Travel Restriction Policies Research Articles

Firm Value as an Intervening Variable in the Relationships of Liquidity, Leverage, and Profitability in the Hospitality and Tourism Sector

One of the benchmarks in assessing the success of an investment activity is based on the acquisition of stock returns. During the pandemic, the hotel, restaurant, and tourism industry sector was very significantly affected. Various travel restriction policies to the closure of tours caused a drop in capital market activity in this sector. The author uses firm value as a mediation to examine various factors such as profitability, liquidity, and leverage which can affect stock returns in the hotel, restaurant, and tourism industry sector. Financial report publications are used as data sources and research objects which are then analyzed and studied using Partial Least Square (PLS) as a test tool. Testing and analysis obtained the fact that the ratio of the ability to fulfill current liabilities (liquidity), the ratio of debt (leverage), and the ratio of profits (profitability) significantly affect the formation of firm value. Firm value can directly affect stock returns but as a whole, the variables cannot provide a significant direct effect on stock returns without being mediated by firm value. It can be concluded that high stock returns in the sectors tested can be influenced by liquidity, leverage, and profitability with good company value. Further research development could pay attention to direct factors such as accounting profits

State-Society Interaction in Addressing Crisis from the Communication Processes of Resilience Perspective: A Case of Local Government Role in Redeveloping Pandeglang Tourism Business

Tourism has significantly contributed to Indonesia’s GDP despite being vulnerable to crisis. Pandeglang Regency with its 14 tourism villages is one among regions that expects income from tourism which unfortunately attacked by tsunami crisis in the end of December 2018, aggravated by travel restriction policies during COVID-19 pandemic since end of March 2020. State-society interactions become important in addressing those crises. Through the commmunication processes of resilience perspective, this research aims to analyze the state-society interactions in addressing crisis on a case of local government agency’s role to redevelop Pandeglang Regency tourism business after crisis. This study uses quantitative approach, strengthened by qualitative research. This study applies survey research to 144 owners of Tourism MSMEs with descriptive analysis by SPSS 25.0, supported by analysis of interviews, a focus group discussion and field observations. The research results show that local tourism-related government agencies have a significant role in the state-society interactions addressing the crisis to redevelop Pandeglang tourism business where Pandeglang Regency Tourism Office has the most significant role. However, this agency needs to communicate more evenly to all Tourism MSMEs’ owners. It is also found that the communication processes of resilience perspective have led more reciprocal interactions between the state and the society.

Using hybrid automata to model mitigation of global disease spread via travel restriction

<abstract><p>Inspired by the COVID-19 pandemic, we build a large-scale epidemiological model that accounts for coordination between regions, each using travel restrictions in order to attempt to mitigate the spread of disease. There is currently a need for simulations of countries cooperating since travel restriction policies are typically taken without global considerations. It is possible, for instance, that a strategy which appears unfavorable to a region at some point during a pandemic might be best for containing the global spread, or that only by coordinating policies among several regions can a restriction strategy be truly effective. We use the formalism of hybrid automata to model the global disease spread among the coordinating regions. We model a connected network of coupled Susceptible-Exposed-Infected-Recovered (SEIR) models by considering a weighted directed graph with each node corresponding to a single region's disease model. The SEIR dynamics for each region admit terms for inter-regional travel determined by the graph's Laplacian that additionally accounts for travel restrictions between regions. The existence of an edge may change according to so-called guard conditions, which are triggered when the proportion of symptomatic infected individuals in a region reaches a critical value. Lastly, we run simulations in MATLAB of a global disease spreading among regions using automated travel restrictions and analyze the results.</p></abstract>

Assessing the Impact of Travel Restrictions on the Spread of the 2020 Coronavirus Epidemic: An Advanced Epidemic Model Based on Human Mobility

Infectious disease transmission can be greatly influenced by human mobility. During the COVID-19 pandemic, the Chinese Government implemented travel restriction policies to mitigate the impact of the disease or even block the transmission chain of it. In order to quantify the impact of these policies on the number of infections and the peak time of transmission, this research modified the traditional SIR model by considering human mobility. The proposed model was validated using a Baidu Qianxi dataset and the results indicate that the number of total infections would have increased by 1.61 to 2.69 times the current value and the peak time would have moved forward by 3 to 8 days if there were no such restriction policies. Furthermore, a mixing index α added in the proposed model showed that the proportion of residents using public transport to travel between different areas had a positive relationship with the number of infections and the duration of the epidemic.

