Association studies between COVID-19 and SSc-ILD

Severe COVID-19 patients may develop pulmonary fibrosis, similar to SSc-ILD disease, suggesting a potential link between the two diseases. However, there are limited treatment options for SSc-ILD-type diseases. Therefore, investigating pathological markers of the two diseases can provide valuable insights for treating related conditions. RNA sequencing technology offers high throughput and precision. However, the bimodal nature of RNA-Seq data cannot be accurately captured by commonly used algorithms such as DESeq2. To address this issue, the Beta-Poisson model has been developed to identify differentially expressed genes. Unlike the classical DESeq2 algorithm, the Beta-Poisson model introduces a Beta distribution to construct a new hybrid distribution in place of the Gamma distribution of the Gamma-Poisson distribution, effectively characterizing the bimodal features of RNA-Seq data. The transcriptomes of SARS-CoV infection and SSc-ILD disease in the lung epithelial cell dataset were analyzed to identify common differentially expressed genes of SARS-CoV and SSc-ILD disease. Gene function and signaling pathway enrichment analysis and protein-protein interaction (PPI) network were used to identify common pathways and drug targets for SSc-ILD with COVID-19 infection. The results show that there are 50 differentially expressed genes in common between COVID-19 and SSC-ILD. The functions of these genes are mainly enriched in immune system response, interferon signaling pathway and other related signaling pathways, and enriched in biological processes such as cell defense response to virus and interferon regulation. Based on the detection of hub genes based on PPIs network, it is predicted that STAT1, ISG15, IRF7, MX1, EIF2AK2, DDX58, OAS1, OAS2, IFIT1 and IFIT3 are the key genes involved in the pathological phenotype of the two diseases. Based on the key genes, the interaction of transcription factor (TF) and miRNA with common differentially expressed genes is also identified. The possible pathological markers of the two diseases and related molecular regulatory mechanisms of disease treatment are revealed to provide theoretical basis for the treatment of the two diseases. © 2023 Editorial Office of Journal of Shenzhen University. All rights reserved.

Within-group estimators for unbalanced-panel data regression model of the open unemployment rate data in east kalimantan province

The COVID-19 pandemic has hit hard the Indonesian economy. Many businesses had to close because they could not cover operational costs, and many workers were laid off creating an unemployment crisis. Unemployment causes people's productivity and income to decrease, leading to poverty and other social problems, making it a crucial problem and great concern for the nation. Economic conditions during this pandemic have also provided an unusual pattern in economic data, in which outliers may occur, leading to biased parameter estimation results. For that reason, it is necessary to deal with outliers in research data appropriately. This study aims to find within-group estimators for unbalanced panel data regression model of the Open Unemployment Rate (OUR) in East Kalimantan Province and the factors that influence it. The method used is the within transformation with mean centering and median centering processing methods. The results of this study may provide advice on factors that can increase and decrease the OUR of East Kalimantan Province. The results show that the best model for estimating OUR data in East Kalimantan Province is the within-transformation estimation method using median centering. According to the best model, the Human Development Index (HDI) and Gross Regional Domestic Product (GRDP) are two factors that influence the OUR of East Kalimantan Province (GRDP). © 2023, International Association of Engineers. All rights reserved.

Large-Scale Analysis of New Employee Network Dynamics

The COVID-19 pandemic has accelerated digital transformations across industries, but also introduced new challenges into workplaces, including the difficulties of effectively socializing with colleagues when working remotely. This challenge is exacerbated for new employees who need to develop workplace networks from the outset. In this paper, by analyzing a large-scale telemetry dataset of more than 10,000 Microsoft employees who joined the company in the first three months of 2022, we describe how new employees interact and telecommute with their colleagues during their "onboarding"period. Our results reveal that although new hires are gradually expanding networks over time, there still exists significant gaps between their network statistics and those of tenured employees even after the six-month onboarding phase. We also observe that heterogeneity exists among new employees in how their networks change over time, where employees whose job tasks do not necessarily require extensive and diverse connections could be at a disadvantaged position in this onboarding process. By investigating how web-based people recommendations in organizational knowledge base facilitate new employees naturally expand their networks, we also demonstrate the potential of web-based applications for addressing the aforementioned socialization challenges. Altogether, our findings provide insights on new employee network dynamics in remote and hybrid work environments, which may help guide organizational leaders and web application developers on quantifying and improving the socialization experiences of new employees in digital workplaces. © 2023 ACM.

