID vaccination. The study examined the possible impact of vaccine mandates on these needs. Autonomy satisfaction was the leading predictor of willingness to receive vaccination (p < .0001). Relatedness satisfaction was the leading predictor of one's intention to receive vaccination (OR = 3.382; p = .0001). The strongest positive correlation was found between needs frustration and external motivation. A moderate positive correlation was found between competence frustration and introjected motivation. No association was found between vaccine mandates and psychological needs. Need satisfaction, especially autonomy and relatedness, appear to positively influence willingness and intention to receive a vaccination. On the other hand, need frustration, especially autonomy and competence frustration, correlates with external motivation, thereby suggesting a detrimental long-term effect on vaccination behavior. Need satisfaction promotes positive vaccination behavior, while need frustration might adversely affect motivation and willingness to receive vaccination. Strategies promoting autonomous decision-making might be more effective than vaccination enforcement in sustaining positive vaccination behavior.

Willingness to vaccinate among adults, and factors associated with vaccine acceptance of COVID-19 vaccines in a nationwide study in Poland between March 2021 and April 2022

Introduction

Despite the availability, safety and effectiveness of COVID-19 vaccines, Poland remains one of the six countries of the European Union with the lowest cumulative uptake of the vaccine's primary course in the general population. This study examined willingness to vaccinate and the associated factors in samples of unvaccinated and vaccinated adults between March 2021 and April 2022.

Methods

Data were collected using OBSER-CO, a nationwide, repeated cross-sectional study, conducted at four different time points (rounds). Data on willingness to vaccinate among the unvaccinated (at all rounds) and willingness to receive another dose in the vaccinated (at 2 rounds-after booster introduction), reasons for reluctance, sociodemographic, health, and behavioral factors were collected using a uniform questionnaire via computer-assisted telephone interviewing. In each round, more than 20,000 respondents were interviewed. To assess associations between factors and willingness to vaccinate, separate multivariable logistic regression models were fitted for each factor at each round and adjusted for confounders.

Results

Between rounds 1 and 4 (March 2021-April 2022), in the unvaccinated, willingness to vaccinate declined from 73 to 12%, whereas in the vaccinated, willingness to receive another dose declined from 90 to 53%. The highest magnitude of decline between subsequent rounds occurred during the Omicron wave. Overall, concerns about side effects, effectiveness, and vaccine adverse effects were common but decreased over time. Age, gender, employment, place of residence, COVID-19 diagnosis or exposure, hospitalization, and participation in social activities were among the factors associated with willingness. However, associations changed over rounds highlighting the influence of different pandemic waves and variants.

Conclusion

We observed a declining and multifactorial willingness to vaccinate in Poland, with vaccine attitudes dynamically changing across subsequent rounds. To address vaccine concerns, sustained health communication about COVID-19 vaccines is essential, especially after the emergence of new variants.

Safety, immunogenicity and efficacy of an mRNA-based COVID-19 vaccine, GLB-COV2-043, in preclinical animal models

Several COVID-19 vaccines, some more efficacious than others, are now available and deployed, including multiple mRNA- and viral vector-based vaccines. With the focus on creating cost-effective solutions that can reach the low- and medium- income world, GreenLight Biosciences has developed an mRNA vaccine candidate, GLB-COV2-043, encoding for the full-length SARS-CoV-2 Wuhan wild-type spike protein. In pre-clinical studies in mice, GLB-COV2-043 induced robust antigen-specific binding and virus-neutralizing antibody responses targeting homologous and heterologous SARS-CoV-2 variants and a TH1-biased immune response. Boosting mice with monovalent or bivalent mRNA-LNPs provided rapid recall and long-lasting neutralizing antibody titers, an increase in antibody avidity and breadth that was held over time and generation of antigen-specific memory B- and T- cells. In hamsters, vaccination with GLB-COV2-043 led to lower viral loads, reduced incidence of SARS-CoV-2-related microscopic findings in lungs, and protection against weight loss after heterologous challenge with Omicron BA.1 live virus. Altogether, these data indicate that GLB-COV2-043 mRNA-LNP vaccine candidate elicits robust protective humoral and cellular immune responses and establishes our mRNA-LNP platform for subsequent clinical evaluations.

The onset of de novo autoantibodies in healthcare workers after mRNA based anti-SARS-CoV-2 vaccines: a single centre prospective follow-up study