Interaction between travel restriction policies and the spread of COVID-19

To investigate the interaction between travel restriction policies and the spread of COVID-19, we collected data on human mobility trends, population density, Gross Domestic Product (GDP) per capita, daily new confirmed cases (or deaths), and the total confirmed cases (or deaths), as well as governmental travel restriction policies from 33 countries. The data collection period was from April 2020 to February 2022, resulting in 24,090 data points. We then developed a structural causal model to describe the causal relationship between these variables. Using the Dowhy method to solve the developed model, we found several significant results that passed the refutation test. Specifically, travel restriction policies played an important role in slowing the spread of COVID-19 until May 2021. International travel controls and school closures had an impact on reducing the spread of the pandemic beyond the impact of travel restrictions. Additionally, May 2021 marked a turning point in the spread of COVID-19 as it became more infectious, but the mortality rate gradually decreased. The impact of travel restriction policies on human mobility and the pandemic diminished over time. Overall, the cancellation of public events and restrictions on public gatherings were more effective than other travel restriction policies. Our findings provide insights into the effects of travel restriction policies and travel behavioral changes on the spread of COVID-19, while controlling for informational and other confounding variables. This experience can be applied in the future to respond to emergent infectious diseases.

COVID-19 impacts on mobility and travel demand

Since the beginning of the COVID-19 pandemic, many travel restriction policies were implemented to reduce further spread of the virus. These measures significantly affected travel demand to levels which could not have been anticipated by most planners in transportation agencies. As the pandemic has proven to have significant short-term impacts, it is anticipated that some of these impacts may translate to longer-term impacts on overall travel behavior and the movement of people and goods. Beyond the pandemic, the observed travel patterns during this period also provides a great opportunity for planners to assess policies such as work from home and remote learning as strategies to manage travel demand. This study provides a scenario analysis framework to re-evaluate travel demand forecasts under uncertain future conditions using the Maryland Statewide Transportation Model (MSTM). Model parameters associated with working from home, household income, changes in discretionary travel, distance learning, increased e-commerce, vehicle occupancy and mode choice were identified. Parameter values were assigned under the various scenarios using employer surveys on workforce teleworking and observed data on e-commerce growth and shopping behavior. The main findings of this study capture the sensitivities of systemwide vehicle miles travel, and vehicle hours travel under different scenarios and implications on future investment decisions.The study found that future investments under the scenarios remain beneficial to systemwide performance and therefore justified. Although this study focuses on the state of Maryland, the scenario framework and parameter definitions can be used in other states or agencies within a travel demand model environment.

Global transmission of COVID-19 — A gravity model approach

This paper aims to describe the spatiotemporal transmission of COVID-19 and examine how various factors influence the global spread of COVID-19 using a modified gravity model. Log-linearizing the model, we run a negative binomial regression with observational data from 22 January 2020 to 31 December 2020. In the first model, population size and GDP per capita are positively related to the sum of newly confirmed COVID-19 cases within a 10-day window; the values for both variables are statistically significant throughout the study period. However, the significance of geographic distance varies. When a single geographic source exits in the early stage, the value is statistically significant. In the intermediate stage, when disease transmission is explosive between countries, the distance loses its statistical significance due to the emergence of multiple geographic transmission sources. In the containment stage, when the spread of disease is more likely to occur within a country, distance becomes statistically significant. According to the second model, the government’s internal movement control and nonpharmaceutical intervention policy, percentage of the population over 70 years old, and population-weighted density are statistically significant and are positively related to the incidence of COVID-19. By contrast, average monthly temperature, international travel restriction policies, and political regimes are statistically significant and negatively associated with the dependent variable.

Impacts of COVID-19 Travel Restriction Policies on the Traffic Quality of the National and Provincial Trunk Highway Network: A Case Study of Shaanxi Province.

