PREDICTING THE RESILIENCE OF THE TOURISM INDUSTRY AFTER THE COVID-19 HEALTH EPIDEMIC

The tourism industry is a major branch of the service sector that contributes to national wealth creation. It is one of the main drivers of employment and foreign exchange drainage in the economies. However, some tragic events affect and slow down its development. The epidemiological context of the coronavirus has deeply affected the sector, implying a total halt to all tourist activities at national and international levels. In this sense, Dauphiné and Provitolo (2007) [16] state that "it is then possible to adopt another strategy based on the concept of resilience. This strategy aims, not to oppose the hazard, but to reduce its impacts as much as possible. From this reflection, we have attempted in this essay to clarify and analyze the determinants of resilience summarised in the characteristics linked to the environment, the strategies implemented, the personal traits of the manager, and the characteristics specific to tourism organizations contributing to helping these Moroccan economic units, specifically in the Rabat-Salé-Kénitra region, to overcome the setbacks caused by the covid-19 health crisis. © 2023 Little Lion Scientific.

CLASSIFICATION OF THE RISK OF COMORBID COVID-19 PATIENT AT BENGKALIS HOSPITAL USING BAYESIAN BINARY LOGISTICS REGRESSION

COVID-19 is an infectious disease caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2). This viral infection causes illness with symptoms ranging from mild to severe. The number of deaths from this disease is increasing day by day. A person who is most easily infected with the COVID-19 virus is a person who has a comorbid disease, because the body's immunity decreases due to the impact of a previous illness. The purpose of this study was to determine the comorbid factors that trigger a person's death due to COVID-19. This research uses binary logistic regression with Bayes method parameter estimation. In this study, the predictor variables used were in the form of categories. The results showed that the factors that influence the death of a person on the death of COVID-19 in comorbid diseases are Diabetes Mellitus and Pneumonia. © 2022, Intellectual Research and Development Education Foundation (YRPI). All rights reserved.

Demystifying EFL Teachers' Experiences During the Pandemic: A Study of the Psychosocial Risks Resulting from COVID-19

This study aims to identify the main psychosocial risks that COVID - 19 has caused in Ecuadorian EFL teachers and determine the factors associated with developing these risks. This study employed a quantitative approach and a non-experimental cross-sectional design, with a sample of 980 teachers from different educational levels from Ecuador. The data analysis was done using Stata 16 statistical program and a multivariate binary logistic regression (LR). The results showed that teachers are emotionally drained, isolated, frustrated with teaching, and exhausted because of teaching during the pandemic, being the women the most affected. The main factors that increased the probability of suffering these psychosocial effects were extra activities beyond working hours, status in the teacher's institutions, and gender. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Would people prefer city-center living in the post-COVID era?: Experience, status, and attitudes to social disasters

The COVID-19 pandemic was a significant social disaster that radically affected the paradigm of current urbanization and city-center living. Responses to the disaster varied depending on related experiences, individual status, and attitudes. Thus, this research extends the previous literature by examining the effects of experiences related to the COVID-19 pandemic, socioeconomic status, and how perceptions and attitudes affect preferences for city-center living in the Seoul Metropolitan Area, South Korea. We use data from PSSRAC (Perception Survey of Seoul metropolitan area Residential Awareness during COVID-19) of 2021. A binary logistic regression method is used to examine significant characteristics that affected the residential preference change due to "Experience,” "Status,” and "Attitude” in the COVID-19 era. The findings showed that respondents' experience, status, and attitude related to the pandemic could have a complex effect on predictions of preference, for central or suburban living tendencies in the post-COVID-19 era. In terms of "Experience,” people who had bad experiences during the pandemic, for example, poor economic conditions were associated with suburban area living trends. For "Status,” socially and economically vulnerable groups preferred suburban residence due to the steep rise in housing prices in the city center after the pandemic. Finally, for "Attitude,” ‘value of housing for investment” was positively associated with a preference for city-center living in the post-COVID-19 era;respondents with a higher priority for maintaining remote work tended not to change their current residence. This study may provide planners, housing developers, and policymakers with meaningful implications for addressing urban changes in the post-COVID-19 era. Additionally, it is expected that this research's ESA analysis and results can be used as a valid reference for other global cities. © The Author(s) 2023.