Nowadays, data concerning the risk of autoimmune disease after SARS-CoV-2 (COVID-19) vaccination is controversial. The aim of this single centre prospective follow-up study was to evaluate whether healthcare workers (HCWs) vaccinated with BNT162b2 mRNA and mRNA-1273 will show a development and/or a persistence of autoantibodies, focussing on the detection of antibodies against nuclear antigens (antinuclear antibodies, ANA). We enrolled 155 HCWs, however only 108 of them received the third dose and were considered for further analysis. Blood samples were collected before vaccine inoculation (T0), at 3 (T1) and 12 months (T2) after the first dose. All samples were analysed for the presence of a) ANA using indirect Immunofluorescence [IIF] (dilutions of 1:80, 1:160. 1:320 and 1:640), and anti-smooth muscle antibodies (ASMA); b) anti-myeloperoxidase (anti-MPO), anti-proteinase 3 (anti-PR3) and anti-citrullinated peptide antibodies (aCCP) [FEIA]; c) anti-phospholipid antibodies (anticardiolipin [aCL], anti-beta-2- glycoprotein I [anti-ß-2GPI] (Chemiluminescence). Line-blot technology was performed using the following kit: EUROLINE ANA profile 3 plus DFS70 (IgG). Our research suggests that mRNA based anti-SARSCoV-2 vaccines can induce the production of de novo ANA in 22/77(28,57%) of subjects and that the percentage of positivity seems to be directly correlated to the number of vaccine expositions: 6/77 (7,79%) after 2 doses; 16/77 (20,78%) after 3 doses. Since it is known that hyperstimulation of the immune system could lead to autoimmunity, these preliminary results seem to further sustain the idea that the hyperstimulation of the immune system might lead to an autoinflammatory mechanism and eventually to autoimmune disorders. However, the link between SARS-CoV-2 vaccination and the development of autoimmune diseases needs to be further investigated.

Vaccination effects on reducing COVID-19 complications in pregnancy: A large-scale report from Iran

OBJECTIVE

The objective of this study was to evaluate the effects of maternal coronavirus disease 2019 (COVID-19) vaccination on preventing severe complications of COVID-19 in pregnant women.

METHODS

A retrospective study was conducted in pregnant women infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during pregnancy and/or for up to 6 weeks postpartum between September 1, 2021, to January 30, 2022. The data was retrieved from a national database. The pregnant women were divided into two groups of vaccinated and unvaccinated. The proposed outcomes (the need for hospitalization, intensive care unit admission, and mechanical ventilation and products of conception complications) were compared between the two groups.

RESULTS

Approximately 90 000 pregnant women infected with COVID-19 were included in the study. The data of the vaccinated (19 922) and unvaccinated (70 147) groups were analyzed and compared. Pregnant patients in the vaccinated group had a significantly lower rate of hospitalization (21.2% vs 29.4%) (odds ratio [OR], 0.648 [95% confidence interval (CI), 0.625-0.673], P = 0.0001) and intensive care unit admission (3.7% vs 7.8%) (OR, 0.453 [95% CI, 0.382-0.535], P = 0.0001). The need for mechanical ventilation was also lower, although not statistically significant, in the vaccinated group than in the unvaccinated group (30 of 155 [19.4%] vs 418 of 1597 [26.2%]) (OR, 0.677 [95% CI, 0.448-1.024], P = 0.063). Cesarean section (54.3% vs 58.1%) (OR, 0.856 [95% CI, 0.751-0.977], P = 0.021) and stillbirth (0.4% vs 3.6%) (OR, 0.097 [95% CI, 0.026-0.252], P = 0.0001) were also significantly lower in the vaccinated patients. Most pregnant women in the vaccinated group (18 484-96.14%) received Sinopharm BIBP COVID-19 inactivated vaccine. No significant differences were seen in the effect of different types of COVID-19 vaccines on reducing COVID-19 complications in infected pregnant patients.

CONCLUSION

Maternal COVID-19 immunization is effective in reducing COVID-19 complications in infected pregnant women.

Safety of COVID-19 vaccination in pregnant women and their neonatal outcome: a narrative Review

BACKGROUND

Even through the fact that pregnant women are more and more severely infected with COVID-19 disease, there are still doubts about vaccinating these people due to the lack of sufficient evidence base information. So in this systematic review, we decided to study vaccinated and unvaccinated pregnant women regarding maternal, fetal and neonatal complications and outcomes.

THE STRATEGY OF SEARCHING

Between 30 December 2019 and 15 October 2021, electronic searches were performed on the databases of PubMed, Scopus, Google Scholar, and Cochrane library by searching in English and free full text. Keywords searched included these: maternal outcome, neonatal outcome, pregnancy, and COVID-19 vaccination. Among 451 articles, finally, seven studies were included to study pregnancy outcomes in vaccinated women compared to unvaccinated for systematic review purposes.

RESULTS

In this study 30257 vaccinated women in their third trimester compared to 132339 unvaccinated women in terms of age, the root of delivery, neonatal adverse outcomes. There were no significant differences between two groups in terms of: IUFD, and 1 min Apgar score, C/S rate, and NICU admission between the two groups, however, the rate of SGA, IUFD, and also neonatal jaundice, asphyxia, and hypoglycemia was more significant in the unvaccinated group comparing to the vaccinated group as a result. Among them, the chance of preterm labor pain was reported more among vaccinated patients. Emphasizing that, except 7.3% of the case population, everyone in the second and third trimesters had been vaccinated with mRNA COVID-19 vaccines.

CONCLUSION

COVID-19 vaccination during the second and third trimesters appears to be the right choice due to the immediate impact of COVID-19 antibodies on the developing fetus and formation of neonatal prophylaxis, as well as the absence of adverse outcomes for both the fetus and mothers.