Nonstationary Gaussian Process Discriminant Analysis With Variable Selection for High-Dimensional Functional Data

High-dimensional classification and feature selection tasks are ubiquitous with the recent advancement in data acquisition technology. In several application areas such as biology, genomics, and proteomics, the data are often functional in their nature and exhibit a degree of roughness and nonstationarity. These structures pose additional challenges to commonly used methods that rely mainly on a two-stage approach performing variable selection and classification separately. We propose in this work a novel Gaussian process discriminant analysis (GPDA) that combines these steps in a unified framework. Our model is a two-layer nonstationary Gaussian process coupled with an Ising prior to identify differentially-distributed locations. Scalable inference is achieved via developing a variational scheme that exploits advances in the use of sparse inverse covariance matrices. We demonstrate the performance of our methodology on simulated datasets and two proteomics datasets: breast cancer and SARS-CoV-2. Our approach distinguishes itself by offering explainability as well as uncertainty quantification in addition to low computational cost, which are crucial to increase trust and social acceptance of data-driven tools. Supplementary materials for this article are available online.

The Effect of Primary School Education on Preventive Behaviours during COVID-19 in Japan

Education plays a critical role in promoting preventive behaviours against the spread of pandemics. In Japan, handwashing education in primary schools was positively correlated with preventive behaviours against COVID-19 transmission for adults in 2020, during the early stages of COVID-19. The following year, the Tokyo Olympics were held in Japan, and a state of emergency was declared several times. Public perceptions of and risks associated with the pandemic changed drastically with the emergence of COVID-19 vaccines. We re-examined whether the effect of handwashing education on preventive behaviours persisted by covering a longer period of the COVID-19 pandemic than previous studies. A total of 26 surveys were conducted nearly once a month for 30 months from March 2020 (the early stage of COVID-19) to September 2022 in Japan. By corresponding with the same individuals across surveys, we comprehensively gathered data on preventive behaviours during this period. In addition, we asked about the handwashing education they had received in their primary school. We used the data to investigate how and to what degree school education is associated with pandemic-mitigating preventive behaviours. We found that handwashing education in primary school is positively associated with behaviours such as handwashing and mask wearing as a COVID-19 preventive measure but not related to staying at home. We observed a statistically significant difference in handwashing between adults who received childhood handwashing education and those who did not. This difference persisted throughout the study period. In comparison, the difference in mask wearing between the two groups was smaller but still statistically significant. Furthermore, there was no difference in staying at home between them. Childhood hygiene education has resulted in individuals engaging in handwashing and mask wearing to cope with COVID-19. Individuals can form sustainable development-related habits through childhood education.

Scaling law of COVID-19 cases and city size and its evolution over time

Urban scaling law quantifies the disproportional growth of urban indicators with urban population size, which is one of the simple rules behind the complex urban system. Infectious diseases are closely related to social interactions that intensify in large cities, resulting in a faster speed of transmission in large cities. However, how this scaling relationship varies in an evolving pandemic is rarely investigated and remains unclear. Here, taking the COVID- 19 epidemic in the United States as an example, we collected daily added cases and deaths from January 2020 to June 2022 in more than three thousand counties to explore the scaling law of COVID- 19 cases and city size and its evolution over time. Results show that COVID- 19 cases super- linearly scaled with population size, which means cases increased faster than population size from a small city to a large city, resulting in a higher morbidity rate of COVID- 19 in large cities. Temporally, the scaling exponent that reflects the scaling relationship stabilized at around 1.25 after a fast increase from less than one. The scaling exponent gradually decreased until it was close to one. In comparison, deaths caused by the epidemic did not show a super-linear scaling relationship with population size, which revealed that the fatality rate of COVID-19 in large cities was not higher than that in small or medium-sized cities. The scaling exponent of COVID- 19 deaths shared a similar trend with that of COVID- 19 cases but with a lag in time. We further estimated scaling exponents in each wave of the epidemic, respectively, which experienced the common evolution process of first rising, then stabilizing, and then decreasing. We also analyzed the evolution of scaling exponents over time from regional and provincial perspectives. The northeast, where New York State is located, had the highest scaling exponent, and the scaling exponent of COVID- 19 deaths was higher than that of COVID-19 cases, which indicates that large cities in this region were more prominently affected by the epidemic. This study reveals the size effect of infectious diseases based on the urban scaling law, and the evolution process of scaling exponents over time also promotes the understanding of the urban scaling law. The mechanism behind temporal variations of scaling exponents is worthy of further exploration. © 2023 Science Press. All rights reserved.

