Parkinson's disease diagnosis using deep learning: A bibliometric analysis and literature review.

Parkinson's Disease (PD) is a progressive neurodegenerative illness triggered by decreased dopamine secretion. Deep Learning (DL) has gained substantial attention in PD diagnosis research, with an increase in the number of published papers in this discipline. PD detection using DL has presented more promising outcomes as compared with common machine learning approaches. This article aims to conduct a bibliometric analysis and a literature review focusing on the prominent developments taking place in this area. To achieve the target of the study, we retrieved and analyzed the available research papers in the Scopus database. Following that, we conducted a bibliometric analysis to inspect the structure of keywords, authors, and countries in the surveyed studies by providing visual representations of the bibliometric data using VOSviewer software. The study also provides an in-depth review of the literature focusing on different indicators of PD, deployed approaches, and performance metrics. The outcomes indicate the firm development of PD diagnosis using DL approaches over time and a large diversity of studies worldwide. Additionally, the literature review presented a research gap in DL approaches related to incremental learning, particularly in relation to big data analysis.

A combined method of optimized learning vector quantization and neuro-fuzzy techniques for predicting unified Parkinson's disease rating scale using vocal features.

Parkinson's Disease (PD) is a common disorder of the central nervous system. The Unified Parkinson's Disease Rating Scale or UPDRS is commonly used to track PD symptom progression because it displays the presence and severity of symptoms. To model the relationship between speech signal properties and UPDRS scores, this study develops a new method using Neuro-Fuzzy (ANFIS) and Optimized Learning Rate Learning Vector Quantization (OLVQ1). ANFIS is developed for different Membership Functions (MFs). The method is evaluated using Parkinson's telemonitoring dataset which includes a total of 5875 voice recordings from 42 individuals in the early stages of PD which comprises 28 men and 14 women. The dataset is comprised of 16 vocal features and Motor-UPDRS, and Total-UPDRS. The method is compared with other learning techniques. The results show that OLVQ1 combined with the ANFIS has provided the best results in predicting Motor-UPDRS and Total-UPDRS. The lowest Root Mean Square Error (RMSE) values (UPDRS (Total)=0.5732; UPDRS (Motor)=0.5645) and highest R-squared values (UPDRS (Total)=0.9876; UPDRS (Motor)=0.9911) are obtained by this method. The results are discussed and directions for future studies are presented.i.ANFIS and OLVQ1 are combined to predict UPDRS.ii.OLVQ1 is used for PD data segmentation.iii.ANFIS is developed for different MFs to predict Motor-UPDRS and Total-UPDRS.

Evaluating COVID-19 infection prevention measures in Malaysia: A fuzzy DEMATEL approach.

Objective: Since unexpected COVID-19 has been causing massive losses worldwide, preventive measures have been emergency provided to curb the expansion of the epidemic and cut off transmission routes. However, there is a lack of studies that comprehensively address COVID-19 infection prevention measures. This aims to provide a comprehensive evaluation framework to identify the factors impacting COVID-19 infection prevention. Meanwhile, categorizing factors into individual, social, environmental, and technological dimensions and uncovering their interrelationships and level of importance are indeed novelties of this study. Methods: An integration of fuzzy logic and decision-making trial and evaluation laboratory (DEMATEL) is utilized, and data was collected from a panel of professional experts in Malaysia. Using a cause-effect relationship diagram, the fuzzy DEMATEL method evaluates the causal relationships between factors. Results: Findings showed that environmental factors play the most significant roles in preventing COVID-19 infection, followed by technology, individual, and social factors. Getting vaccinated is the most crucial factor in the environmental dimension in cutting the spread of COVID-19. Telehealth, the use of personal protective equipments (PPEs), and the adoption of social distancing are the most important measures in technology, individual and social dimensions, respectively. Conclusions: This study offered valuable insights for policymakers and healthcare professionals in designing and implementing effective strategies to prevent pandemic disease transmission. Findings can be practically applied to optimize and prioritize infection prevention measures, assign resources more effectively, and guide evidence-based decision-making in the face of evolving pandemic situations. This process involves the active commitment of all parties, including governments, medical health executives, and citizens.

The nexus between quality of customer relationship management systems and customers' satisfaction: Evidence from online customers' reviews.