From De Novo Design to Redesign: Harnessing Computational Protein Design for Understanding SARS-CoV-2 Molecular Mechanisms and Developing Therapeutics

The continuous emergence of novel SARS-CoV-2 variants and subvariants serves as compelling evidence that COVID-19 is an ongoing concern. The swift, well-coordinated response to the pandemic highlights how technological advancements can accelerate the detection, monitoring, and treatment of the disease. Robust surveillance systems have been established to understand the clinical characteristics of new variants, although the unpredictable nature of these variants presents significant challenges. Some variants have shown resistance to current treatments, but innovative technologies like computational protein design (CPD) offer promising solutions and versatile therapeutics against SARS-CoV-2. Advances in computing power, coupled with open-source platforms like AlphaFold and RFdiffusion (employing deep neural network and diffusion generative models), among many others, have accelerated the design of protein therapeutics with precise structures and intended functions. CPD has played a pivotal role in developing peptide inhibitors, mini proteins, protein mimics, decoy receptors, nanobodies, monoclonal antibodies, identifying drug-resistance mutations, and even redesigning native SARS-CoV-2 proteins. Pending regulatory approval, these designed therapies hold the potential for a lasting impact on human health and sustainability. As SARS-CoV-2 continues to evolve, use of such technologies enables the ongoing development of alternative strategies, thus equipping us for the "New Normal".

Cost-effectiveness analysis of COVID-19 variants effects in an age-structured model

This study analyzes the impact of COVID-19 variants on cost-effectiveness across age groups, considering vaccination efforts and nonpharmaceutical interventions in Republic of Korea. We aim to assess the costs needed to reduce COVID-19 cases and deaths using age-structured model. The proposed age-structured model analyzes COVID-19 transmission dynamics, evaluates vaccination effectiveness, and assesses the impact of the Delta and Omicron variants. The model is fitted using data from the Republic of Korea between February 2021 and November 2022. The cost-effectiveness of interventions, medical costs, and the cost of death for different age groups are evaluated through analysis. The impact of different variants on cases and deaths is also analyzed, with the Omicron variant increasing transmission rates and decreasing case-fatality rates compared to the Delta variant. The cost of interventions and deaths is higher for older age groups during both outbreaks, with the Omicron outbreak resulting in a higher overall cost due to increased medical costs and interventions. This analysis shows that the daily cost per person for both the Delta and Omicron variants falls within a similar range of approximately $10-$35. This highlights the importance of conducting cost-effect analyses when evaluating the impact of COVID-19 variants.

A model-based strategy for the COVID-19 vaccine roll-out in the Philippines

COVID-19 has affected millions of people worldwide, causing illness and death, and disrupting daily life while imposing a significant social and economic burden. Vaccination is an important control measure that significantly reduces mortality if properly and efficiently distributed. In this work, an age-structured model of COVID-19 transmission, incorporating an unreported infectious compartment, is developed. Three age groups are considered: young (0-19 years), adult (20-64 years), and elderly (65+ years). The transmission rate and reporting rate are determined for each group by utilizing the number of COVID-19 cases in the National Capital Region in the Philippines. Optimal control theory is employed to identify the best vaccine allocation to different age groups. Further, three different vaccination periods are considered to reflect phases of vaccination priority groups: the first, second, and third account for the inoculation of the elderly, adult and elderly, and all three age groups, respectively. This study could guide in making informed decisions in mitigating a population-structured disease transmission under limited resources.

Optimized numerical solutions of SIRDVW multiage model controlling SARS-CoV-2 vaccine roll out: An application to the Italian scenario

In the context of SARS-CoV-2 pandemic, mathematical modelling has played a fundamental role for making forecasts, simulating scenarios and evaluating the impact of preventive political, social and pharmaceutical measures. Optimal control theory represents a useful mathematical tool to plan the vaccination campaign aimed at eradicating the pandemic as fast as possible. The aim of this work is to explore the optimal prioritisation order for planning vaccination campaigns able to achieve specific goals, as the reduction of the amount of infected, deceased and hospitalized in a given time frame, among age classes. For this purpose, we introduce an age stratified SIR-like epidemic compartmental model settled in an abstract framework for modelling two-doses vaccination campaigns and conceived with the description of COVID19 disease. Compared to other recent works, our model incorporates all stages of the COVID-19 disease, including death or recovery, without accounting for additional specific compartments that would increase computational complexity and that are not relevant for our purposes. Moreover, we introduce an optimal control framework where the model is the state problem while the vaccine doses administered are the control variables. An extensive campaign of numerical tests, featured in the Italian scenario and calibrated on available data from Dipartimento di Protezione Civile Italiana, proves that the presented framework can be a valuable tool to support the planning of vaccination campaigns. Indeed, in each considered scenario, our optimization framework guarantees noticeable improvements in terms of reducing deceased, infected or hospitalized individuals with respect to the baseline vaccination policy.

COVID-19 vaccine hesitancy among pregnant women attending tertiary care centre: A cross-sectional study

OBJECTIVE

To evaluate the knowledge and attitude towards coronavirus disease 2019 (COVID-19) vaccination during pregnancy and to discover factors that lead to non-acceptance of vaccine.