Immediate effects of the COVID-19 pandemic on the use of wildlife as food and local communities in South America

The COVID-19 pandemic has had a range of effects on the environment and particularly on wildlife, through diverse and sometimes contradictory impact pathways. In this study, based on data collected among indigenous people and local communities from South America (Colombia, Ecuador, Guyana, and Peru), we investigated changes in the use of wildlife resources for food during the first months of the COVID-19 pandemic. Our study generated unique data collected from 756 households in 60 communities and nine sites. We confirm the hypothesis that wildlife use increased as a short-term response to food insecurity during the pandemic, and fish played a more significant role than wild meat in that endeavor. The increase in wild-meat consumption as a response to food insecurity was conditioned by prices and availability (unsuccessful hunts). Wildlife use did not increase as an alternative means to generate income, because communities were cut off from the market economy for several months. Also, whereas the reliance on wildlife emerged as an immediate solution during the first months of the crisis, longer-term strategies prioritized at household level involved diversifying food sources through domestic meat and crop production. Among all available animal-based proteins, local chicken came just after fish as the animal-based source of protein whose consumption increased the most during the first months of the crisis, as a response to food insecurity. We caution that relying on wildlife as a safety net may constitute a poverty trap in cases where the resource is depleted. Although not specifically studied here, access to land and the transmission of traditional knowledge/skills are possible additional determinants of the role that wildlife may play in times of crisis, and this is proposed as an area for future research. Results also attest to local communities expecting more support from their respective national governments, and confirm results from Walters et al. (2021) that governments were generally absent or unable to react quickly during the pandemic, leaving households (or their local leaders) with the responsibility to innovate with local solutions and pro-actively adapt to the rapid impacts of the crisis.

FARMERS' PERSPECTIVE OF COVID-19 EFFECTS ON DEMAND AND SUPPLY OF AGRICULTURAL COMMODITIES IN NYAMASHEKE DISTRICT, RWANDA

COVID-19 is an infectious disease caused by a new strain of coronavirus. This disease disrupted the functionality of the global economy, and the agriculture sector was not spared. It is in this context that this paper aims at assessing farmers' perceptions about the perceived shocks of COVID-19 on the side of demand and supply of agricultural commodities. The study was guided by three objectives viz to investigate the perceptions of farmers on the effects of COVID-19 pandemic on the demand for agricultural commodities, to explore the perceptions of farmers on the effects of COVID-19 pandemic on the supply of agricultural commodities and to analyze the factors affecting farmers' perceptions of COVID-19 pandemic on demand and supply of agricultural commodities in Nyamasheke district. The target population size was 6237 composed of farmers of three irrigated lands in Nyamasheke District. Yamane's formula for the sample size determination was used to find the sample size of the respondents which was 376 and then stratified and systematic sampling procedures were employed to get 174 farmers in Kirimbi, 114 farmers in Mugonero and 88 farmers in Kamiranzovu irrigated lands, respectively. This study employed both descriptive and inferential statistical tools to analyze data. Descriptive statistics were used to analyze data on the perceptions of farmers towards COVID-19 pandemic effects on the demand and the supply for agricultural commodities while the inferential statistics were used to estimate the logit of farmers' perceptions on COVID-19 pandemic effects of demand and supply of agricultural commodities in the area under study. Results from the descriptive statistics revealed that 326 (86.7%) farmers agreed that COVID-19 pandemic has affected the demand of agricultural commodities while 244 (64.9%) farmers confirmed that COVID-19 pandemic has affected the supply of agricultural commodities in the area under study. The output of the model revealed that the independent variables that significantly contribute to the logit of the dependent variable were farming experience and labor. During the COVID-19 pandemic, farmers encountered several challenges that disrupted their livelihoods including changes in household consumption patterns, changes in market functionalities, discontinuity of the planned training and field visits, and low number of farmers in the farms. Researchers recommend that farmers' purchasing power should be reinforced by providing financial support to them through lowering interest rates on loans. [ FROM AUTHOR]

Factors affecting face mask-wearing behaviors to prevent COVID-19 among Thai people: A binary logistic regression model.