Customer Relationship Management (CRM) is a method of management that aims to establish, develop, and improve relationships with targeted customers in order to maximize corporate profitability and customer value. There have been many CRM systems in the market. These systems are developed based on the combination of business requirements, customer needs, and industry best practices. The impact of CRM systems on the customers' satisfaction and competitive advantages as well as tangible and intangible benefits are widely investigated in the previous studies. However, there is a lack of studies to assess the quality dimensions of these systems to meet an organization's CRM strategy. This study aims to investigate customers' satisfaction with CRM systems through online reviews. We collected 5172 online customers' reviews from 8 CRM systems in the Google play store platform. The satisfaction factors were extracted using Latent Dirichlet Allocation (LDA) and grouped into three dimensions; information quality, system quality, and service quality. Data segmentation is performed using Learning Vector Quantization (LVQ). In addition, feature selection is performed by the entropy-weight approach. We then used the Adaptive Neuro Fuzzy Inference System (ANFIS), the hybrid of fuzzy logic and neural networks, to assess the relationship between these dimensions and customer satisfaction. The results are discussed and research implications are provided.

Parkinson's Disease Diagnosis Using Laplacian Score, Gaussian Process Regression and Self-Organizing Maps.

Parkinson's disease (PD) is a complex degenerative brain disease that affects nerve cells in the brain responsible for body movement. Machine learning is widely used to track the progression of PD in its early stages by predicting unified Parkinson's disease rating scale (UPDRS) scores. In this paper, we aim to develop a new method for PD diagnosis with the aid of supervised and unsupervised learning techniques. Our method is developed using the Laplacian score, Gaussian process regression (GPR) and self-organizing maps (SOM). SOM is used to segment the data to handle large PD datasets. The models are then constructed using GPR for the prediction of the UPDRS scores. To select the important features in the PD dataset, we use the Laplacian score in the method. We evaluate the developed approach on a PD dataset including a set of speech signals. The method was evaluated through root-mean-square error (RMSE) and adjusted R-squared (adjusted R²). Our findings reveal that the proposed method is efficient in the prediction of UPDRS scores through a set of speech signals (dysphonia measures). The method evaluation showed that SOM combined with the Laplacian score and Gaussian process regression with the exponential kernel provides the best results for R-squared (Motor-UPDRS = 0.9489; Total-UPDRS = 0.9516) and RMSE (Motor-UPDRS = 0.5144; Total-UPDRS = 0.5105) in predicting UPDRS compared with the other kernels in Gaussian process regression.

A Combined Method for Diabetes Mellitus Diagnosis Using Deep Learning, Singular Value Decomposition, and Self-Organizing Map Approaches.

Diabetes in humans is a rapidly expanding chronic disease and a major crisis in modern societies. The classification of diabetics is a challenging and important procedure that allows the interpretation of diabetic data and diagnosis. Missing values in datasets can impact the prediction accuracy of the methods for the diagnosis. Due to this, a variety of machine learning techniques has been studied in the past. This research has developed a new method using machine learning techniques for diabetes risk prediction. The method was developed through the use of clustering and prediction learning techniques. The method uses Singular Value Decomposition for missing value predictions, a Self-Organizing Map for clustering the data, STEPDISC for feature selection, and an ensemble of Deep Belief Network classifiers for diabetes mellitus prediction. The performance of the proposed method is compared with the previous prediction methods developed by machine learning techniques. The results reveal that the deployed method can accurately predict diabetes mellitus for a set of real-world datasets.

Vulnerability of Parkinson's Patients to COVID-19 and Its Consequences and Effects on Them: A Systematic Review.

Introduction: Parkinson's disease (PD) is the second most common neurological disorder. Patients with PD were affected by the COVID-19 pandemic in many different ways. This study's principal purpose is to assess PD patients' vulnerability to COVID-19 and its consequences. Method: This systematic review was performed based on Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) guidelines. A thorough search was conducted in the Medline (through PubMed) and Scopus databases from inception to January 30, 2022. The Joanna Briggs Institute (JBI) critical appraisal checklist was used to evaluate the studies. Results: Most of the studies (38%) had been conducted in Italy. Of the total number of studies, 17 (58%) were cross-sectional, seven (22%) were cohort, four (12%) were quasiexperimental, two (6%) were case-control, and one (3%) was a qualitative study. The PD duration in patients ranged from 3.26 to 13.40 years (IQR1: 5.7 yrs., median: 3.688 yrs., and IQR3: 8.815 yrs.). Meanwhile, the sample size ranged from 12 to 30872 participants (IQR1: 46, median: 96, and IQR3: 211). Despite worsening PD symptoms in the targeted population (persons with COVID-19 and Parkinson's disease), some studies found PD to be a risk factor for more severe COVID-19 disease. There are many adverse effects during the pandemic period in PD patients such as abnormalities of motor, nonmotor functioning, clinical outcomes, activities of daily living, and other outcomes. Conclusion: This study confirmed the negative effect of the COVID-19 pandemic on health-related quality of life and its determinants in patients with PD and their caregivers. Thus, due to the worsening symptoms of PD patients in the current pandemic, these people should be given more care and supervision to minimize their coronavirus exposure.