Investigation of demographic implicit discrimination and disparate impact in chest radiography image-based AI for COVID-19 severity prediction

It is important to evaluate medical imaging artificial intelligence (AI) models for possible implicit discrimination (ability to distinguish between subgroups not related to the specific clinical task of the AI model) and disparate impact (difference in outcome rate between subgroups). We studied potential implicit discrimination and disparate impact of a published deep learning/AI model for the prediction of ICU admission for COVID-19 within 24 hours of imaging. The IRB-approved, HIPAA-compliant dataset contained 8,357 chest radiography exams from February 2020-January 2022 (12% ICU admission within 24 hours) and was separated by patient into training, validation, and test sets (64%, 16%, 20% split). The AI output was evaluated in two demographic categories: sex assigned at birth (subgroups male and female) and self-reported race (subgroups Black/African-American and White). We failed to show statistical evidence that the model could implicitly discriminate between members of subgroups categorized by race based on prediction scores (area under the receiver operating characteristic curve, AUC: median [95% confidence interval, CI]: 0.53 [0.48, 0.57]) but there was some marginal evidence of implicit discrimination between members of subgroups categorized by sex (AUC: 0.54 [0.51, 0.57]). No statistical evidence for disparate impact (DI) was observed between the race subgroups (i.e. the 95% CI of the ratio of the favorable outcome rate between two subgroups included one) for the example operating point of the maximized Youden index but some evidence of disparate impact to the male subgroup based on sex was observed. These results help develop evaluation of implicit discrimination and disparate impact of AI models in the context of decision thresholds © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.

Opportunities for K-8 Students to Learn Statistics Created by States' Standards in the United States

Statistical literacy is key in this heavily polarized information age for an informed and critical citizenry to make sense of arguments in the media and society. The responsibility of developing statistical literacy is often left to the K-12 mathematics curriculum. In this article, we discuss our investigation of K-8 students' current opportunities to learn statistics created by state mathematics standards. We analyze the standards for alignment to the Guidelines for the Assessment and Instruction in Statistics Education (GAISE II) PreK-12 report and summarize the conceptual themes that emerged. We found that while states provide K-8 students opportunities to analyze and interpret data, they do not offer many opportunities for students to engage in formulating questions and collecting/considering data. We discuss the implications of the findings for policy makers and researchers and provide recommendations for policy makers and standards writers.

FINANCIAL SECURITY in SMALL BUSINESS in PERIOD of DIGITAL ECONOMY: In CASE of UZBEKISTAN

In this paper has been proposed a methodology for ensuring the financial security of enterprises in the context of recession caused by the COVID-19 pandemic. Based on pre-crisis data related to the new coronavirus infection pandemic and multi-component modeling of the dynamics of industrial production in the Republic of Uzbekistan during the "corona crisis,"this study seeks to identify the dynamics of growth by economic activity type and recovery rate in order to identify areas of state support for industrial production. In this paper has been investigated issues of financial security management of textile enterprises. On the basis of secondary statistics, the growth of textile production in the regions of the Republic of Uzbekistan in 2008-2020 was analyzed and the factors influencing it were identified. By the author have been presented the main tasks and conditions for the financial security of enterprises, as well as developed scientific and practical recommendations for eliminating factors affecting the financial security of textile enterprises. © 2022 Owner/Author.

Distance Learning in an Inclusive Preschool Focused on Autism During the COVID-19 Pandemic: A Mixed-Methods Multiple Case Study of Four Children

Despite the widespread use of distance learning during the coronavirus disease-2019 (COVID-19) pandemic, little is known about the developmental appropriateness of this instructional format for preschoolers, particularly when implemented in inclusive settings. The current research was implemented in a university-affiliated, state-funded inclusive preschool classroom focused on autism spectrum disorder (ASD) where parents were given the option to enroll in a full distance program (FDP) during the first 9 weeks of the 2020/21 school year. Parents of four children, including two children with ASD, selected the FDP option. Synchronous whole group, small group, and individual online sessions were recorded using screen capture and coded for children's maintained attention and directed communication. Further, parents and teachers completed exit interviews or a focus group, which were transcribed and analyzed using qualitative research methods. This is the first study to use observational methods to measure child engagement during preschool FDP sessions. Results from this mixed-method multiple case study paint a rich picture of both opportunities and limitations inherent in distance learning when implemented in inclusive preschool settings. General conclusions, future directions, and study limitations are discussed.