Objectives: Face mask wearing is a standard preventive measure, in addition to handwashing and physical distancing. Individuals may find that wearing a face mask protects their physical health and prevents viral transmission. However, none of the studies in Thailand identified factors associated with face mask-wearing behaviors among Thai people. Therefore, this study aims to determine factors affecting face mask-wearing behaviors to prevent COVID-19. Methods: This research is analytical survey research. The data used in this study were under the project title "The assessment of psychosocial and behavioral response and compliance to restriction measures to prevent and control COVID-19: A series of the rapid survey." A total of 6,521 people participated in an online survey by multi-stage sampling. Bivariate logistic regression analysis was used to examine the factors associated with face mask-wearing behaviors. Results: After adjusting for independent variables (i.e., gender, age, education, career, smoking, and comorbidity disease), the bivariate logistic regression analysis revealed that gender, age, and career were statistically significant to the face mask-wearing behaviors (p < 0.05). Level of education, smoking, and comorbidity disease were not statistically significant with face mask-wearing behaviors among Thai people. Conclusion: Further study should explore broader on individual face mask perceptions and wearing in the continuing of COVID-19 across gender, age, and careers to better understand their health behaviors and to inform further policy. In addition, the development of an intervention to promote face mask wearing should target men who age below 30 yrs. and did not work in government services careers as this group of the population was likely not to wear a face mask outside the home.

Modelling academic delivery challenges during COVID-19: a binary logistic apporach

This study identifies significant challenges that academicians working in higher education experienced during the COVID-19 pandemic, as physical classrooms transformed into virtual ones. The study includes evidence from the experiences of higher education academicians from three countries: namely, India, Malaysia, and the United Arab Emirates. The study adopts a quantitative research method to analyse the challenges that impacted the ease of coping with pedagogical delivery and the significant differences across the three countries. The study uses binary logistic regression modelling to evaluate the significance of the identified factors. The findings indicate that academicians experienced challenges working from home while adapting to the new model of teaching. Contrary to the established results gender, lack of formal training, work experience and home environment, which did not seem to impact consistently across the three geographical locations. This also is the highlight of the study, as we used the log odds to illustrate the probabilities of impacting factors in each geographical location on the Ease of Coping the dependent variable. The findings of this study may be directed to comprehend the most substantial factors specific to ease of coping. As the universities prepare themselves for the 'new normal' this study will contribute towards a paradigm shift in higher education, thereby enhancing the development of a framework for an effective teaching model to address pedagogical transition.

Factors Affecting the Uptake of COVID-19 Vaccine among Dubai Airport's Professionals

Aim: Comprehending the elements that influence COVID-19 vaccination acceptability and recognizing expediters for vaccination decisions are critical components of developing effective ways to increase vaccine coverage in the general population. This study aims to investigate the main factors affecting COVID-19 vaccination uptake among Dubai 'Airport's employees. In addition, it seeks to explore the main signs and symptoms that appeared on vaccinated employees after taking the COVID-19 vaccination, hence, track the vaccine's safety. Methods: Employees at Dubai's airport in the United Arab Emirates (UAE), mainly in Dubai, provided data. To gather data online utilising the Google Forms platform, a questionnaire was used as the main quantitative tool. As 2000 questionnaires got distributed, 1007 employees participated in the survey, yielding a 50.4% response rate. Results: The results show that employees overwhelmingly agree with the assertion that the factors of accessibility and affordability have a significant effect on their decision to receive the COVID19 vaccine, followed by a trust in vaccine, knowledge, vaccine safety, advice and information, and beliefs on the vaccine. In this study, the agreement level on factors affecting the COVID-19 vaccine uptake was found significantly to be higher in females (88.6%) who were married (91.6%) and those aged over 60 years (89.2%) at P <.05. In addition, the results show that 53.7% of vaccinated staff was found to have one or more side effects of the vaccine, where none of them was hospitalized after immunization. The binary logistic regression analysis in this study shows that females were two times more likely to have 'vaccine's symptoms after vaccination than males (Exp (B): 1.6;95%CI: 1.127-2.351, P< .01). It further reveals that participants in the age group over 50 were three times more likely to have 'vaccine's symptoms after vaccination than participants in the age group 20-29 (Exp (B): 2.9;95%CI: 2.497-9.681, P< .001). Finally, it indicates that individuals with previous SARS-CoV-2 infection were 2 times more likely to have 'vaccine's symptoms after vaccination than those without known past infection (Exp (B): 1.9;95%CI: 1.272 2.542, P< .01). Conclusion: There are several factors that playing a significant role in population’s decision to receive the COVID-19 vaccine, where the accessibility and affordability factors were found to have the greatest effect on their decision to uptake the vaccine. The current study concluded that COVID-19 vaccination is safe and that adverse effects from a vaccine are usually modest and affected by several factors such as age, gender, and COVID-19 infection history. © 2022 Taryam et al.