Electroencephalography (EEG) eye state classification using learning vector quantization and bagged trees.

The analysis of Electroencephalography (EEG) signals has been an effective way of eye state identification. Its significance is highlighted by studies that examined the classification of eye states using machine learning techniques. In previous studies, supervised learning techniques have been widely used in EEG signals analysis for eye state classification. Their main goal has been the improvement of classification accuracy through the use of novel algorithms. The trade-off between classification accuracy and computation complexity is an important task in EEG signals analysis. In this paper, a hybrid method that can handle multivariate signals and non-linear is proposed with supervised and un-supervised learning to achieve a fast EEG eye state classification with high prediction accuracy to provide real-time decision-making applicability. We use the Learning Vector Quantization (LVQ) technique and bagged tree techniques. The method was evaluated on a real-world EEG dataset which included 14976 instances after the removal of outlier instances. Using LVQ, 8 clusters were generated from the data. The bagged tree was applied on 8 clusters and compared with other classifiers. Our experiments revealed that LVQ combined with the bagged tree provides the best results (Accuracy = 0.9431) compared with the bagged tree, CART (Classification And Regression Tree) (Accuracy = 0.8200), LDA (Linear Discriminant Analysis) (Accuracy = 0.7931), Random Trees (Accuracy = 0.8311), Naïve Bayes (Accuracy = 0.8331) and Multilayer Perceptron (Accuracy = 0.7718), which demonstrates the effectiveness of incorporating ensemble learning and clustering approaches in the analysis of EEG signals. We also provided the time complexity of the methods for prediction speed (Observation/Second). The result showed that LVQ + Bagged Tree provides the best result for prediction speed (58942 Obs/Sec) in relation to Bagged Tree (28453 Obs/Sec), CART (27784 Obs/Sec), LDA (26435 Obs/Sec), Random Trees (27921), Naïve Bayes (27217) and Multilayer Perceptron (24163).

COVID-19 and sustainable development goals: A bibliometric analysis and SWOT analysis in Malaysian context.

The COVID-19 crisis has been a core threat to the lives of billions of individuals over the world. The COVID-19 crisis has influenced governments' aims to meet UN Sustainable Development Goals (SDGs); leading to exceptional conditions of fragility, poverty, job loss, and hunger all over the world. This study aims to investigate the current studies that concentrate on the COVID-19 crisis and its implications on SDGs using a bibliometric analysis approach. The study also deployed the Strengths, Weaknesses, Opportunities, and Threats (SWOT) approach to perform a systematic analysis of the SDGs, with an emphasis on the COVID-19 crisis impact on Malaysia. The results of the study indicated the unprecedented obstacles faced by countries to meet the UN's SDGs in terms of implementation, coordination, trade-off decisions, and regional issues. The study also stressed the impact of COVID-19 on the implementation of the SDGs focusing on the income, education, and health aspects. The outcomes highlighted the emerging opportunities of the crisis that include an improvement in the health sector, the adoption of online modes in education, the swift digital transformation, and the global focus on environmental issues. Our study demonstrated that, in the post-crisis time, the ratio of citizens in poverty could grow up more than the current national stated values. We stressed the need to design an international agreement to reconsider the implementation of SDGs, among which, are strategic schemes to identify vital and appropriate policies.

Early diagnosis of Parkinson's disease: A combined method using deep learning and neuro-fuzzy techniques.