According to recent research, the COVID-19 pandemic has impacted road traffic quality. This study aims to analyze the impacts of COVID-19 travel restriction policies on the traffic quality of the national and provincial trunk highway network (NPTHN) in Shaanxi Province. We collected the traffic data of the NPTHN for three consecutive years (from 2019 to 2021), before and after the COVID-19 outbreak, including weekly average daily traffic, weekly traffic interruption times, weekly traffic control time, weekly traffic accidents, weekly traffic injuries, and weekly traffic deaths. Using descriptive statistics and dynamic analysis methods, we studied the safety and service levels of the NPTHN. We set up an assessment model of the NPTHN operational orderliness through dissipative structure theory and entropy theory to study the operational orderliness of the NPTHN. Results show that in 2020, the service level, safety level, and operational orderliness of the NPTHN dropped to the lowest levels. The pandemic was gradually brought under control, and the travel restriction policies were gradually reduced and lifted. The adverse impacts on the operational orderliness of the NPTHN decreased, but the operational orderliness did not yet recover to the pre-pandemic level. Meanwhile, the service and safety levels of the NPTHN did not recover. Taken together, the COVID-19 travel restriction policies had adverse impacts on the traffic quality of the NPTHN in Shaanxi Province.

The effectiveness of urban mobility restriction policies on the spatio-temporal pattern of COVID-19 incidence in Balikpapan City

Abstract In 2020, the first year of the Covid-19 pandemic provided a valuable experience for city policymakers. As one of the epicenters of Covid-19 in East Kalimantan, Balikpapan City has also made various efforts to suppress the transmission of Covid-19, mainly focused on minimizing the urban mobility rate. This study was conducted to track the effectiveness of policies applied to the number of Covid-19 active cases in Balikpapan City. This study performs the Spatio-temporal Getis-Ord Gi* statistic to determine the characteristics of changes in the concentration of the Covid-19 active cases and calculate the correlation of the time data. The results showed that during the first year of pandemic and application of the travel restriction policy, hotspots were always formed in the potential economic sub-districts, which became the business center of Balikpapan City. In addition, the implementation of significant travel restriction policies has a strong correlation with the tendency to decrease the active case number of COVID-19 in each policy issue date.

Impact of COVID-19 travel-restriction policies on road traffic accident patterns with emphasis on cyclists: A case study of New York City

Since the COVID-19 outbreak, travel-restriction policies widely adopted by cities across the world played a profound role in reshaping urban travel patterns. At the same time, there has been an increase in both cycling trips and traffic accidents involving cyclists. This paper aims to provide new insights and policy guidance regarding the effect of COVID-19 related travel-restriction policies on the road traffic accident patterns, with an emphasis on cyclists’ safety. Specifically, by analysing the accidents data in the New York City and estimating three fixed effects logit models on the occurrence of different types of accidents in a given zip code area and time interval, we derived the following findings. First, while the overall number of road traffic accidents plummeted in the NYC after the stay-at-home policy was implemented, the average severity increased. The average number of cyclists killed or injured per accidents more than tripled relative to levels in similar times in previous years. Second, the declaration of the New York State stay-at-home order was significantly associated with a higher risk of accidents resulting in casualties. The number of Citi Bike trips in the area at the time overwhelmingly predicted severe risk for cyclists. Last, we applied the models to detect hot zones for cyclists’ severe accidents. We found that these hot zones tend to be spatially and temporally concentrated, making it possible to devise targeted safety measures. This paper contributes to the understanding of the impact of COVID-19 travel-restriction policies on accidents involving cyclists, reveals higher risks for cyclists as an unintended consequence of travel-restriction policies, and provides an analytical tool for road safety impact evaluation should future travel restrictions be considered.