Characteristics of Climate Concern—Attitudes and Personal Actions—A Case Study of Hungarian Settlements

This article discusses one of the most important social factors of climate protection: climate concern. Most research in this area focuses on North America and Western Europe or presents international comparative statistics. Our work is innovative because we have designated a lesser-known post-socialist region in East-Central Europe as a sample area, and we intend to conduct in-depth analyses at the municipal level. Our study describes the second largest city in Hungary, Debrecen, and its agglomeration. Based on a questionnaire survey in 2020 (N = 512), we examined opinion factors, and we have presented features consistent with or different from the findings in the relevant literature. In the statistical analysis, chi-square tests and binary logistic regressions were applied to reveal significant differences between the responses of different types of respondents. As response variables, we used the questions about general concerns regarding air pollution, knowledge about climate change, beliefs about tackling, perceived threat, behavioural responses, personal actions, and demography. We found that the concern about air pollution and a feeling of threat to respondents’ life was mainly affected by the degree of climate concern. We conclude that the knowledge of local communities on climate change has increased, and risk perception has improved. Still, there is no clear relationship between the level of concern and climate-conscious behaviour. The findings provide ideas for promoting local climate management and awareness-raising in the European Union or other countries.

Determinants of COVID-19 Anxiety in Pakistan Through Binary Logistic Regression

Background: hassled environment This study for was last conducted few months to due evaluate to outbreak the determinants of COVID-19. of anxiety among the people living in Methods: 279 participants were interviewed through a questionnaire about COVID-19 anxiety on four likert scales. Data was analyzed by using statistical software IBM SPSS Statistics 21. A binary logistic regression was used to estimate relationship between anxiety and T.V news, social media news, social isolation, disturbance of sleep and decrease in daily income. Significance of these factors are tested at 5 percent level of significance ( = 0.05). Results: 90.33 percent of the respondents are feeling COVID-19 anxiety while 9.67 percent peoples are taking it mild. The values of test statistics for the variables listening of T.V news, social media news, social isolation, disturbance of sleep and decrease in daily income are highly significant at p < 0.05. Conclusion: The existence of anxiety was found to be highly significance due to listening of T.V news, social media news, social isolation, disturbance of sleep and decrease in daily income. © 2021. Advancements in Life Sciences. All rights reserved.

Level of Depression of College Students with Binary Logistic Regression Model Approximation in Covid-19 times

The objective of this research is to detect the level of depression that university students have because of Covid-19 using the binary logistic regression model and comparing with the linear model, it is of non-experimental design, cross-sectional descriptive type, with quantitative approach, The 9-item Patient Health Questionnaire (PHQ-9) was used with a population of 2185 respondents carried out during the month of May, the population is made up of university students from the Piura region chosen randomly, voluntarily and anonymously, resulting that the PHQ-9 measurement instrument is very good with a Cronbach's Alpha = 0. 885 and McDonald's W = 0.886, with 69.9% depression in university students, concentrated in 4 levels of depression, mild 39.7%, moderate 17.8%, severe 7% and very severe 5.3%. It was concluded that the PHQ-9 depression measurement instrument is adequate to measure depression in university students. In the discussion, it was possible to model the equation with the binomial logistic regression model, which results in better approximations than the linear model;this model is adequate to measure the level of depression in university students in the Piura region. © 2021 IEEE.