Predicting Unified Parkinson's Disease Rating Scale (UPDRS) in Total- UPDRS and Motor-UPDRS clinical scales is an important part of controlling PD. Computational intelligence approaches have been used effectively in the early diagnosis of PD by predicting UPDRS. In this research, we target to present a combined approach for PD diagnosis using an ensemble learning approach with the ability of online learning from clinical large datasets. The method is developed using Deep Belief Network (DBN) and Neuro-Fuzzy approaches. A clustering approach, Expectation-Maximization (EM), is used to handle large datasets. The Principle Component Analysis (PCA) technique is employed for noise removal from the data. The UPDRS prediction models are constructed for PD diagnosis. To handle the missing data, K-NN is used in the proposed method. We use incremental machine learning approaches to improve the efficiency of the proposed method. We assess our approach on a real-world PD dataset and the findings are assessed compared to other PD diagnosis approaches developed by machine learning techniques. The findings revealed that the approach can improve the UPDRS prediction accuracy and the time complexity of previous methods in handling large datasets.

A Hybrid MCDM Approach Based on Fuzzy-Logic and DEMATEL to Evaluate Adult Obesity.

Obesity and its complications is one of the main issues in today's world and is increasing rapidly. A wide range of non-contagious diseases, for instance, diabetes type 2, cardiovascular, high blood pressure and stroke, numerous types of cancer, and mental health issues are formed following obesity. According to the WHO, Malaysia is the sixth Asian country with an adult population suffering from obesity. Therefore, identifying risk factors associated with obesity among Malaysian adults is necessary. For this purpose, this study strives to investigate and assess the risk factors related to obesity and overweight in this country. A quantitative approach was employed by surveying 26 healthcare professionals by questionnaire. Collected data were analyzed with the DEMATEL and Fuzzy Rule-Based methods. We found that lack of physical activity, insufficient sleep, unhealthy diet, genetics, and perceived stress were the most significant risk factors for obesity.

What is the impact of eWOM in social network sites on travel decision-making during the COVID-19 outbreak? A two-stage methodology.

Social media users share a variety of information and experiences and create Electronic Word of Mouth (eWOM) in the form of positive or negative opinions to communicate with others. In the context of the COVID-19 outbreak, eWOM has been an effective tool for knowledge sharing and decision making. This research aims to reveal what factors of eWOM can influence travelers' trust in their decision-making to travel during the COVID-19 outbreak. In addition, we aim to find the relationships between trust in eWOM and perceived risk, and perceived risk and the decision to travel. These relationships are investigated based on online customers' reviews in TripAdvisor's COVID-19 forums. We use a two-stage data analysis which includes cluster analysis and structural equation modeling. In the first stage, a questionnaire survey was designed and the data was collected from 1546 respondents by referring to the COVID-19 forums on TripAdvisor. Specifically, we use k-means to segment the users' data into different groups. In the second stage, Structural Equation Modeling (SEM) was performed to inspect the relations between the variables in the hypothesized research model using a subsample of 679 respondents. The results of the first stage of the analysis showed that three segments could be discovered from the collected data for trust based on eWOM source and eWOM message attributes. These segments clearly showed that there are significant relationships between trust and perceived risk, and between perceived risk and the decision to travel. The results in all segments showed that users with a low level of trust have a high level of perceived risk and a low level of intention to travel during the COVID-19 outbreak. In addition, it was found that users with a high level of e-trust have a low level of perceived risk and a high level of intention to travel. These results were confirmed in all segments and these relationships were confirmed by SEM. The results of SEM revealed that visual and external information moderated the relationship between eWOM length and trust, and experience moderated the relationship between trust and perceived risk. For the moderating role of gender, it was found that the perceived risk has a higher impact on the decision to travel in the female sample.

Customer satisfaction with Restaurants Service Quality during COVID-19 outbreak: A two-stage methodology.

