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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.
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User-Generated-Content (UGC) has gained increasing attention as an important indicator of business success in the tourism and hospitality sectors. Previous literature has analyzed travelers' satisfaction through quantitative approaches using questionnaire surveys. Another direction of research has explored the dimensions of satisfaction based on online customers' reviews using the machine learning approach. This study aims to present a new method that combines machine learning and survey-based approaches for customers' satisfaction analysis during the COVID-19 outbreak. In addition, we investigate the moderating role of service quality on the relationship between hotels' performance criteria and customers' satisfaction. To achieve this, the Latent Dirichlet Allocation (LDA) was used for textual data analysis, k-means was used for data segmentation, dimensionality reduction approach was used for the imputation of the missing values, and fuzzy rule-based was used for the prediction of satisfaction level. Following that, a survey-based approach was used to validate the research model by distributing the questionnaire and analyzing the collected data using the Structural Equation Modeling technique. The result of this research presents important contributions from the methodological and practical perspectives in the context of customers' satisfaction in tourism and hospitality during the COVID-19 outbreak. The outcomes of this research confirm the significant influence of the quality of services during the COVID-19 crisis on the relationship between hotel services and travellers' satisfaction.
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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.
ABSTRACT
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.
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Travel recommendation agents have been a helpful tool for travelers in their decision-making for destination choices. It has been shown that sparsity can significantly impact on the accuracy of recommendation agents. The COVID-19 outbreak has affected the tourism and hospitality industry of almost all countries in the world. Tourists who have planned to travel are canceling or postponing trips due to this pandemic. Accordingly, this will impact the rate of travelers' online reviews on tourism products. Hence, the lack of data, in terms of ratings and textual reviews on hotels, will be a major issue for travel recommendation agents during the COVID-19 outbreak in the context of tourism and hospitality. This will be a new challenge for the researchers in the development of travel recommendation agents. Machine learning has been found to be effective in dealing with the data sparsity in recommendation agents. Therefore, developing new algorithms would be helpful to overcome the sparsity issue in travel recommendation agents. This research provides a new method through neurofuzzy, dimensionality reduction, and clustering techniques and evaluates it on the TripAdvisor dataset to see its effectiveness in solving the sparsity issue. The results showed that the method which used the fuzzy logic technique with the aid of clustering, dimensionality reduction, and fuzzy logic is more efficient in addressing the sparsity problem and presenting more accurate results. The results of the method evaluation are presented and discussed, and several suggestions are provided for future studies. [ FROM AUTHOR] Copyright of Mathematical Problems in Engineering is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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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.
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COVID-19 , Refuse Disposal , COVID-19/epidemiology , Food , Humans , Pandemics , PanicABSTRACT
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.
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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.
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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.
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COVID-19 , Artificial Intelligence , Bibliometrics , Humans , Image Processing, Computer-Assisted , SARS-CoV-2ABSTRACT
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.
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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.
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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.
Subject(s)
Clinical Trials as Topic , Trace Elements/pharmacology , Vitamins/pharmacology , Bibliometrics , COVID-19/epidemiology , Humans , Immune System/drug effectsABSTRACT
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.