Environmental factors and spatiotemporal distribution characteristics of the global outbreaks of the highly pathogenic avian influenza H5N1.

The spread of highly pathogenic avian influenza H5N1 has posed a major threat to global public health. Understanding the spatiotemporal outbreak characteristics and environmental factors of H5N1 outbreaks is of great significance for the establishment of effective prevention and control systems. The time and location of H5N1 outbreaks in poultry and wild birds officially confirmed by the World Organization for Animal Health from 2005 to 2019 were collected. Spatial autocorrelation analysis and multidistance spatial agglomeration analysis methods were used to analyze the global outbreak sites of H5N1. Combined with remote sensing data, the correlation between H5N1 outbreaks and environmental factors was analyzed using binary logistic regression methods. We analyzed the correlation between the H5N1 outbreak and environmental factors and finally made a risk prediction for the global H5N1 outbreaks. The results show that the peak of the H5N1 outbreaks occurs in winter and spring. H5N1 outbreaks exhibit aggregation, and a weak aggregation phenomenon is noted on the scale close to 5000 km. Water distance, road distance, railway distance, wind speed, leaf area index (LAI), and specific humidity were protective factors for the outbreak of H5N1, and the odds ratio (OR) were 0.985, 0.989, 0.995, 0.717, 0.832, and 0.935, respectively. Temperature was a risk factor with an OR of 1.073. The significance of these ORs was greater than 95%. The global risk prediction map was obtained. Given that the novel coronavirus (COVID-19) is spreading globally, the methods and results of this study can provide a reference for studying the spread of COVID-19.

Psychiatric Health as The Dominant Factor of Covid-19 Symptom

Strictly implementing health protocols have been campaigned continuously during the Covid-19 pandemic. However, the number of positive cases of Covid-19 keeps growing and reaches even more than thousands of cases per day. This study aims to determine the dominant factor of the Covid-19 symptom using a binary logistic regression model. This is a cross-sectional study using 415 respondents from the productive age population in Yogyakarta. The data collected was Covid-19 symptoms experienced by respondents in the last 14 days and 8 variables sourced from the health protocol and the Depression Anxiety Stress Scale. The instrument used was an online questionnaire. Data was collected for 1 month, which was at the beginning of the outbreak (March-April 2020). The data obtained were analyzed by Binary Logistic Regression. The results showed that the dominant factors for the Covid-19 symptom were psychiatric health condition affected the mild symptom shifting to severe symptom, but the handwashing activities and the body immune did not affect the shifting. If people with mild symptoms have severe psychiatric health conditions, they would have a 9 times risk shift into severe Covid-19 symptoms. and if people with mild symptoms have mild psychiatric health conditions, they will have 5 times the risk of shifting into severe Covid-19 symptoms. In conclusion, the dominant factors that affect the occurrence of the mild symptom shifting to severe symptom were psychiatric health conditions and the risk factor was 5-9 times. People had to pay more attention to keeping psychiatric health conditions so that they would not shift into severe symptoms. © 2021 Teikyo University School of Medicine. All rights reserved.

Individual outcome prediction models for patients with COVID-19 based on their first day of admission to the intensive care unit.

