The mediation of perceived risk's impact on destination image and travel intention: An empirical study of Chengdu, China during COVID-19.

Perceived risk clearly impacts travel behavior, including destination selection and satisfaction, but it is unclear how or why its effect is only significant in certain cases. This is because existing studies have undervalued the mediating factors of risk aversion, government initiatives, and media influence as well as the multiple forms or dimensions of risk that can mask its direct effect. This study constructs a structural equation model of perceived risk's impact on destination image and travel intention for a more nuanced model of the perceived risk mechanism in tourism, based on 413 e-questionnaires regarding travel to Chengdu, China during the COVID-19 pandemic, using the Bootstrap method to analyze suppressing effect. It finds that while perceived risk has a significant negative impact on destination image and travel intention, this is complexly mediated so as to appear insignificant. Furthermore, different mediating factors and dimensions of perceived risk operate differently according to their varied combinations in actual circumstances. This study is significant because it provides a theoretical interpretation of tourism risk, elucidates the mechanisms or paths by which perceived risk affects travel intention, and expands a framework for research on destination image and travel intention into the realms of psychology, political, and communication science. It additionally encourages people to pay greater attention to the negative impact of crises and focuses on the important role of internal and external responses in crisis management, which can help improve the effectiveness of crisis management and promote the sustainable development of the tourism industry.

Epidemiological characteristics of COVID-19 travel-associated cases in Vojvodina, Serbia, during 2020.

Coronavirus disease 2019 (COVID-19) is currently the biggest public health problem worldwide. Intensive international travel and tourism have greatly contributed to its rapid global spreading. This study is the first comprehensive analysis of the epidemiological characteristics and clinical outcomes of the travel-associated COVID-19 cases in Vojvodina, Serbia, from March 6 to December 31, 2020 and it concerns permanent residents of Serbia. A cross-sectional study was conducted using data from the dedicated surveillance database of the Institute of Public Health of Vojvodina. Overall, 650 travel-associated COVID-19 cases were recorded in Vojvodina during the observed period, mainly imported from Bosnia and Herzegovina, followed by Austria and Germany (N = 195, 30%; N = 79, 12.15% and N = 75, 11.54%, respectively). The majority of cases were in the working-age groups, 18-44 and 45-64 years (56.46% and 34.15%, respectively). Overall, 54 (8.31%) patients developed pneumonia. In comparison to patients without pneumonia, those with pneumonia were older (mean age = 55.39 years vs. 41.34 years, p<0.01) and had a higher percentage of comorbidities (57.41% vs. 16.61%). Men were more likely to develop pneumonia than women (OR = 2.22; 95% CI: 1.14-4.30, p = 0.02), as well as those in retired-age group (OR = 4.11; 95% CI: 2.0-8.46, p<0.01). Obesity (OR = 14.40; 95% CI: 3.8-54.6, p<0.01), diabetes (OR = 9.82; 95% CI: 3.15-30.61, p<0.01) and hypertension (OR = 7.99; 95% CI: 3.98-16.02, p<0.01) were the most prominent main comorbidities as predictors of pneumonia. Our results represent general epidemiological and clinical dynamics of COVID-19 disease in Vojvodina. Also, they provide evidence that the predictors of pneumonia were: increasing age, male sex, having underlying comorbidities, an increasing number of days from the return to laboratory confirmation of COVID-19 (OR = 1.08, 95% CI: 1.03-1.12, p<0.01), as well as an increasing number of days from symptoms onset to diagnosis (OR = 1.14, 95% CI: 1.07-1.21, p<0.01), while anosmia and ageusia were protective factors for developing it (OR = 0.31, 95% CI: 0.12-0.79, p = 0.01).

Simulating the impacts of interregional mobility restriction on the spatial spread of COVID-19 in Japan.