METHODS

A cross-sectional study was performed in the Department of Obstetrics and Gynecology, Hamdard Institute of Medical Science & Research, New Delhi over a period of 3 months through a web-based questionnaire via Google form. The questionnaire was assessed using Cronbach α for internal consistency, which was 0.795.

RESULTS

News (74%) was the major source of knowledge among pregnant women. Around 60% women were not willing to receive the vaccine, mainly because of their fear of a harmful effect on pregnancy. The anticipated vaccine acceptance rate was 41% but actual vaccine acceptance rate in pregnancy was 7.3%.

CONCLUSION

Efforts should be made to reduce the gap of knowledge regarding vaccine among pregnant women.

Evolutionary implications of SARS-CoV-2 vaccination for the future design of vaccination strategies

Once the first SARS-CoV-2 vaccine became available, mass vaccination was the main pillar of the public health response to the COVID-19 pandemic. It was very effective in reducing hospitalizations and deaths. Here, we discuss the possibility that mass vaccination might accelerate SARS-CoV-2 evolution in antibody-binding regions compared to natural infection at the population level. Using the evidence of strong genetic variation in antibody-binding regions and taking advantage of the similarity between the envelope proteins of SARS-CoV-2 and influenza, we assume that immune selection pressure acting on these regions of the two viruses is similar. We discuss the consequences of this assumption for SARS-CoV-2 evolution in light of mathematical models developed previously for influenza. We further outline the implications of this phenomenon, if our assumptions are confirmed, for the future design of SARS-CoV-2 vaccination strategies.

No association of vaccination with inactivated COVID-19 vaccines before conception with pregnancy complications and adverse birth outcomes: A cohort study of 5457 Chinese pregnant women

Data on the safety of inactivated COVID-19 vaccines in pregnant women is limited and monitoring pregnancy outcomes is required. We aimed to examine whether vaccination with inactivated COVID-19 vaccines before conception was associated with pregnancy complications or adverse birth outcomes. We conducted a birth cohort study in Shanghai, China. A total of 7000 healthy pregnant women were enrolled, of whom 5848 were followed up through delivery. Vaccine administration information was obtained from electronic vaccination records. Relative risks (RRs) of gestational diabetes mellitus (GDM), hypertensive disorders in pregnancy (HDP), intrahepatic cholestasis of pregnancy (ICP), preterm birth (PTB), low birth weight (LBW), and macrosomia associated with COVID-19 vaccination were estimated by multivariable-adjusted log-binomial analysis. After exclusion, 5457 participants were included in the final analysis, of whom 2668 (48.9%) received at least two doses of an inactivated vaccine before conception. Compared with unvaccinated women, there was no significant increase in the risks of GDM (RR = 0.80, 95% confidence interval [CI], 0.69, 0.93), HDP (RR = 0.88, 95% CI, 0.70, 1.11), or ICP (RR = 1.61, 95% CI, 0.95, 2.72) in vaccinated women. Similarly, vaccination was not significantly associated with any increased risks of PTB (RR = 0.84, 95% CI, 0.67, 1.04), LBW (RR = 0.85, 95% CI, 0.66, 1.11), or macrosomia (RR = 1.10, 95% CI, 0.86, 1.42). The observed associations remained in all sensitivity analyses. Our findings suggested that vaccination with inactivated COVID-19 vaccines was not significantly associated with an increased risk of pregnancy complications or adverse birth outcomes.

Coupled disease-vaccination behavior dynamic analysis and its application in COVID-19 pandemic

Predicting the evolutionary dynamics of the COVID-19 pandemic is a complex challenge. The complexity increases when the vaccination process dynamic is also considered. In addition, when applying a voluntary vaccination policy, the simultaneous behavioral evolution of individuals who decide whether and when to vaccinate must be included. In this paper, a coupled disease-vaccination behavior dynamic model is introduced to study the coevolution of individual vaccination strategies and infection spreading. We study disease transmission by a mean-field compartment model and introduce a non-linear infection rate that takes into account the simultaneity of interactions. Besides, the evolutionary game theory is used to investigate the contemporary evolution of vaccination strategies. Our findings suggest that sharing information with the entire population about the negative and positive consequences of infection and vaccination is beneficial as it boosts behaviors that can reduce the final epidemic size. Finally, we validate our transmission mechanism on real data from the COVID-19 pandemic in France.

SARS-CoV-2 Seroprevalence Following a Large-Scale Vaccination Campaign in Portugal: Results of the National Serological Survey, September - November 2021

INTRODUCTION

Following a COVID-19 mass vaccination campaign, it is important to evaluate the population level of SARS-CoV-2 antibodies. The aim of this study was to estimate the seroprevalence rate of SARS-CoV-2 specific antibodies acquired due to infection or vaccination in the Portuguese population.

MATERIAL AND METHODS

The National Serological Survey (third wave - ISN3COVID-19) is a cross-sectional nationwide epidemiological study developed on a sample of 4545 Portuguese residents aged one year or older, between the 28th September 2021 and the 19th November 2021. The SARS-CoV-2 anti-nucleoprotein and anti-spike IgG antibody levels were determined in serum samples using Abbott Chemiluminescent Microparticle Immunoassays. Seroprevalence estimates were stratified by age group, sex, administrative region and self-reported chronic conditions. Medians and respective 95% confidence intervals were used to describe the distribution of SARS-CoV-2 specific antibodies in specific population subgroups.

