Adverse Effects of COVID-19 Vaccination: Machine Learning and Statistical Approach to Identify and Classify Incidences of Morbidity and Postvaccination Reactogenicity

COVID-19 vaccinations and their side effects: a scoping systematic review

Introduction: The COVID-19 virus has impacted people worldwide, causing significant changes in their lifestyles. Since the emergence of the epidemic, attempts have begun to prepare a vaccine that can eliminate the virus and restore balance to life in the entire world. Over the past two years, countries and specialized companies have competed to obtain a license from the World Health Organization for the vaccines that were discovered. After the appearance of vaccines in the health community, comparisons and fears of their side effects began, but people don't get an answer to the question of which is the best vaccine. Methods: IEEE Xplore, ScienceDirect, the New England Journal of Medicine, Google Scholar, and PubMed databases were searched for literature on the COVID-19 vaccine and its side effects. we surveyed the literature on the COVID-19 vaccine's side effects and the sorts of side effects observed after vaccination. Depending on data from the literature, we compared these vaccines in terms of side effects, then we analyzed the gaps and obstacles of previous studies and made proposals to process these gaps in future studies. Results: Overall, 17 studies were included in this scoping systematic review as they fulfilled the criteria specified, the majority of which were cross-sectional and retrospective cross-sectional studies. Most of the side effects were mild, self-limiting, and common. Thus, they usually resolve within 1-3 days after vaccination. Factors associated with higher side effects included advanced age, allergic conditions, those taking other medications (particularly immunosuppressive ones), those with a history of type II diabetes, heart disease, hypertension, COVID-19 infection, and female sex. Our meta-analyses also found that mRNA vaccines looked to be more effective, while inactivated vaccinations had fewer side effects. Conclusion: This review shows that the COVID-19 vaccine is safe to administer and induces protection.

Exploring post-COVID-19 health effects and features with advanced machine learning techniques

COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recovered, many experience negative health effects, particularly influenced by demographic factors such as gender and age, as well as physiological and neurological factors like sleep patterns, emotional states, anxiety, and memory. This research aims to explore various health factors affecting different demographic profiles and establish significant correlations among physiological and neurological factors in the post-COVID-19 state. To achieve these objectives, we have identified the post-COVID-19 health factors and based on these factors survey data were collected from COVID-recovered patients in Bangladesh. Employing diverse machine learning algorithms, we utilised the best prediction model for post-COVID-19 factors. Initial findings from statistical analysis were further validated using Chi-square to demonstrate significant relationships among these elements. Additionally, Pearson's coefficient was utilized to indicate positive or negative associations among various physiological and neurological factors in the post-COVID-19 state. Finally, we determined the most effective machine learning model and identified key features using analytical methods such as the Gini Index, Feature Coefficients, Information Gain, and SHAP Value Assessment. And found that the Decision Tree model excelled in identifying crucial features while predicting the extent of post-COVID-19 impact.

Computational design and evaluation of mRNA- and protein-based conjugate vaccines for influenza A and SARS-CoV-2 viruses

BACKGROUND

Israel confirmed the first case of "flurona"-a co-infection of seasonal flu (IAV) and SARS-CoV-2 in an unvaccinated pregnant woman. This twindemic has been confirmed in multiple countries and underscores the importance of managing respiratory viral illnesses.

RESULTS

The novel conjugate vaccine was designed by joining four hemagglutinin, three neuraminidase, and four S protein of B-cell epitopes, two hemagglutinin, three neuraminidase, and four S proteins of MHC-I epitopes, and three hemagglutinin, nine neuraminidase, and five S proteins of MHC-II epitopes with linkers and adjuvants. The constructed conjugate vaccine was found stable, non-toxic, non-allergic, and antigenic with 0.6466 scores. The vaccine contained 14.87% alpha helix, 29.85% extended strand, 9.64% beta-turn, and 45.64% random coil, which was modeled to a 3D structure with 94.7% residues in the most favored region of the Ramachandran plot and Z-score of -3.33. The molecular docking of the vaccine with TLR3 represented -1513.9 kcal/mol of binding energy with 39 hydrogen bonds and 514 non-bonded contacts, and 1.582925e-07 of eigenvalue complex. Immune stimulation prediction showed the conjugate vaccine could activate T and B lymphocytes to produce high levels of Th1 cytokines and antibodies.

CONCLUSION

The in silico-designed vaccine against IAV and SARS-CoV-2 showed good population coverage and immune response with predicted T- and B-cell epitopes, favorable molecular docking, Ramachandran plot results, and good protein expression. It fulfilled safety criteria, indicating potential for preclinical studies and experimental clinical trials.

Overview of the cardio-metabolic impact of the COVID-19 pandemic

Evidence has shown that cardiometabolic disorders (CMDs) are amongst the top contributors to COVID-19 infection morbidity and mortality. The reciprocal impact of COVID-19 infection and the most common CMDs, the risk factors for poor composite outcome among patients with one or several underlying diseases, the effect of common medical management on CMDs and their safety in the context of acute COVID-19 infection are reviewed. Later on, the changes brought by the COVID-19 pandemic quarantine on the general population's lifestyle (diet, exercise patterns) and metabolic health, acute cardiac complications of different COVID-19 vaccines and the effect of CMDs on the vaccine efficacy are discussed. Our review identified that the incidence of COVID-19 infection is higher among patients with underlying CMDs such as hypertension, diabetes, obesity and cardiovascular disease. Also, CMDs increase the risk of COVID-19 infection progression to severe disease phenotypes (e.g. hospital and/or ICU admission, use of mechanical ventilation). Lifestyle modification during COVID-19 era had a great impact on inducing and worsening of CMDs. Finally, the lower efficacy of COVID-19 vaccines was found in patients with metabolic disease.

