• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권6호
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• pp.2018-2043
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• 2022

Nowadays, COVID-19 infections are influencing our daily lives which have spread globally. The major symptoms' of COVID-19 are dry cough, sore throat, and fever which in turn to critical complications like multi organs failure, acute respiratory distress syndrome, etc. Therefore, to hinder the spread of COVID-19, a Computerized Doughty Predictor Framework (CDPF) is developed to yield benefits in monitoring the progression of disease from Chest CT images which will reduce the mortality rates significantly. The proposed framework CDPF employs Convolutional Neural Network (CNN) as a feature extractor to extract the features from CT images. Subsequently, the extracted features are fed into the Adaptive Dragonfly Algorithm (ADA) to extract the most significant features which will smoothly drive the diagnosing of the COVID and Non-COVID cases with the support of Doughty Learners (DL). This paper uses the publicly available SARS-CoV-2 and Github COVID CT dataset which contains 2482 and 812 CT images with two class labels COVID⁺ and COVI^-. The performance of CDPF is evaluated against existing state of art approaches, which shows the superiority of CDPF with the diagnosis accuracy of about 99.76%.

• Safety and Health at Work
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• 제12권4호
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• pp.490-495
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• 2021

Introduction: The risk of experiencing psychiatric symptoms related to the COVID-19 pandemic is high among healthcare workers whose occupations are in public health, emergency medicine, and intensive or critical care. Materials and methods: A cross-sectional study aimed to assess the prevalence of fear of COVID-19 among 411 frontline Egyptian physicians during the COVID-19 pandemic; identify determinants and predictors for fear of COVID-19; determine the impact of fear of COVID-19 on job satisfaction; and detect the impact of fear of COVID-19 on turnover intention. Three standardized scales (fear of COVID-19, job satisfaction, and turnover intention scores) were used for data collection via online Google Form. Results: Regarding fear relating to the COVID-19 pandemic, 16.5% of the study subjects were classified as experiencing a severe fear level, while 78.1% experienced a moderate degree. A significant association between the level of fear relating to COVID-19 and the work department. The highest degree of fear is in a general-educational-university facility. Regarding job satisfaction, 42% of those having a severe level of fear are dissatisfied. Fear of COVID-19 is negatively associated with job satisfaction while positively significant correlated with turnover scores, a positive significant predictor of turnover intention. Job satisfaction is negatively associated with turnover intention; a negative significant predictor of turnover intention. Conclusions: Frontline Egyptian physicians reported higher levels of fear relating to the COVID-19 pandemic (moderate to severe). Increased fear levels relating to COVID-19 have a relationship with lower levels of job satisfaction and higher levels of job turnover.

• Asian Journal for Public Opinion Research
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• 제10권4호
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• pp.277-292
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• 2022

Cognizant of the role of the family in influencing health behaviors among its young members, especially during the time of COVID-19 in the Philippines where stay-at-home measures were in place from March 2020 until March 2022, this study sought to determine the predictive relationship of family relationship to COVID-19 preventive behavioral intention and pandemic fatigue among young Filipino undergraduates. A total of 1,665 undergraduate students participated in this cross-sectional online survey. Findings reveal that family cohesion significantly increases COVID-19 prevention behavioral intention among undergraduates. Moreover, students who report high levels of conflict in the family are less likely to comply with preventive behaviors and exhibit higher levels of pandemic fatigue.

• Tuberculosis and Respiratory Diseases
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• 제85권4호
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• pp.349-357
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• 2022