RESULTS

The total seroprevalence rate of SARS-CoV-2 was 86.4% (95% CI: 85.2% to 87.6%). A higher seroprevalence rate was estimated for women (88.3%), 50 to 59 years-old (96.5%) and in those with two or more self-reported chronic conditions (90.8%). A higher IgG (anti-Spike) concentration was observed in individuals vaccinated with the booster dose (median = 1 2601.3 AU/mL; 95% CI: 4127.5 to 19 089.1).

CONCLUSION

There was a significant increase in SARS-CoV-2 seroprevalence following the mass vaccination campaign in Portugal. It is important to continue to monitor the distribution of specific SARS-COV-2 antibody at the population level to further inform public health policies.

Optimizing two-dose vaccine resource allocation to combat a pandemic in the context of limited supply: The case of COVID-19

The adequate vaccination is a promising solution to mitigate the enormous socio-economic costs of the ongoing COVID-19 pandemic and allow us to return to normal pre-pandemic activity patterns. However, the vaccine supply shortage will be inevitable during the early stage of the vaccine rollout. Public health authorities face a crucial challenge in allocating scarce vaccines to maximize the benefits of vaccination. In this paper, we study a multi-period two-dose vaccine allocation problem when the vaccine supply is highly limited. To address this problem, we constructed a novel age-structured compartmental model to capture COVID-19 transmission and formulated as a nonlinear programming (NLP) model to minimize the total number of deaths in the population. In the NLP model, we explicitly take into account the two-dose vaccination procedure and several important epidemiologic features of COVID-19, such as pre-symptomatic and asymptomatic transmission, as well as group heterogeneity in susceptibility, symptom rates, severity, etc. We validated the applicability of the proposed model using a real case of the 2021 COVID-19 vaccination campaign in the Midlands of England. We conducted comparative studies to demonstrate the superiority of our method. Our numerical results show that prioritizing the allocation of vaccine resources to older age groups is a robust strategy to prevent more subsequent deaths. In addition, we show that releasing more vaccine doses for first-dose recipients could lead to a greater vaccination benefit than holding back second doses. We also find that it is necessary to maintain appropriate non-pharmaceutical interventions (NPIs) during the vaccination rollout, especially in low-resource settings. Furthermore, our analysis indicates that starting vaccination as soon as possible is able to markedly alleviate the epidemic impact when the vaccine resources are limited but are currently available. Our model provides an effective tool to assist policymakers in developing adaptive COVID-19 likewise vaccination strategies for better preparedness against future pandemic threats.

Effective screening strategies for safe opening of universities under Omicron and Delta variants of COVID-19

As new COVID-19 variants emerge, and disease and population characteristics change, screening strategies may also need to change. We develop a decision-making model that can assist a college to determine an optimal screening strategy based on their characteristics and resources, considering COVID-19 infections/hospitalizations/deaths; peak daily hospitalizations; and the tests required. We also use this tool to generate screening guidelines for the safe opening of college campuses. Our compartmental model simulates disease spread on a hypothetical college campus under co-circulating variants with different disease dynamics, considering: (i) the heterogeneity in disease transmission and outcomes for faculty/staff and students based on vaccination status and level of natural immunity; and (ii) variant- and dose-dependent vaccine efficacy. Using the Spring 2022 academic semester as a case study, we study routine screening strategies, and find that screening the faculty/staff less frequently than the students, and/or the boosted and vaccinated less frequently than the unvaccinated, may avert a higher number of infections per test, compared to universal screening of the entire population at a common frequency. We also discuss key policy issues, including the need to revisit the mitigation objective over time, effective strategies that are informed by booster coverage, and if and when screening alone can compensate for low booster coverage.

Altered pre-existing SARS-CoV-2-specific T cell responses in elderly individuals

Pre-existing SARS-CoV-2-specific T cells, but not antibodies, have been detected in some unexposed individuals. This may account for some of the diversity in clinical outcomes ranging from asymptomatic infection to severe COVID-19. Although age is a risk factor for COVID-19, how age affects SARS-CoV-2-specific T cell responses remains unknown. We found that pre-existing T cell responses to specific SARS-CoV-2 proteins, Spike (S) and Nucleoprotein (N), were significantly lower in elderly donors (>70 years old) than in young donors. However, substantial pre-existing T cell responses to the viral membrane (M) protein were detected in both young and elderly donors. In contrast, young and elderly donors exhibited comparable T cell responses to S, N, and M proteins after infection with SARS-CoV-2. These data suggest that although SARS-CoV-2 infection can induce T cell responses specific to various viral antigens regardless of age, diversity of target antigen repertoire for long-lived memory T cells specific for SARS-CoV-2 may decline with age; however, memory T cell responses can be maintained by T cells reactive to specific viral proteins such as M. A better understanding of the role of pre-existing SARS-CoV-2-specific T cells that are less susceptible to age-related loss may contribute to development of more effective vaccines for elderly people.