BACKGROUND: Currently, good prognosis and management of critically ill patients with COVID-19 are crucial for developing disease management guidelines and providing a viable healthcare system. We aimed to propose individual outcome prediction models based on binary logistic regression (BLR) and artificial neural network (ANN) analyses of data collected in the first 24 h of intensive care unit (ICU) admission for patients with COVID-19 infection. We also analysed different variables for ICU patients who survived and those who died. METHODS: Data from 326 critically ill patients with COVID-19 were collected. Data were captured on laboratory variables, demographics, comorbidities, symptoms and hospital stay related information. These data were compared with patient outcomes (survivor and non-survivor patients). BLR was assessed using the Wald Forward Stepwise method, and the ANN model was constructed using multilayer perceptron architecture. RESULTS: The area under the receiver operating characteristic curve of the ANN model was significantly larger than the BLR model (0.917 vs 0.810; p < 0.001) for predicting individual outcomes. In addition, ANN model presented similar negative predictive value than the BLR model (95.9% vs 94.8%). Variables such as age, pH, potassium ion, partial pressure of oxygen, and chloride were present in both models and they were significant predictors of death in COVID-19 patients. CONCLUSIONS: Our study could provide helpful information for other hospitals to develop their own individual outcome prediction models based, mainly, on laboratory variables. Furthermore, it offers valuable information on which variables could predict a fatal outcome for ICU patients with COVID-19.

Did the COVID-19 lockdown affect consumers' sustainable behaviour in food purchasing and consumption in China?

The COVID-19 pandemic situation has altered consumers' behaviour in food purchasing and consumption. This study, as a first attempt, assesses how the COVID-19 lockdown affects Chinese consumers' purchasing and consumption behaviour from a sustainability point of view. To reach this objective, a semi-structured questionnaire is designed, collecting data from 1006 participants. The food purchasing behaviour towards the importance of sustainable attributes (P), sustainable and healthy diets (D), and food waste (W) as three dependent variables are measured, and three binary logistic regressions are estimated. The results suggest that gender and age are relevant factors affecting sustainable behaviour. Household size has a significant effect on the healthy diet shift and food waste reduction. Risk attitude has a negative and significant impact on the sustainable purchase decision. In addition, consumers' food security, financial, and health risk perceptions are highly important factors in understanding consumers' sustainable purchasing and consumption behaviour. Consumers' subjective and objective knowledge levels regarding COVID-19 influence consumers' sustainability shift during the lockdown. The findings provide some practical implications for policymakers and stakeholders to carry out more socially acceptable policy actions that ensure consumers' sustainable purchasing and consumption behaviour during the COVID-19 pandemic.

Does worriedness among the rural adults promote COVID-19 related awareness in Bangladesh?

People living in urban areas are usually more aware of their health issues due to the availability and accessibility of health care facilities. Several studies have illustrated anxiousness, attitudes, and perceptions among urban people during COVID-19. This research attempted to assess how worriedness among rural adults may promote COVID-19 related awareness in Bangladesh. A cross-sectional online survey of 311 respondents aged 18 or greater was conducted through Facebook focusing only on the people living in rural areas. The survey included a consent form and requested demographic as well as pandemic related information in a three-section questionnaire from the respondents. We used the chi-square test statistic for bivariate analysis and the binary logistic regression model along with some tools to validate the model to analyze the impact of worriedness on awareness. The bivariate result showed a significant association among regular hand washing ( p = . 007 ) , knowledge about the proper amount of time for washing one's hands effectively ( p = . 004 ) , rules of social distancing ( p = . 00 ) , and education level ( p = . 046 ) with our outcome variable worriedness. From our binary logistic regression model fitting, it emerged that the females ( p = .032, OR = .729) who regularly wash their hands (0R = .393, p = .023), know the rules of social distancing for "yes" (0R = 14.525, p < .01), and "no" groups (0R = 5.518, p < .01), and age groups (18-27, 28 to 37, 38 to 47) were more worried. Results from our modeling justify an accuracy of 73.08%, a sensitivity of 93.71%, and a specificity of 29.33% with Cohen's kappa statistic = .2716, suggesting a fair model fitting. This study shows that the current COVID-19 situation created awareness among females and adults aged between 18 to 47 years in rural Bangladesh.

A Policy Category Analysis Model for Tourism Promotion in China During the COVID-19 Pandemic Based on Data Mining and Binary Regression.