Élmények a zöldfelületeken: A megélt tér különbségei Stockholmban

A zöldfelületek használati értékének felértékelodése a lakosok szabadidejének növekedésével, a szabadido eltöltésének igényével, valamint a környezettudatosság erosödésével is összefügg, amit az elmúlt években a koronavírus-járvány okozta bezártság is tovább erosített. A különbözo embercsoportok által használt funkciók (szociotópok), a zöldfelületek esztétikája, hangulata egyaránt befolyásolják az adott területen szerzett élményeket, amelyek a térrol alkotott képünket, véleményünket is meghatározhatják. Jelen kutatás a stockholmi zöldfelületek példáján keresztül mutatja be a pozitív vagy negatív élményekért felelos tényezoket, illetve azt, hogy vane e összefüggés ezek és a városi zöldfelületek kategóriái között. Stockholm 2010-ben elsoként nyerte el az Európa Zöld Fovárosa díjat, ezáltal is jelezve, hogy a város élen jár a környezettudatos gondolkodásban, és példaként szolgál más európai, vagy akár az Európa Zöld Fovárosa díjra szintén pályázott, ám a kiválasztott döntosök szuk körébe sem bekerülo magyar városok (Budapest, Pécs) számára. Stockholm úgy is jellemezheto, mint „részben víz, részben zöldövezet, részben pedig város, ahogy azt az Európai Bizottság (2010: 21. o.). által kiadott hivatalos dokumentum is tartalmazza Stockholm az európai városok között is kimagasló zöldterület-elérhetoségi mutatóval (Buckland_Pojani 2022), Zöld Város Indexszel és környezeti politikával (Akande et al. 2019) rendelkezik, valamint a lakosság elégedettsége a városban éléssel is az élmezonybe tartozik (Okulicz-Kozaryn 2013), így jó gyakorlatként (best practice) szolgálhat más városok számára is. A vizsgálatban a stockholmi zöldfelületek kategorizálása Ibes (2015) tanulmánya alapján készült, miszerint hat kategória különítheto el tíz változó segítségével. A szociotóp funkciók gyakoriságában megfigyelheto a zöldfelületek eltéro „részesedése", illetve a különbözo zöldfelületi kategóriák idobeli (napszak, évszak, tartózkodási ido), gyakorisági (terület látogatottsága) és gazdasági (területen igénybe vett szolgáltatások ára) különbségei egyaránt kirajzolódnak.Alternate :Nowadays, the leisure time of the inhabitants is increasing, meaning that urban green spaces are becoming more and more valuable, as people visit these places more often and for longer periods to spend their spare time there. In the aftermath of the Covid pandemic, these open spaces are playing an even more important role in social interaction. The features used by different groups of the society (sociotopes), the aesthetics and the atmosphere of green spaces all have an impact on the experiences gained in the area, all these may determine our perception and opinion of the given space. The present research aims to show, through the example of Stockholm's green spaces, the factors responsible for good or negative experiences and whether there is any correlation between these and the urban green space types. Stockholm was the first city to win the European Green Capital Award in 2010, showing its leadership in environmental thinking and being a role model for other European, including Hungarian (Budapest, Pécs) cities that have also applied for the European Green Capital Award but were not shortlisted as finalists. Stockholm can also be described as 'part water, part green belt, part city', as stated in an official document published by the European Commission (2010, p. 21). Stockholm ranks highly among European cities in terms of green space accessibility (Buckland-Pojani 2022), Green City Index score and environmental policy (Akande et al. 2019), as well as in terms of public satisfaction with the quality of life in the city (Okulicz-Kozaryn 2013), therefore may serve as a best practice model for other cities. A categorisation of Stockholm's green spaces has been carried out in this study, using Ibes' (2015) paper as a basis. According to this, six categories can be distinguished by using ten variables. The green space categories identified in the analysis show differences in the number of sociotope functions and differences in the temporal (time of day, season, length of stay), frequency (visits to the area) and economic (cost of services in the area) aspects of these categories.