Measuring the impact of COVID-19 vaccination and immunity waning: A modelling study for Portugal

Vaccination strategies to control COVID-19 have been ongoing worldwide since the end of 2020. Understanding their possible effect is key to prevent future disease spread. Using a modelling approach, this study intends to measure the impact of the COVID-19 Portuguese vaccination strategy on the effective reproduction number and explore three scenarios for vaccine effectiveness waning. Namely, the no-immunity-loss, 1-year and 3-years of immunity duration scenarios. We adapted an age-structured SEIR deterministic model and used Portuguese hospitalisation data for the model calibration. Results show that, although the Portuguese vaccination plan had a substantial impact in reducing overall transmission, it might not be sufficient to control disease spread. A significant vaccination coverage of those above 5 years old, a vaccine effectiveness against disease of at least 80% and softer non-pharmaceutical interventions (NPIs), such as mask usage and social distancing, would be necessary to control disease spread in the worst scenario considered. The immunity duration scenario of 1-year displays a resurgence of COVID-19 hospitalisations by the end of 2021, the same is observed in 3-year scenario although with a lower magnitude. The no-immunity-loss scenario presents a low increase in hospitalisations. In both the 1-year and 3-year scenarios, a vaccination boost of those above 65 years old would result in a 53% and 38% peak reduction of non-ICU hospitalisations, respectively. These results suggest that NPIs should not be fully phased-out but instead be combined with a fast booster vaccination strategy to reduce healthcare burden.

A general framework for optimising cost-effectiveness of pandemic response under partial intervention measures

The COVID-19 pandemic created enormous public health and socioeconomic challenges. The health effects of vaccination and non-pharmaceutical interventions (NPIs) were often contrasted with significant social and economic costs. We describe a general framework aimed to derive adaptive cost-effective interventions, adequate for both recent and emerging pandemic threats. We also quantify the net health benefits and propose a reinforcement learning approach to optimise adaptive NPIs. The approach utilises an agent-based model simulating pandemic responses in Australia, and accounts for a heterogeneous population with variable levels of compliance fluctuating over time and across individuals. Our analysis shows that a significant net health benefit may be attained by adaptive NPIs formed by partial social distancing measures, coupled with moderate levels of the society's willingness to pay for health gains (health losses averted). We demonstrate that a socially acceptable balance between health effects and incurred economic costs is achievable over a long term, despite possible early setbacks.

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

INTRODUCTION

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

Evaluation of immunogenicity and reactogenicity of COVID-19 vaccines in pregnant women

OBJECTIVE

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in pregnancy is associated with increased risk of adverse maternal and perinatal outcomes. Vaccines are highly effective at preventing severe coronavirus disease 2019 (COVID-19), but there are limited data on COVID-19 vaccines in pregnancy. This study aimed to investigate the reactogenicity and immunogenicity of COVID-19 vaccines in pregnant women when administered according to the 12-week-interval dosing schedule recommended in the UK.

METHODS

This was a cohort study of pregnant women receiving COVID-19 vaccination between April and September 2021. The outcomes were immunogenicity and reactogenicity after COVID-19 vaccination. Pregnant women were recruited by phone, e-mail and/or text and were vaccinated according to vaccine availability at their local vaccination center. For immunogenicity assessment, blood samples were taken at specific timepoints after each dose to evaluate nucleocapsid protein (N) and spike protein (S) antibody titers. The comparator group comprised non-pregnant female healthcare workers in the same age group who were vaccinated as part of the national immunization program in a contemporaneous longitudinal cohort study. Longitudinal changes in serum antibody titers and association with pregnancy status were assessed using a two-step regression approach. Reactogenicity assessment in pregnant women was undertaken using an online questionnaire. The comparator group comprised non-pregnant women aged 18-49 years who had received two vaccine doses in primary care. The association of pregnancy status with reactogenicity was assessed using logistic regression analysis.

RESULTS

Overall, 67 pregnant women, of whom 66 had received a mRNA vaccine, and 79 non-pregnant women, of whom 50 had received a mRNA vaccine, were included in the immunogenicity study. Most (61.2%) pregnant women received their first vaccine dose in the third trimester, while 3.0% received it in the first trimester and 35.8% in the second trimester. SARS-CoV-2 S-antibody geometric mean concentrations after mRNA vaccination were not significantly different at 2-6 weeks after the first dose but were significantly lower at 2-6 weeks after the second dose in infection-naïve pregnant compared with non-pregnant women. In pregnant women, prior infection was associated with higher antibody levels at 2-6 weeks after the second vaccine dose. Reactogenicity analysis included 108 pregnant women and 116 non-pregnant women. After the first dose, tiredness and chills were reported less commonly in pregnant compared with non-pregnant women (P = 0.043 and P = 0.029, respectively). After the second dose, feeling generally unwell was reported less commonly (P = 0.046) in pregnant compared with non-pregnant women.