Detecting the most critical clinical variables of COVID-19 breakthrough infection in vaccinated persons using machine learning

Background

COVID-19 vaccines offer different levels of immune protection but do not provide 100% protection. Vaccinated persons with pre-existing comorbidities may be at an increased risk of SARS-CoV-2 breakthrough infection or reinfection. The aim of this study is to identify the critical variables associated with a higher probability of SARS-CoV-2 breakthrough infection using machine learning.

Methods

A dataset comprising symptoms and feedback from 257 persons, of whom 203 were vaccinated and 54 unvaccinated, was used for the investigation. Three machine learning algorithms - Deep Multilayer Perceptron (Deep MLP), XGBoost, and Logistic Regression - were trained with the original (imbalanced) dataset and the balanced dataset created by using the Random Oversampling Technique (ROT), and the Synthetic Minority Oversampling Technique (SMOTE). We compared the performance of the classification algorithms when the features highly correlated with breakthrough infection were used and when all features in the dataset were used.

Result

The results show that when highly correlated features were considered as predictors, with Random Oversampling to address data imbalance, the XGBoost classifier has the best performance (F1 = 0.96; accuracy = 0.96; AUC = 0.98; G-Mean = 0.98; MCC = 0.88). The Deep MLP had the second best performance (F1 = 0.94; accuracy = 0.94; AUC = 0.92; G-Mean = 0.70; MCC = 0.42), while Logistic Regression had less accurate performance (F1 = 0.89; accuracy = 0.88; AUC = 0.89; G-Mean = 0.89; MCC = 0.68). We also used Shapley Additive Explanations (SHAP) to investigate the interpretability of the models. We found that body temperature, total cholesterol, glucose level, blood pressure, waist circumference, body weight, body mass index (BMI), haemoglobin level, and physical activity per week are the most critical variables indicating a higher risk of breakthrough infection.

Conclusion

These results, evident from our unique data source derived from apparently healthy volunteers with cardiovascular risk factors, follow the expected pattern of positive or negative correlations previously reported in the literature. This information strengthens the body of knowledge currently applied in public health guidelines and may also be used by medical practitioners in the future to reduce the risk of SARS-CoV-2 breakthrough infection.

[Neurological complications after covid-19 vaccination]

The aim of our work was to study the relevance and incidence of neurological post-vaccination complications during the COVID-19 pandemic. Based on the results of a systematic literature search of several databases, the current review describes the diagnosed complications, including neurological, that occurred after the administration of the COVID-19 vaccine during the pandemic period. To fully establish the pathophysiological mechanisms of the development of a causal relationship of neurological complications with vaccines against COVID-19, it becomes necessary to continue long-term studies. This will make it possible to carry out a pharmacological correction of the quality of vaccine safety.

Covid-19 vaccination reported side effects and hesitancy among the Syrian population: a cross-sectional study

INTRODUCTION

Studying post-vaccination side effects and identifying the reasons behind low vaccine uptake are pivotal for overcoming the pandemic.

METHODS

This cross-sectional study was distributed through social media platforms and face-to-face interviews. Data from vaccinated and unvaccinated participants were collected and analyzed using the chi-square test, multivariable logistic regression to detect factors associated with side effects and severe side effects.

RESULTS

Of the 3509 participants included, 1672(47.6%) were vaccinated. The most common reason for not taking the vaccine was concerns about the vaccine's side effects 815(44.4). The majority of symptoms were mild 788(47.1%), followed by moderate 374(22.3%), and severe 144(8.6%). The most common symptoms were tiredness 1028(61.5%), pain at the injection site 933(55.8%), and low-grade fever 684(40.9%). Multivariable logistic regression analysis revealed that <40 years (vs. ≥40; OR: 2.113, p-value = 0.008), females (vs. males; OR: 2.245, p-value< .001), did not receive influenza shot last year (vs. did receive Influenza shot last year OR: 1.697, p-value = 0.041), AstraZeneca (vs. other vaccine brands; OR: 2.799, p-value< .001), co-morbidities (vs. no co-morbidities; OR: 1.993, p-value = 0.008), and diabetes mellitus (vs. no diabetes mellitus; OR: 2.788, p-value = 0.007) were associated with severe post-vaccine side effects. Serious side effects reported were blood clots 5(0.3%), thrombocytopenia 2(0.1%), anaphylaxis 1(0.1%), seizures 1(0.1%), and cardiac infarction 1(0.1%).

CONCLUSION

Our study revealed that most side effects reported were mild in severity and self-limiting. Increasing the public's awareness of the nature of the vaccine's side effects would reduce the misinformation and improve the public's trust in vaccines. Larger studies to evaluate rare and serious adverse events and long-term side effects are needed, so people can have sufficient information and understanding before making an informed consent which is essential for vaccination.