Online reviews have been used effectively to understand customers' satisfaction and preferences. COVID-19 crisis has significantly impacted customers' satisfaction in several sectors such as tourism and hospitality. Although several research studies have been carried out to analyze consumers' satisfaction using survey-based methodologies, consumers' satisfaction has not been well explored in the event of the COVID-19 crisis, especially using available data in social network sites. In this research, we aim to explore consumers' satisfaction and preferences of restaurants' services during the COVID-19 crisis. Furthermore, we investigate the moderating impact of COVID-19 safety precautions on restaurants' quality dimensions and satisfaction. We applied a new approach to achieve the objectives of this research. We first developed a hybrid approach using clustering, supervised learning, and text mining techniques. Learning Vector Quantization (LVQ) was used to cluster customers' preferences. To predict travelers' preferences, decision trees were applied to each segment of LVQ. We used a text mining technique; Latent Dirichlet Allocation (LDA), for textual data analysis to discover the satisfaction criteria from online customers' reviews. After analyzing the data using machine learning techniques, a theoretical model was developed to inspect the relationships between the restaurants' quality factors and customers' satisfaction. In this stage, Partial Least Squares (PLS) technique was employed. We evaluated the proposed approach using a dataset collected from the TripAdvisor platform. The outcomes of the two-stage methodology were discussed and future research directions were suggested according to the limitations of this study.

Impacts of the COVID-19 pandemic on household food waste behaviour: A systematic review.

Food waste has adverse economic, social, and environmental impacts and increases the prevalence of food insecurity. Panic buying at the beginning of the COVID-19 outbreak raised serious concerns about a potential rise in food waste levels and higher pressure on waste management systems. This article aims to investigate the impact of COVID-19 on food waste behaviour and the extent to which it occurs using the systematic review method. A total of 38 articles were identified and reviewed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The findings showed that the COVID-19 pandemic led to reductions in household food waste in most countries. Several changes in shopping and cooking behaviours, food consumption, and managing inventory and leftovers have occurred due to COVID-19. Based on these insights, we predicted that some desirable food-management habits would be retained, and others would roll back in the post-COVID-19 world. The review contributes to the food waste literature by offering a comprehensive overview of behavioural changes during the COVID-19 pandemic and future research directions.

Predicting Parkinson's Disease Progression: Evaluation of Ensemble Methods in Machine Learning.

Parkinson's disease (PD) is a complex neurodegenerative disease. Accurate diagnosis of this disease in the early stages is crucial for its initial treatment. This paper aims to present a comparative study on the methods developed by machine learning techniques in PD diagnosis. We rely on clustering and prediction learning approaches to perform the comparative study. Specifically, we use different clustering techniques for PD data clustering and support vector regression ensembles to predict Motor-UPDRS and Total-UPDRS. The results are then compared with the other prediction learning approaches, multiple linear regression, neurofuzzy, and support vector regression techniques. The comparative study is performed on a real-world PD dataset. The prediction results of data analysis on a PD real-world dataset revealed that expectation-maximization with the aid of SVR ensembles can provide better prediction accuracy in relation to decision trees, deep belief network, neurofuzzy, and support vector regression combined with other clustering techniques in the prediction of Motor-UPDRS and Total-UPDRS.

Medical image processing and COVID-19: A literature review and bibliometric analysis.

COVID-19 crisis has placed medical systems over the world under unprecedented and growing pressure. Medical imaging processing can help in the diagnosis, treatment, and early detection of diseases. It has been considered as one of the modern technologies applied to fight against the COVID-19 crisis. Although several artificial intelligence, machine learning, and deep learning techniques have been deployed in medical image processing in the context of COVID-19 disease, there is a lack of research considering systematic literature review and categorization of published studies in this field. A systematic review locates, assesses, and interprets research outcomes to address a predetermined research goal to present evidence-based practical and theoretical insights. The main goal of this study is to present a literature review of the deployed methods of medical image processing in the context of the COVID-19 crisis. With this in mind, the studies available in reliable databases were retrieved, studied, evaluated, and synthesized. Based on the in-depth review of literature, this study structured a conceptual map that outlined three multi-layered folds: data gathering and description, main steps of image processing, and evaluation metrics. The main research themes were elaborated in each fold, allowing the authors to recommend upcoming research paths for scholars. The outcomes of this review highlighted that several methods have been adopted to classify the images related to the diagnosis and detection of COVID-19. The adopted methods have presented promising outcomes in terms of accuracy, cost, and detection speed.

Recommendation agents and information sharing through social media for coronavirus outbreak.