CONCLUSIONS

Using an extended 12-week interval between vaccine doses, antibody responses after two doses of mRNA COVID-19 vaccine were found to be lower in pregnant compared with non-pregnant women. Strong antibody responses were achieved after one dose in previously infected women, regardless of pregnancy status. Pregnant women reported fewer adverse events after both the first and second dose of vaccine. These findings should now be addressed in larger controlled studies. © 2022 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Two years of COVID-19 pandemic: Framework of health interventions in a Brazilian city

The COVID-19 pandemic and its effects on public health have urgently demanded effective health policies to avoid the spread of COVID-19. Thus, public administrators have implemented non-pharmacological and pharmacological interventions to mitigate the pandemic's impacts and strengthen health services. The aim of this ecological study is to describe the scenario of COVID-19 pandemic in a Brazilian city, during 2 years. This ecological study was carried out in Nova Friburgo, a Brazilian city, for 105 weeks (two years), from March 29, 2020 (week 1) to April 02, 2022 (week 105). Data on COVID-19 cases and COVID-19 deaths, occupation of COVID-19 exclusive beds in hospitals, community mobility, vaccination, government regulation on the opening of city establishments and city risk assessment were collected from public datasets. Four waves of COVID-19 cases and deaths were observed during this period. The first case occurred in week 1 and first death in week 3 of this study. The highest peaks of cases and deaths were observed during the third wave with 1,131 cases (week 54) and 47 deaths (week 55) and where the highest occupation of COVID-19 exclusive beds in local hospitals occurred. Interventions from more restrictive to more flexible, were implemented throughout this study, including lockdown and gradual return in economic and social strata levels. Vaccination began on week 43 and at the end of this study 89.91% of the total population was vaccinated with at least one dose, being 83.22% fully vaccinated. A deep description of several interventions used to avoid COVID-19 spread in a Brazilian city during 2 years of this pandemic can help promote better decision-making in the future while it exposes the challenges of conducting public health policies in a pandemic scenario.

Adjusted COVID-19 booster schedules balance age-dependent differences in antibody titers benefitting risk populations

We provide follow-up data on the humoral immune response after COVID-19 vaccinations of two distinct cohorts aged below 60 and over 80 years to screen for age-related differences in the longevity and magnitude of the induction of the antibody responses post booster-vaccinations. While anti-SARS-CoV-2 spike-specific IgG and neutralization capacity waned rapidly after the initial vaccination schedule, additional boosters highly benefitted the humoral immune responses especially in the elderly cohort, including the neutralization of Omikron variants. Thus, adjusted COVID-19 booster vaccination schedules are an appropriate tool to overcome limitations in the success of vaccinations.

A multicenter, double-blind, randomized, parallel-group, placebo-controlled study to evaluate the efficacy and safety of camostat mesilate in patients with COVID-19 (CANDLE study)

BACKGROUND

In vitro drug screening studies have indicated that camostat mesilate (FOY-305) may prevent SARS-CoV-2 infection into human airway epithelial cells. This study was conducted to investigate whether camostat mesilate is an effective treatment for SARS-CoV-2 infection (COVID-19).

METHODS

This was a multicenter, double-blind, randomized, parallel-group, placebo-controlled study. Patients were enrolled if they were admitted to a hospital within 5 days of onset of COVID-19 symptoms or within 5 days of a positive test for asymptomatic patients. Severe cases (e.g., those requiring oxygenation/ventilation) were excluded. Patients were enrolled, randomized, and allocated to each group using an interactive web response system. Randomization was performed using a minimization method with the factors medical institution, age, and underlying diseases (chronic respiratory disease, chronic kidney disease, diabetes mellitus, hypertension, cardiovascular diseases, and obesity). The patients, investigators/subinvestigators, study coordinators, and other study personnel were blinded throughout the study. Patients were administered camostat mesilate (600 mg qid; four to eight times higher than the clinical doses in Japan) or placebo for up to 14 days. The primary efficacy endpoint was the time to the first two consecutive negative tests for SARS-CoV-2.

RESULTS

One-hundred fifty-five patients were randomized to receive camostat mesilate (n = 78) or placebo (n = 77). The median time to the first test was 11.0 days (95% confidence interval [CI]: 9.0-12.0) in the camostat mesilate group and 11.0 days (95% CI: 10.0-13.0) in the placebo group. Conversion to negative viral status by day 14 was observed in 45 of 74 patients (60.8%) in the camostat mesilate group and 47 of 74 patients (63.5%) in the placebo group. The primary (Bayesian) and secondary (frequentist) analyses found no significant differences in the primary endpoint between the two groups. No additional safety concerns beyond those already known for camostat mesilate were identified.

CONCLUSIONS

Camostat mesilate did not substantially reduce the time to viral clearance, based on upper airway viral loads, compared with placebo for treating patients with mild to moderate SARS-CoV-2 infection with or without symptoms.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT04657497. Japan Registry for Clinical Trials, jRCT2031200198.