BACKGROUND AND AIM: At the end of 2019, the outbreak of COVID-19 had a significant impact on China's tourism industry, which was almost at a standstill in the short-term. After reaching the preliminarily stable state, the government and the scenic area management department implemented a series of incentive policies in order to speed up the recovery of the tourism industry. Therefore, analyzing all sorts of social effects after policy implementation is of guiding significance for the government and the scenic areas. METHODS: Targeted as the social effect with the implementation of tourism promotion policy during the COVID-19 pandemic, this paper briefly analyzes the impact of COVID-19 on the national cultural and tourism industry and selects several representative types of tourism policies, crawls the comment data of Weibo users, analyzes users' perception and emotional preference to the policy, and thus mines the social effect of various policies. Subsequently, by identifying the social effects of various policies as dependent variables, a binary logistic regression model is constructed to obtain the best combination of tourism promotion policies and promote the rapid revitalization of the cultural and tourism industry. RESULTS: The results show that from the single policy, the social effect of the "safety" policy is the best. From the perspective of combination policies, the simultaneous release of "safety" policies and "economy" policies have the greatest social impact, which can dramatically accelerate the recovery of the cultural and tourism industry. Finally, this paper proposes suggestions for policy formulation to improve the ability of the cultural tourism industry to cope with crisis events. CONCLUSION: These results explain the perceived effects of the public on the government policies and can be used to judge whether the policies have been released in place. Based on the above results, corresponding suggestions are proposed as follows: 1) the combination of economic policies and security policies can achieve better results; and 2) the role of "opinion leaders" can be played to improve the perceived effect of policies.

Prediction of the Severity of the Coronavirus Disease and Its Adverse Clinical Outcomes.

This study aims to investigate blood and biochemical laboratory findings in patients with severe coronavirus disease 2019 (COVID-19) and to develop a joint predictor for predicting the likelihood of severe COVID-19 and its adverse clinical outcomes and to provide more information for treatment. We collected the data of 88 patients with laboratory-confirmed COVID-19. Further, the patients were divided into a non-severe group and a critical group (including critically ill cases). Univariate analysis showed that the absolute lymphocyte count, albumin level, albumin/globulin ratio, lactate dehydrogenase level, interleukin-6 (IL-6) level, erythrocyte count, globulin level, blood glucose level, and age were significantly correlated with the severity of COVID-19. The multivariate binary logistic regression model revealed that age, absolute lymphocyte count, and IL-6 level were independent risk factors in patients with COVID-19. The receiver operating characteristic curve revealed that the combination of IL-6 level, absolute lymphocyte count, and age is superior to a single factor as predictors for severe COVID-19, regardless of whether it is in terms of the area under the curve or the prediction sensitivity and specificity. Early application is beneficial to early identification of critically ill patients and timing individual treatments to reduce mortality.

A cross-sectional study to determine factors affecting dental and medical students' preference for virtual learning during the COVID-19 outbreak.

Virtual "online" teaching has been adopted by most universities around the world during the COVID-19 outbreak. This study aims to investigate the factors that might affect students' preference for virtual learning. Since a second wave of such pandemic is expected to occur, professors and teaching assistants may want to be prepared and aware to create an effective virtual learning environment for students. Using an online survey questionnaire, a total of 488 students in their basic science years of study (first to the third year) who are enrolled in dental and medical college responded to the online survey. The authors utilized a binary logistic regression model to estimate the impact of the nine explanatory variables (gender, student's year of study, accessibility of online tools, class engagement in virtual classes, GPA change during COVID-19 outbreak, class attendance in virtual vs. in-person lectures, type of study material, time saving for virtual classes, and anxiety level during the COVID-19 outbreak) on the students' preference for virtual learning. The analysis of variance showed that three out of the nine variables were not significant to the model: gender, study level, and study material. In addition, to understand the behavioral intention for the students during such pandemic, the online survey questionnaire captured students' voice on their willingness to wear masks, wash their hands, or both as well as their acceptance to take the vaccine once it is available. The results showed that 7.02 % of the students did not change simple health behaviors and 18.43% are not interested in taking the vaccine. This implies the importance of enacting new laws for reopening universities, applying high fines for violators, and obligating students to take the vaccine since university settings have high levels of social contact with populations from different communities and countries.