Background: The most consistently identified mortality determinants for the new coronavirus 2019 (COVID-19) infection are aging, male sex, cardiovascular/respiratory diseases, and cancer. They were determined from heterogeneous cohorts that included patients with different disease severity and previous conditions. The main goal of this study was to determine if activities of daily living (ADL) dependence measured by Barthel's index could be a predictor for COVID-19 mortality. Methods: A prospective cohort study was performed with a consecutive sample of 340 COVID-19 patients representing patients from all over the northern region of Portugal from October 2020 to March 2021. Mortality risk factors were determined after controlling for demographics, ADL dependence, admission time, comorbidities, clinical manifestations, and delay-time for diagnosis. Central tendency measures were used to analyze continuous variables and absolute numbers (proportions) for categorical variables. For univariable analysis, we used t test, chi-square test, or Fisher exact test as appropriate (α=0.05). Multivariable analysis was performed using logistic regression. IBM SPSS version 27 statistical software was used for data analysis. Results: The cohort included 340 patients (55.3% females) with a mean age of 80.6±11.0 years. The mortality rate was 19.7%. Univariate analysis revealed that aging, ADL dependence, pneumonia, and dementia were associated with mortality and that dyslipidemia and obesity were associated with survival. In multivariable analysis, dyslipidemia (odds ratio [OR], 0.35; 95% confidence interval [CI], 0.17-0.71) was independently associated with survival. Age ≥86 years (pooled OR, 2.239; 95% CI, 1.100-4.559), pneumonia (pooled OR, 3.00; 95% CI, 1.362-6.606), and ADL dependence (pooled OR, 6.296; 95% CI, 1.795-22.088) were significantly related to mortality (receiver operating characteristic area under the curve, 82.1%; p<0.001). Conclusion: ADL dependence, aging, and pneumonia are three main predictors for COVID-19 mortality in an elderly population.

• 한국임상약학회지
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• 제34권3호
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• pp.141-154
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• 2024

Background: As preexisting comorbidities are risk factors for Coronavirus Disease 19 (COVID-19), improved tools are needed for screening or diagnosing COVID-19 in clinical practice. Difficulties of including vulnerable patient data may create data imbalance and hinder the provision of well-performing prediction tools, such as artificial intelligence (AI) models. Thus, we systematically reviewed studies on AI prognosis prediction in patients infected with COVID-19 and existing comorbidities, including cancer, to investigate model performance and predictors dependent on patient data. PubMed and Cochrane Library databases were searched. This study included research meeting the criteria of using AI to predict outcomes in COVID-19 patients, whether they had cancer or not. Preprints, abstracts, reviews, and animal studies were excluded from the analysis. Majority of non-cancer studies (54.55 percent) showed an area under the curve (AUC) of >0.90 for AI models, whereas 30.77 percent of cancer studies showed the same result. For predicting mortality (3.85 percent), severity (8.33 percent), and hospitalization (14.29 percent), only cancer studies showed AUC values between 0.50 and 0.69. The distribution of comorbidity data varied more in non-cancer studies than in cancer studies but age was indicated as the primary predictor in all studies. Non-cancer studies with more balanced datasets of comorbidities showed higher AUC values than cancer studies. Based on the current findings, dataset balancing is essential for improving AI performance in predicting COVID-19 in patients with comorbidities, especially considering age.

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.145-152
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• 2022

One of the impacts of Covid-19 on education systems has been the shift to online education. This shift has changed the way education is consumed and perceived by students. However, the exact nature of student perception about online education is not known. The aim of this study was to understand the perceptions of Saudi higher education students (e.g., post-school students) about online education during the Covid-19 pandemic. Various aspects of online education including benefits, features and cybersecurity were explored. The data collected were analysed using statistical techniques, especially artificial neural networks, to address the research aims. The key findings were that benefits of online education was perceived by students with positive experience or when ensured of safe use of online platforms without the fear cyber security breaches for which recruitment of a cyber security officer was an important predictor. The issue of whether perception of online education as a necessity only for Covid situation or a lasting option beyond the pandemic is a topic for future research.