Association of COVID-19 vaccination before conception with maternal liver function during early pregnancy: a cohort study of 7745 Chinese pregnant women

Multicenter case series has reported patients with hepatic injury following COVID-19 vaccination, which raised concern for the possibility of vaccine-induced liver dysfunction. We aimed to assess the impact of COVID-19 vaccination on liver function of pregnant women, who may be uniquely susceptible to vaccine-induced liver dysfunction. We conducted a retrospective cohort study at a tertiary hospital in Shanghai, China. Vaccine administration was obtained from the electronic vaccination record. Serum levels of alanine transaminase (ALT), aspartate transaminase (AST), total bile acid (TBA) and total bilirubin (TBIL) in early pregnancy were determined by enzymatic methods. Among the 7745 included pregnant women, 3832 (49.5%) received at least two doses of an inactivated vaccine. COVID-19 vaccination was significantly associated with higher levels of maternal serum TBA. Compared with unvaccinated pregnant women, the mean TBA levels increased by 0.17 μmol/L (β = 0.17, 95% CI, 0.04, 0.31) for women who had been vaccinated within 3 months before the date of conception. Moreover, COVID-19 vaccination was significantly associated with an increased risk of maternal hyperbileacidemia. The risk of hyperbileacidemia increased by 113% (RR = 2.13; 95% CI, 1.17-3.87) for pregnant women who had been vaccinated within 3 months before conception compared with unvaccinated pregnant women. However, when the interval from complete vaccination to conception was prolonged to more than 3 months, COVID-19 vaccination was not associated with serum TBA levels or maternal hyperbileacidemia. Our findings suggest that vaccination with inactivated COVID-19 vaccines more than 3 months before conception have no detrimental effects on maternal liver function in early pregnancy.

Control of COVID-19 Outbreaks under Stochastic Community Dynamics, Bimodality, or Limited Vaccination

Reaching population immunity against COVID-19 is proving difficult even in countries with high vaccination levels. Thus, it is critical to identify limits of control and effective measures against future outbreaks. The effects of nonpharmaceutical interventions (NPIs) and vaccination strategies are analyzed with a detailed community-specific agent-based model (ABM). The authors demonstrate that the threshold for population immunity is not a unique number, but depends on the vaccination strategy. Prioritizing highly interactive people diminishes the risk for an infection wave, while prioritizing the elderly minimizes fatalities when vaccinations are low. Control over COVID-19 outbreaks requires adaptive combination of NPIs and targeted vaccination, exemplified for Germany for January-September 2021. Bimodality emerges from the heterogeneity and stochasticity of community-specific human-human interactions and infection networks, which can render the effects of limited NPIs uncertain. The authors' simulation platform can process and analyze dynamic COVID-19 epidemiological situations in diverse communities worldwide to predict pathways to population immunity even with limited vaccination.

Diagnostics of messenger ribonucleic acid (mRNA) severe acute respiratory syndrome-corona virus‑2 (SARS-CoV‑2) vaccination-associated myocarditis—A systematic review

Background

Objective

Methods

Results

Conclusion

Supplementary Information

Senna makki and other active phytochemicals: Myths and realities behind covid19 therapeutic interventions

This study aims to investigate the binding potential of chemical compounds of Senna in comparison with the experimentally tested active phytochemicals against SARS-CoV-2 protein targets to assist in prevention of infection by exploring multiple treatment options. The entire set of phytochemicals from both the groups were subjected to advanced computational analysis that explored functional molecular descriptors from a set of known medicinal-based active therapeutics followed by MD simulations on multiple SARS-CoV-2 target proteins. Our findings manifest the importance of hydrophobic substituents in chemical structures of potential inhibitors through cross-validation with the FDA-approved anti-3CL^pro drugs. Noteworthy improvement in end-point binding free energies and pharmacokinetic profiles of the proposed compounds was perceived in comparison to the control drug, vizimpro. Moreover, the identification of common drug targets namely; AKT1, PTGS1, TNF, and DPP4 between proposed active phytochemicals and Covid19 using network pharmacological analysis further substantiate the importance of medicinal scaffolds. The structural dynamics and binding affinities of phytochemical compounds xanthoangelol_E, hesperetin, and beta-sitosterol reported as highly potential against 3CL^pro in cell-based and cell-free assays are consistent with the computational analysis. Whereas, the secondary metabolites such as sennosides A, B, C, D present in higher amount in Senna exhibited weak binding affinity and instability against the spike protein, helicase nsp13, RdRp nsp12, and 3CL^pro. In conclusion, the results contravene fallacious efficacy claims of Senna tea interventions circulating on electronic/social media as Covid19 cure; thus emphasizing the importance of well-examined standardized data of the natural products in hand; thereby preventing unnecessary deaths under pandemic hit situations worldwide.

Disease spreading modeling and analysis: a survey

MOTIVATION

The control of the diffusion of diseases is a critical subject of a broad research area, which involves both clinical and political aspects. It makes wide use of computational tools, such as ordinary differential equations, stochastic simulation frameworks and graph theory, and interaction data, from molecular to social granularity levels, to model the ways diseases arise and spread. The coronavirus disease 2019 (COVID-19) is a perfect testbench example to show how these models may help avoid severe lockdown by suggesting, for instance, the best strategies of vaccine prioritization.

RESULTS

Here, we focus on and discuss some graph-based epidemiological models and show how their use may significantly improve the disease spreading control. We offer some examples related to the recent COVID-19 pandemic and discuss how to generalize them to other diseases.