A spatial susceptible-exposed-infectious-recovered (SEIR) model is developed to analyze the effects of restricting interregional mobility on the spatial spread of the coronavirus disease 2019 (COVID-19) infection in Japan. National and local governments have requested that residents refrain from traveling between prefectures during the state of emergency. However, the extent to which restricting interregional mobility prevents infection expansion is unclear. The spatial SEIR model describes the spatial spread pattern of COVID-19 infection when people commute or travel to a prefecture in the daytime and return to their residential prefecture at night. It is assumed that people are exposed to an infection risk during their daytime activities. The spatial spread of COVID-19 infection is simulated by integrating interregional mobility data. According to the simulation results, interregional mobility restrictions can prevent the geographical expansion of the infection. On the other hand, in urban prefectures with many infectious individuals, residents are exposed to higher infection risk when their interregional mobility is restricted. The simulation results also show that interregional mobility restrictions play a limited role in reducing the total number of infected individuals in Japan, suggesting that other non-pharmaceutical interventions should be implemented to reduce the epidemic size.

Carbon emission savings and short-term health care impacts from telemedicine: An evaluation in epilepsy.

OBJECTIVE: Health systems make a sizeable contribution to national emissions of greenhouse gases that contribute to global climate change. The UK National Health Service is committed to being a net zero emitter by 2040, and a potential contribution to this target could come from reductions in patient travel. Achieving this will require actions at many levels. We sought to determine potential savings and risks over the short term from telemedicine through virtual clinics. METHODS: During the severe acute respiratory syndrome coronavirus 2 (SARS-2-CoV) pandemic, scheduled face-to-face epilepsy clinics at a specialist site were replaced by remote teleclinics. We used a standard methodology applying conversion factors to calculate emissions based on the total saved travel distance. A further conversion factor was used to derive emissions associated with electricity consumption to deliver remote clinics from which net savings could be calculated. Patients' records and clinicians were interrogated to identify any adverse clinical outcomes. RESULTS: We found that enforced telemedicine delivery for over 1200 patients resulted in the saving of ~224 000 km of travel with likely avoided emissions in the range of 35 000-40 000 kg carbon dioxide equivalent (CO2 e) over a six and half month period. Emissions arising directly from remote delivery were calculated to be <200 kg CO2 e (~0.5% of those for travel), representing a significant net reduction of greenhouse gas emissions. Only one direct adverse outcome was identified, with some additional benefits identified anecdotally. SIGNIFICANCE: The use of telemedicine can make a contribution toward reduced emissions in the health care sector and, in the delivery of specialized epilepsy services, had minimal adverse clinical outcomes over the short term. However, these outcomes will likely vary with clinic locations, medical specialties and conditions.

Changes in motor function in Duchenne muscular dystrophy patients after travel restrictions due to COVID-19.

INTRODUCTION/AIM: This retrospective study aimed to quantify the changes in motor function in patients with Duchenne muscular dystrophy (DMD) due to the government-imposed travel restrictions associated with the coronavirus disease 2019 (COVID-19) pandemic. METHODS: Twelve DMD patients were enrolled in this investigation (mean ± SD age: 9.8 ± 3.6 y). Their physical characteristics and motor function were evaluated approximately 3 mo before, immediately before, and approximately 3 mo after the travel restrictions were decreed. Statistical comparisons were performed of the changes in motor function before and after the travel restrictions. RESULTS: The change in range of motion (ROM) of ankle dorsiflexion was significantly decreased after the travel restrictions. Changes in body mass index and other motor function parameters were not significant. DISCUSSION: An apparent decrease in the amount of physical activity due to travel restrictions in response to COVID-19 negatively affected ankle dorsiflexion ROM but not other motor functions. A more sedentary lifestyle and lack of regular physical therapy services most likely contributed to this reduction. The use of remote rehabilitation tools with the involvement of physiotherapists may help mitigate such changes and prevent more severe physical decline.

Mobility Change and COVID-19 in Japan: Mobile Data Analysis of Locations of Infection.

BACKGROUND: As the COVID-19 pandemic spread, the Japanese government declared a state of emergency on April 7, 2020 for seven prefectures, and on April 16, 2020 for all prefectures. The Japanese Prime Minister and governors requested people to adopt self-restraint behaviors, including working from home and refraining from visiting nightlife spots. However, the effectiveness of the mobility change due to such requests in reducing the spread of COVID-19 has been little investigated. The present study examined the association of the mobility change in working, nightlife, and residential places and the COVID-19 outbreaks in Tokyo, Osaka, and Nagoya metropolitan areas in Japan. METHODS: First, we calculated the daily mobility change in working, nightlife, and residential places compared to the mobility before the outbreak using mobile device data. Second, we estimated the sensitivity of mobility changes to the reproduction number by generalized least squares. RESULTS: Mobility change had already started in March, 2020. However, mobility reduction in nightlife places was particularly significant due to the state of emergency declaration. Although the mobility in each place type was associated with the COVID-19 outbreak, the mobility changes in nightlife places were more significantly associated with the outbreak than those in the other place types. There were regional differences in intensity of sensitivity among each metropolitan area. CONCLUSIONS: Our findings indicated the effectiveness of the mobility changes, particularly in nightlife places, in reducing the outbreak of COVID-19.