Tourism Capacity Management and Growth Trend: Acces la Success

The purpose of this article is to compare the number of tourists during different periods over the years, the management of tourist capacities with a focus on the increase or decrease of tourists compared to recent years. The paper was prepared using the quantitative method with secondary data based on (Kosovo Statistical Agency) processed in SPSS program. The research includes 14 different countries from which foreign tourists came for 2017 2018 2019. In 2017, 138,657 foreign tourists visited Kosovo, 165, 281 in 2018, and 154, 507 in 2019 (total 485, 445 tourists), the growth trend is presented in the analysis results. Initially the latest data released by (Kosovo Statistical Agency) were researched and analyzed and the number of foreign and domestic tourists by countries of different countries and regions in Kosovo was analyzed, focusing on capacities Hoteliers by type of accommodation. The results revealed that over the years the number of tourists has increased based on the methods used to measure the growth trend.

"Research exceptionalism" in the COVID-19 pandemic: an analysis of scientific retractions in Scopus

This study aimed to outline the profile of retractions of scientific articles on COVID-19 published in journals indexed in the Scopus database between 2020 and 2021. To analyze the data, we used a bibliometric technique, with the Bibliometrix package in the R-Studio software, and descriptive statistics. Twenty-nine retractions were analyzed, and we found that the most common reasons for retraction were related to ethical issues and that 68.97% of authors have previously retracted articles. We concluded that there appears to have been a change in the publication policies of journals, which resulted in an increase in scientific retractions related to COVID-19 during the study period.

The South-East Asia Region: Societal Conditions for LGBT Health and Cancer Research

This chapter is a substitute for chapters that were not completed due to the COVID-19 pandemic. In lieu, this chapter on LGBT and cancer in the South-East Asia region provides an overview of recent changes in sexual and gender minorities' legal situation, various aspects of LGBT communities, and organizations. While cancer is recognized as a threat to the region, a literature review of LGBT health research shows that a wide range of different topics are explored, with infectious diseases being the main focus. The time appears right for an expansion of the scope of LGBT health research to more prominently address noncommunicable diseases, such as cancer. However, it might be possible that COVID-19 will further delay research on noncommunicable diseases among LGBT populations. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Flipped Classrooms in Undergraduate Statistics: Online Works Just Fine

Background The flipped classroom method requires that students engage with homework before coming to the classroom so that class time can be spent on active and collaborative learning exercises. Research has demonstrated that this can improve student performance versus traditional lecturer-led teaching methods. Objective During the COVID-19 pandemic, the vast majority of teaching has been entirely online such that even ‘in-class' time has been virtual. The current article examined whether online-only delivery affects the efficacy of the flipped classroom approach. Method Grades for a research methods and statistics module and a statistics portfolio assignment were compared across consecutive cohorts of undergraduate psychology students taught by different methods. Results Overall grades on the module did not differ significantly across teaching methods but student performance on statistics tests did. Flipped classrooms, whether accompanied by on-campus or synchronous online classes, led to significantly better performance than traditional methods. No detriment was observed by teaching entirely online. Conclusion The key advantages of the flipped classroom method appear driven by active learning which can occur irrespective of classroom context. Teaching Implications Using flipped classrooms can be a useful tool, particularly in subjects where students may otherwise be less engaged with the content.

Multivariate Conway-Maxwell-Poisson Distribution: Sarmanov Method and Doubly Intractable Bayesian Inference