Impact of pre-event testing and quarantine on reducing the risk of COVID-19 epidemic rebound: a modelling study

BackgroundWith the evolving growth of the COVID-19 epidemic, travel restriction policies would need to be adjusted accordingly. Prohibition of mass event may be relaxed for social and economic benefits when virus transmission stops but could bear the risk of epidemic rebound. Against the background of the varied SARS-CoV-2 prevalence internationally, we modelled the potential impacts of pre-event interventions on epidemic risk of holding a mass event when COVID-19 is under control.MethodsWe developed a mathematical model of SARS-CoV-2 transmission in Guangdong Province, China, where local virus transmission ceased to occur. A large-scale international trade fair was assumed to be held, with influx of people from overseas and rest of China over a short period of time, who participated for 2-week. Scenarios of pre-event intervention (none, quarantine arrangement and polymerase chain reaction (PCR) testing for participants) were compared. The influence of contact pattern, SARS-CoV-2 prevalence outside the province and China, and testing coverage were examined in sensitivity analyses.ResultsIn basecase scenario (no event), the epidemic has been under control since March 2020. The event would lead to the detection of 1% more confirmed cases by 31 July when community contact rate increases to pre-epidemic level. In event scenario without additional interventions, there would be 599 (93%) more new infections comparing with basecase scenario. To avert new infections, quarantining all participants before the event would be the most effective strategy, followed by quarantining all overseas participants and testing all other participants, and testing all participants before the event and on day 7. However, testing strategy is likely to be affected by the SARS-CoV-2 prevalence outside the event province.ConclusionsPre-event interventions are effective for reducing the risk of epidemic rebound caused by an international large-scale event. Universal testing for participants is likely to be an effective and feasible intervention.

PANDEMICS, TOURISM AND COMMUNITIES: AN ADAPTIVE POLICY FROM A CASE STUDY OF INDONESIA

This article shed more light on several policies can be adopted by the Indonesian government in terms saving the tourism-communities and tourism itself during Covid-19 pandemic. However, travel restriction policies in various countries also demanded the solidarity of tourists to stay at home, avoiding crowds, and delaying travel plans during the COVID-19 pandemic. It clearly causes the tourism sector to be heavily affected. As a solution , this research used the systematic literature review method to collecting data from 63 article that relevant to this topic. The results of this research classified into three main point that inovation can be raise by re-emphasising alternative forms of tourism through responsible torism and sustainable tourism. Utilization of technology (e-tourism) is a new form of transformation in the tourism sector, it can help tourist screening, case and contact tracing. Therefore, several policy should be created by central to local government that concern about communities sustainability during it’s condition.

The Effectiveness of Land Transportation Policy and the Dynamics of Travel Planned Behavior during the COVID-19 Pandemic in Indonesia

Since 2020, Indonesia has become the country with the highest positive confirmed cases of COVID-19 in Southeast Asia. This situation has urged the Government to issue a series of policies in the transportation sector, namely the Large-Scale Social Restrictions and the new habitual adaptation period or the New Normal Period. This research aims to understand the effectiveness of both land transportation policy implementations during the COVID-19 pandemic and the dynamics of driving factors behind people’s decision to travel during those periods. By taking a sample of 941 respondents in two phases of the survey, the data were processed by using Wilcoxon Signed Rank Test and Multiple Linear Regressions to be discussed in the Theory of Planned Behavior perspective. Serial data collection on two phases of travel restriction policies makes it possible to identify the dynamics of people's travel behavior. The results indicate that the policies were generally successful in reducing travel frequency. However, there were significant dynamics of intentional driving factors to travel. The upper-middle-income group has a more stable attitude between the two phases of the land transport restriction policy. On the contrary, the lower-middle-income group shows a more dynamic pattern of travel behavior with a higher intention to travel for work, particularly in the second phase. These results imply that the government’s travel restriction policies during the pandemic should consider different strategies to cope with the different factors influencing the travel decision of each group.

Observing the silent world under COVID-19 with a comprehensive impact analysis based on human mobility

Since spring 2020, the human world seems to be exceptionally silent due to mobility reduction caused by the COVID-19 pandemic. To better measure the real-time decline of human mobility and changes in socio-economic activities in a timely manner, we constructed a silent index (SI) based on Google’s mobility data. We systematically investigated the relations between SI, new COVID-19 cases, government policy, and the level of economic development. Results showed a drastic impact of the COVID-19 pandemic on increasing SI. The impact of COVID-19 on human mobility varied significantly by country and place. Bi-directional dynamic relationships between SI and the new COVID-19 cases were detected, with a lagging period of one to two weeks. The travel restriction and social policies could immediately affect SI in one week; however, could not effectively sustain in the long run. SI may reflect the disturbing impact of disasters or catastrophic events on the activities related to the global or national economy. Underdeveloped countries are more affected by the COVID-19 pandemic.