The novel outbreak of coronavirus disease (COVID-19) was an unexpected event for tourism in the world as well as tourism in the Netherlands. In this situation, the travelers' decision-making for tourism destinations was heavily affected by this global event. Social media usage has played an essential role in travelers' decision-making and increased the awareness of travel-related risks from the COVID-19 outbreak. Online consumer media for the outbreak of COVID-19 has been a crucial source of information for travelers. In the current situation, tourists are using electronic word of mouth (eWOM) more and more for travel planning. Opinions provided by peer travelers for the outbreak of COVID-19 tend to reduce the possibility of poor decisions. Nevertheless, the increasing number of reviews per experience makes reading all feedback hard to make an informed decision. Accordingly, recommendation agents developed by machine learning techniques can be effective in the analysis of such social big data for the identification of useful patterns from the data, knowledge discovery, and real-time service recommendations. The current research aims to adopt a framework for the recommendation agents through topic modeling to uncover the most important dimensions of COVID-19 reviews in the Netherland forums in TripAdvisor. This study demonstrates how social networking websites and online reviews can be effective in unexpected events for travelers' decision making. We conclude with the implications of our study for future research and practice.

What is the impact of service quality on customers' satisfaction during COVID-19 outbreak? New findings from online reviews analysis.

The COVID-19 pandemic has caused major global changes both in the areas of healthcare and economics. This pandemic has led, mainly due to conditions related to confinement, to major changes in consumer habits and behaviors. Although there have been several studies on the analysis of customers' satisfaction through survey-based and online customers' reviews, the impact of COVID-19 on customers' satisfaction has not been investigated so far. It is important to investigate dimensions of satisfaction from the online customers' reviews to reveal their preferences on the hotels' services during the COVID-19 outbreak. This study aims to reveal the travelers' satisfaction in Malaysian hotels during the COVID-19 outbreak through online customers' reviews. In addition, this study investigates whether service quality during COVID-19 has an impact on hotel performance criteria and consequently customers' satisfaction. Accordingly, we develop a new method through machine learning approaches. The method is developed using text mining, clustering, and prediction learning techniques. We use Latent Dirichlet Allocation (LDA) for big data analysis to identify the voice-of-the-customer, Expectation-Maximization (EM) for clustering, and ANFIS for satisfaction level prediction. In addition, we use Higher-Order Singular Value Decomposition (HOSVD) for missing value imputation. The data was collected from TripAdvisor regarding the travelers' concerns in the form of online reviews on the COVID-19 outbreak and numerical ratings on hotel services from different perspectives. The results from the analysis of online customers' reviews revealed that service quality during COVID-19 has an impact on hotel performance criteria and consequently customers' satisfaction. In addition, the results showed that although the customers are always seeking hotels with better performance, they are also concerned with the quality of related services in the COVID-19 outbreak.

The impact of coronavirus pandemic (COVID-19) on education: The role of virtual and remote laboratories in education.

To avoid the spread of the COVID-19 crisis, many countries worldwide have temporarily shut down their academic organizations. National and international closures affect over 91% of the education community of the world. E-learning is the only effective manner for educational institutions to coordinate the learning process during the global lockdown and quarantine period. Many educational institutions have instructed their students through remote learning technologies to face the effect of local closures and promote the continuity of the education process. This study examines the expected benefits of e-learning during the COVID-19 pandemic by providing a new model to investigate this issue using a survey collected from the students at Imam Abdulrahman Bin Faisal University. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed on 179 useable responses. This study applied Push-Pull-Mooring theory and examined how push, pull, and mooring variables impact learners to switch to virtual and remote educational laboratories. The Protection Motivation theory was employed to explain how the potential health risk and environmental threat can influence the expected benefits from e-learning services. The findings revealed that the push factor (environmental threat) is significantly related to perceived benefits. The pull factors (e-learning motivation, perceived information sharing, and social distancing) significantly impact learners' benefits. The mooring factor, namely perceived security, significantly impacts learners' benefits.

A literature review on beneficial role of vitamins and trace elements: Evidence from published clinical studies.

COVID-19 is a kind of SARS-CoV-2 viral infectious pneumonia. This research aims to perform a bibliometric analysis of the published studies of vitamins and trace elements in the Scopus database with a special focus on COVID-19 disease. To achieve the goal of the study, network and density visualizations were used to introduce an overall picture of the published literature. Following the bibliometric analysis, we discuss the potential benefits of vitamins and trace elements on immune system function and COVID-19, supporting the discussion with evidence from published clinical studies. The previous studies show that D and A vitamins demonstrated a higher potential benefit, while Selenium, Copper, and Zinc were found to have favorable effects on immune modulation in viral respiratory infections among trace elements. The principles of nutrition from the findings of this research could be useful in preventing and treating COVID-19.