In this article, a multivariate count distribution with Conway-Maxwell (COM)-Poisson marginals is proposed. To do this, we develop a modification of the Sarmanov method for constructing multivariate distributions. Our multivariate COM-Poisson (MultCOMP) model has desirable features such as (i) it admits a flexible covariance matrix allowing for both negative and positive nondiagonal entries;(ii) it overcomes the limitation of the existing bivariate COM-Poisson distributions in the literature that do not have COM-Poisson marginals;(iii) it allows for the analysis of multivariate counts and is not just limited to bivariate counts. Inferential challenges are presented by the likelihood specification as it depends on a number of intractable normalizing constants involving the model parameters. These obstacles motivate us to propose Bayesian inferential approaches where the resulting doubly intractable posterior is handled with via the noisy exchange algorithm or the Grouped Independence Metropolis–Hastings algorithm. Numerical experiments based on simulations are presented to illustrate the proposed Bayesian approach. We demonstrate the potential of the MultCOMP model through a real data application on the numbers of goals scored by the home and away teams in the English Premier League from 2018 to 2021. Here, our interest is to assess the effect of a lack of crowds during the COVID-19 pandemic on the well-known home team advantage. A MultCOMP model fit shows that there is evidence of a decreased number of goals scored by the home team, not accompanied by a reduced score from the opponent. Hence, our analysis suggests a smaller home team advantage in the absence of crowds, which agrees with the opinion of several football experts. Supplementary materials for this article are available online.

The Relationship of SEDS, Food Intake and Work Engagement among Telecommuting Employees of the Education Sector during the COVID-19 Pandemic: A Mixed Method Study

Due to COVID-19, the shift to telecommuting became a widely used work set-up to maintain economic balance. This work set up is associated with risks to employees' wellness. As prevention to the risks, employees must be provided with ways to understand the telecommuting attributes. In relation, this study targets in understanding the links between the socio-economic demographic status, work engagement, and food intake of the education sector's tele-employees. The 110 samples are gathered from the Senior High school Department using convenience sampling, an online survey, and the mixed method. ANOVA and multi-linear regression are used as statistical treatments. The study found that the older generation with higher Income is more likely linked with higher work engagement. The younger generation, low-income earners, and males are inclined more toward unhealthy foods as compared to their counterparts. Low-income earners perceived that their work engagement falls under the category that energy to work is at a bare minimum level. The participants' education attainment revealed significance with energy-giving or carbohydrate-source foods. The qualitative data highlighted job position was perceived with a link to food intake and work engagement. Unhealthy food consumption is perceived with a beneficial association with work engagement, although it is suggested for further investigation. With these findings, the education sector's stakeholders, nutrition, mental health professionals, and future researchers would mainly benefit from this study for intervention generation. © 2023 ACM.

Environmental Sustainability and Territorial Competitiveness: A Comparison between Italian Provinces

The emergence of globalisation and the removal of obstacles between markets have heightened rivalry between territorial areas. To have a competitive advantage, the regions have to be unique. As one of the tactics used to boost their reputation on a territorial level, territories are progressively adopting environmental policies for sustainable and shared prosperity. Indeed, effective management of urban growth depends heavily on sustainable development. In this regard, the literature occasionally refers to the "green branding” of cities, a strategy that makes use of environmental aspects to boost the allure of metropolitan environments. There is currently little consensus in the literature on the measuring of environmental performance, and no statistical study has been done to confirm the efficacy of these measures in terms of territorial competitiveness. Therefore, it is important to determine whether there is a relationship between a territory's level of sustainability and competitiveness in Italy. According to the statistical analysis of the Italian provinces, the Northeast, Northwest, Centre, and Islands are the four geographical regions with the highest average scores. This unquestionably indicates a basic comprehension and supports the notion that there is a relationship between the two variables. However, it also serves as a warning about how geographical disparities in Italy represent a major issue affecting the most diverse sectors. In addition, the COVID-19 pandemic problem has drastically altered consumer demands and goals, leading consumers to seek out more sustainable travel and cities that are designed with citizens' requirements in mind. It will therefore become more and more important to research how public and private administrators, as well as policy makers, react to these changes.

Recreational travel behavior and COVID-19: insights from expected utility and the theory of planned behavior

Understanding what factors play a role in people's decisions to travel during a pandemic is important to public health officials and to stakeholders in the travel and tourism industry in the United States (US) and worldwide. This study examines factors influencing people's decisions to cancel/postpone recreational travel within the US amidst the COVID-19 pandemic. Our conceptual framework extends the Expected Utility model, commonly used in economics to model decisions under risk and uncertainty, to incorporate subjective norms and perceived behavioral control from the Theory of Planned Behavior. Our results suggest that risk perceptions, subjective norms, and concerns over transmitting COVID-19 to others play a significant role in the decision to cancel and postpone recreational travel. Results also suggest that perceived behavioral control may be less relevant to travel decisions when traveling involves elevated health risks.