• Asian Journal for Public Opinion Research
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• 제8권3호
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• pp.297-323
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• 2020

The relationship between compliance with behaviors recommended to prevent the spread of COVID-19 and media exposure, negative emotions, and risk perception was examined using regression analyses of data from KAMOS, a nationally representative survey of South Korean adults. The strongest predictor of preventive behaviors in general was negative emotions, which had the largest βh (.22) among the independent variables considered. The eight negative emotions, identified using factor analysis of a series of 11 emotions, were anger, annoyance, fear, sadness, anxiety, insomnia, helplessness, and stress. Negative emotions themselves were influenced most strongly by the respondent's anxiety over social safety (βe=.286), followed by prediction of COVID-10 spread (β=.121, p<.001) and perceived risk of COVID-19 infection (β=.70, p=.023). Females (β=-.134) and those who felt less healthy (βo=-.097) experienced more negative emotions. Media exposure and increased media exposure both have significant relationships with negative emotions and both a direct and indirect impact on the adoption of preventive measures. Women, older people, and healthier people perceived greater risks and engaged in more preventive behaviors than their counterparts.

• 한국보건간호학회지
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• 제36권3호
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• pp.323-333
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• 2022

Purpose: This study was conducted to identify factors of COVID-19 stress, impulsiveness, and family strength on suicidal ideation of public nursing officials in community health centers. Methods: This study was a descriptive analysis study the subjects of this study were a total of 145 public nursing officials from community health centers in Busan. The data were analyzed using IBM SPSS/WIN 23.0 version. Result: The result of the predictor analysis showed that the motor impulsiveness (β=.383, p<.001), workload (β=.222, p=.003), cognitive impulsiveness (β=-.205, p=.012), bond (β=-.169, p=.033). The regression model showed an explanatory power of 24.1%. Conciusion: it is necessary to increase impulse and stress control ability, and to adjust the workload. In addition, it is necessary to systematically guarantee a working environment where practical vacation can be used, but it may not be a realistic alternative in a disaster crisis such as COVID-19, so indirect alternatives such as reinforcing infectious disease experts, improving the work environment, and psychological support to prevent suicide in advance are required.

• 품질경영학회지
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• 제52권1호
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• pp.1-21
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• 2024

Purpose: This study seeks to explore the impact of COVID-19-induced boredom, a prevalent form of pandemic-related stress, on travel motivation and post-pandemic travel intentions. Additionally, it examines the interplay among travel motivation, travel constraints, and the willingness to pay more for travel experiences in the post-pandemic context. Methods: A PLS-SEM analysis was conducted to analyze the data. Data collection took place through an online survey in February and March 2021, with a total of 575 respondents participating. Participants provided responses regarding their current levels of boredom due to COVID-19, five different travel motivations, seven travel constraints, and their post-pandemic travel intentions. Additionally, participants were asked about their willingness to pay more for travel. Results: This study highlights the significant role of COVID-19-induced boredom in predicting post-pandemic travel intentions and the willingness to pay more for travel. Contrary to previous perceptions, boredom emerges as a driving factor, enhancing travel intentions during the pandemic. Additionally, relaxation becomes the primary motivation for travel during COVID-19, and structural constraints exert a noticeable impact on travel intentions, challenging previous assumptions. Stress levels directly influence the willingness to pay more during travel experiences, expanding the understanding of additional payment behavior in the context of travel. Conclusion: This study offers practical insights for tourism stakeholders. Recognizing and addressing boredom in marketing strategies, implementing aggressive additional payment options, and focusing on relaxation-oriented travel products are recommended to cater to post-pandemic traveler preferences and revive the tourism industry effectively.

• 디지털산업정보학회논문지
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• 제19권2호
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• pp.79-87
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• 2023

On-line learning has been adopted as a major educational method due to the COVID-19 pandemic. Students and faculties got accustomed to on-line educational environment as they experienced it during the COVID-19 pandemic. Development of various technologies and social requirement for educational renovation lay groundwork for on-line learning as well. Therefore, on-line learning or blended learning will be likely to go on after the end of COVID-19 pandemic and it is necessary to prepare the guidelines for effective utilizing on-line learning. The primary purpose of this study is to examine the learning behaviors and the learning effects by using LMS data. Learning behaviors were measured in terms of learning time and access frequency for pre-recorded video lectures targeting computer-practice classes. The results of empirical analysis reveal that frequency was the significant predictor of course achievements but learning time was not. The findings of empirical analysis will provide insights that the effective planning and designing on-line classes based on learning behaviors are key to enhancing learning effects and learner's satisfaction.