How does globalization affect COVID-19 responses?

BackgroundThe ongoing COVID-19 pandemic has highlighted the vast differences in approaches to the control and containment of coronavirus across the world and has demonstrated the varied success of such approaches in minimizing the transmission of coronavirus. While previous studies have demonstrated high predictive power of incorporating air travel data and governmental policy responses in global disease transmission modelling, factors influencing the decision to implement travel and border restriction policies have attracted relatively less attention. This paper examines the role of globalization on the pace of adoption of international travel-related non-pharmaceutical interventions (NPIs) during the coronavirus pandemic. This study aims to offer advice on how to improve the global planning, preparation, and coordination of actions and policy responses during future infectious disease outbreaks with empirical evidence.Methods and dataWe analyzed data on international travel restrictions in response to COVID-19 of 185 countries from January to October 2020. We applied time-to-event analysis to examine the relationship between globalization and the timing of travel restrictions implementation.ResultsThe results of our survival analysis suggest that, in general, more globalized countries, accounting for the country-specific timing of the virus outbreak and other factors, are more likely to adopt international travel restrictions policies. However, countries with high government effectiveness and globalization were more cautious in implementing travel restrictions, particularly if through formal political and trade policy integration. This finding is supported by a placebo analysis of domestic NPIs, where such a relationship is absent. Additionally, we find that globalized countries with high state capacity are more likely to have higher numbers of confirmed cases by the time a first restriction policy measure was taken.ConclusionsThe findings highlight the dynamic relationship between globalization and protectionism when governments respond to significant global events such as a public health crisis. We suggest that the observed caution of policy implementation by countries with high government efficiency and globalization is a by-product of commitment to existing trade agreements, a greater desire to ‘learn from others’ and also perhaps of ‘confidence’ in a government’s ability to deal with a pandemic through its health system and state capacity. Our results suggest further research is warranted to explore whether global infectious disease forecasting could be improved by including the globalization index and in particular, the de jure economic and political, and de facto social dimensions of globalization, while accounting for the mediating role of government effectiveness. By acting as proxies for a countries’ likelihood and speed of implementation for international travel restriction policies, such measures may predict the likely time delays in disease emergence and transmission across national borders.

Staycation During COVID-19 Pandemic with Virtual Tourism: Tele-Tourist Attitude Toward Experience in Cultural Heritage Destination

Malang City, Indonesia, is known as the Paris van oost Java and was officially named the City of Heritage because of the high interest of domestic and foreign tourists to trace the history and antique architecture of the city. However, travel restriction policies in various countries also demanded the solidarity of tourists to stay at home, avoiding crowds, and delaying travel plans during the COVID-19 pandemic. It clearly causes the tourism sector to be heavily affected. Therefore, it is necessary to manage cultural heritage tourism destinations that can allow tourists to do a staycation in sophisticated form and considering the carrying capacity more according to the COVID-19 safety protocol. As a solution, this research used the R&D method with mixed methods approach consist of observation, visual analysis, and survey experimental to produce web-based virtual tourism (web-VR) for ancient and astonishing The Great Mosque of Jami' Malang. The content was further confirmed using field experiments analyzed by Generalized Structured Component Analysis (GSCA) involving 30 tele-tourists. The results of this study indicate that the virtual experience of tourists, which consists of immersiveness and telepresence after visiting the virtual destination, has an effect on the attitude of tele-tourist. Therefore, virtual tourism can be considered complementary to actual visits, visitor management, as well as preserve destination sustainability. Keywords: COVID-19, immersion, telepresence, tele-tourist attitude, virtual experience, virtual tourism.

Deep reinforcement learning approaches for global public health strategies for COVID-19 pandemic.