Citizenship, Migration and Mobility in a Pandemic (CMMP): A global dataset of COVID-19 restrictions on human movement.

This research note introduces a new global dataset, the Citizenship, Migration and Mobility in a Pandemic (CMMP). The dataset features systematic information on border closures and domestic lockdowns in response to the COVID-19 outbreak in 211 countries and territories worldwide from 1 March to 1 June 2020. It documents the evolution of the types and scope of international travel bans and exceptions to them, as well as internal measures including limitations of non-essential movement and curfews in 27 countries. CMMP can be used to study causes and effects of policy restrictions to migration and mobility during the COVID-19 pandemic. The dataset is available through Cadmus and will be regularly updated until the last pandemic-related restriction has been lifted or become long-term.

Increased air pollution exposure among the Chinese population during the national quarantine in 2020.

The COVID-19 quarantine in China is thought to have reduced ambient air pollution. The overall exposure of the population also depends, however, on indoor air quality and human mobility and activities. Here, by integrating real-time mobility data and a questionnaire survey on time-activity patterns during the pandemic, we show that despite a decrease in ambient PM2.5 during the quarantine, the total population-weighted exposure to PM2.5 considering both indoor and outdoor environments increased by 5.7 µg m-3 (95% confidence interval, 1.2-11.0 µg m-3). The increase in population-weighted exposure was mainly driven by a nationwide urban-to-rural population migration before the Spring Festival coupled with the freezing of the migration backward due to the quarantine, which increased household energy consumption and the fraction of people exposed to rural household air pollution indoors. Our analysis reveals an increased inequality of air pollution exposure during the quarantine and highlights the importance of household air pollution for population health in China.

Mobile device location data reveal human mobility response to state-level stay-at-home orders during the COVID-19 pandemic in the USA.

One approach to delaying the spread of the novel coronavirus (COVID-19) is to reduce human travel by imposing travel restriction policies. Understanding the actual human mobility response to such policies remains a challenge owing to the lack of an observed and large-scale dataset describing human mobility during the pandemic. This study uses an integrated dataset, consisting of anonymized and privacy-protected location data from over 150 million monthly active samples in the USA, COVID-19 case data and census population information, to uncover mobility changes during COVID-19 and under the stay-at-home state orders in the USA. The study successfully quantifies human mobility responses with three important metrics: daily average number of trips per person; daily average person-miles travelled; and daily percentage of residents staying at home. The data analytics reveal a spontaneous mobility reduction that occurred regardless of government actions and a 'floor' phenomenon, where human mobility reached a lower bound and stopped decreasing soon after each state announced the stay-at-home order. A set of longitudinal models is then developed and confirms that the states' stay-at-home policies have only led to about a 5% reduction in average daily human mobility. Lessons learned from the data analytics and longitudinal models offer valuable insights for government actions in preparation for another COVID-19 surge or another virus outbreak in the future.

Changing travel patterns in China during the early stages of the COVID-19 pandemic.

Understanding changes in human mobility in the early stages of the COVID-19 pandemic is crucial for assessing the impacts of travel restrictions designed to reduce disease spread. Here, relying on data from mainland China, we investigate the spatio-temporal characteristics of human mobility between 1st January and 1st March 2020, and discuss their public health implications. An outbound travel surge from Wuhan before travel restrictions were implemented was also observed across China due to the Lunar New Year, indicating that holiday travel may have played a larger role in mobility changes compared to impending travel restrictions. Holiday travel also shifted healthcare pressure related to COVID-19 towards locations with lower healthcare capacity. Network analyses showed no sign of major changes in the transportation network after Lunar New Year. Changes observed were temporary and did not lead to structural reorganisation of the transportation network during the study period.