BackgroundUnprecedented public health measures have been used during this coronavirus 2019 (COVID-19) pandemic to control the spread of SARS-CoV-2 virus. It is a challenge to implement timely and appropriate public health interventions.Methods and findingsPopulation and COVID-19 epidemiological data between 21st January 2020 to 15th November 2020 from 216 countries and territories were included with the implemented public health interventions. We used deep reinforcement learning, and the algorithm was trained to enable agents to try to find optimal public health strategies that maximized total reward on controlling the spread of COVID-19. The results suggested by the algorithm were analyzed against the actual timing and intensity of lockdown and travel restrictions. Early implementations of the actual lockdown and travel restriction policies, usually at the time of local index case were associated with less burden of COVID-19. In contrast, our agent suggested to initiate at least minimal intensity of lockdown or travel restriction even before or on the day of the index case in each country and territory. In addition, the agent mostly recommended a combination of lockdown and travel restrictions and higher intensity policies than the policies implemented by governments, but did not always encourage rapid full lockdown and full border closures. The limitation of this study was that it was done with incomplete data due to the emerging COVID-19 epidemic, inconsistent testing and reporting. In addition, our research focuses only on population health benefits by controlling the spread of COVID-19 without balancing the negative impacts of economic and social consequences.InterpretationCompared to actual government implementation, our algorithm mostly recommended earlier intensity of lockdown and travel restrictions. Reinforcement learning may be used as a decision support tool for implementation of public health interventions during COVID-19 and future pandemics.

Vietnam's Containment of COVID-19: Why the Coronavirus Mortality has Been So Low.

The successful experience of Vietnam, with a population of almost 100 million, in handling the COVID pandemic from March to November 2020 is presented as a case study. It is posited that lessons learned apply to every other country-even at this late stage of the epidemic. Detailed aspects of detection, containment, testing, contact tracing, quarantining, targeted lockdowns, public health messaging, and travel and mobility restriction policies are discussed.

What determines governments’ response time to COVID-19? A cross-country inquiry on the measure restricting internal movements

Abstract After the appearance of the first COVID-19 cases and deaths, countries’ responses were enacted at different points in time. This paper explores the factors behind the timeliness of travel restriction policies at the onset of the pandemic. Using instrumental variable techniques on a sample of 149 countries, our empirical exercise shows that while urban population and political stability are conducive to a prompt activation of a government’s lockdown policy after initial cases, a country’s wealth and the rule of law may produce an opposite effect. When the time from first deaths is considered, the presence of a female leader, net migration levels, voice and accountability, and political stability are associated with a quicker launch of a domestic travel restriction policy, while democracy and a country’s wealth may represent an obstacle to an immediate policy activation.

The impact of COVID-19 pandemic on travel behaviour of commuters in Makurdi metropolis

The outbreak of Corona Virus (COVID-19) in the 4th quarter of year 2019 at Wuhan city of China, and its rapid spread across the world necessitated the introduction of travel restriction policies by the federal and state governments of Nigeria. This was to help curtail the spread of the deadly disease. This study examined the travel behaviour of flouting commuters in Makurdi metropolis amidst the travel restriction policy during the lockdown period. About 496 questionnaires were administered to commuters travelling within the 16 km radius metropolis. Essential factors captured in the questionnaire included modal split and trip purposes as affected by socio-demographic characteristics of commuters such as; age, gender, marital status, level of education, etc. The findings of the study revealed that there was no statistically significant level of dependence of commuters’ socio-demographic characteristics on modal split and trip destinations at 5% level of significance using contingency tables. The trend revealed that there were no obvious influential changes in the travel behaviour of commuters in Makurdi metropolis amidst the COVID-19 pandemic. It was recommended that concerned authorities should create more awareness about the outbreak of pandemics to help instil fear in commuters and discourage travelling to prevent wide spread. Also, distribution of palliatives to households during lockdown periods to enable them cope with the situation as they remain indoors, and deployment of law enforcement agents to streets and highways to stop commuters from travelling during lockdown periods to minimise the spread of communicable diseases were recommended.