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As of July 30, 2020, more than 17 million novel coronavirus disease 2019 (COVID-19) cases were registered including 671,500 deaths. Yet, there is no immediate medicine or vaccination for control this dangerous pandemic and researchers are trying to implement mathematical or time series epidemic models to predict the disease severity with national wide data.

In this study, the authors considered COVID-19 daily infection data four most COVID-19 affected nations (such as the USA, Brazil, India and Russia) to conduct 60-day forecasting of total infections. To do that, the authors adopted a machine learning (ML) model called Fb-Prophet and the results confirmed that the total number of confirmed cases in four countries till the end of July were collected and projections were made by employing Prophet logistic growth model.

Results highlighted that by late September, the estimated outbreak can reach 7.56, 4.65, 3.01 and 1.22 million cases in the USA, Brazil, India and Russia, respectively. The authors found some underestimation and overestimation of daily cases, and the linear model of actual vs predicted cases found a p-value (<2.2e-16) lower than the R2 value of 0.995.

In this paper, the authors adopted the Fb-Prophet ML model because it can predict the epidemic trend and derive an epidemic curve.

After the identification of a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at Wuhan, China, a pandemic was widely spread worldwide. In Italy, about 240,000 people were infected because of this virus including 34,721 deaths until the end of June 2020. To control this new pandemic, epidemiologists recommend the enforcement of serious mitigation measures like country lockdown, contact tracing or testing, social distancing and self-isolation.

This paper presents the most popular epidemic model of susceptible (S), exposed (E), infected (I) and recovered (R) collectively called SEIR to understand the virus spreading among the Italian population.

Developed SEIR model explains the infection growth across Italy and presents epidemic rates after and before country lockdown. The results demonstrated that follow-up of strict measures such that country lockdown along with high testing is making Italy practically a pandemic-free country.

These models largely help to estimate and understand how an infectious agent spreads in a particular country and how individual factors can affect the dynamics. Further studies like classical SEIR modeling can improve the quality of data and implementation of this modeling could represent a novelty of epidemic models.

This study aims to examine how the initial ambiguity of COVID-19 contributed to tourists' intentions for visiting a once-viral outbreak site in the future.

The present study (N = 248) used partial least-squares structural equation modeling (PLS-SEM) to examine whether perceptions of ambiguity and mismanagement of COVID-19 are indirectly related to intentions to travel to Wuhan in a post-pandemic world through perceptions of risk and tourism value. Further, whether the model effects differed as a function of individual safety orientation was examined.

Perceptions of COVID-19 risk and tourism value serially mediated the effects of perceived COVID-19 ambiguity on post-pandemic travel intentions. Safety orientation did not moderate any paths. Perceived risk was a negative direct correlate of post-pandemic travel intentions.

The current study's strength is rooted in its specific targeting of post-pandemic travel intentions to Wuhan—the first city to experience a widescale outbreak of COVID-19 and subsequent international stigma—compared to general travel inclinations.

The study aims to investigate the consumers' behavioral intention toward green hotels. The tendency of individuals to afford green hotels is further escalating with progressing coronavirus disease-2019 (COVID-19) pandemic recurring waves. The increased worry of consumers toward health, hygiene and the climate is acquiring momentum and transforming how consumers traditionally perceive green hotels.

The study has recommended an integrated framework incorporating various research fields as attitude-behavior-context theory, theory of planned behavior (TPB) and moderating influences to study the associations among the antecedents of consumers' behavioral intention toward green hotels. The study comprised the participation of 536 respondents residing in the Delhi and National Capital Region (NCR) of India. The data analysis strategy involved the use of structural equation modeling (SEM) analysis to test the proposed research framework.

The results and findings of the study indicated a significant influence of fear and uncertainty of the COVID-19 pandemic and environmental concern on green trust. The results also revealed the considerable impact of green trust on willingness to pay premium, attitude and subjective norms, which significantly influenced behavioral intention. The analysis also revealed the moderating influence of environmental concern in the relationship of green trust and behavioral intention.

The study has recommended significant theoretical. The theorists may use this research framework to analyze better the transforming consumer behavior trends toward green hotels in the ongoing fearful and uncertain COVID-19 pandemic scenario.

The study has recommended significant managerial implications. The industry practitioners may also utilize the framework to sustain the hotel business and bring new strategic insights into practice to combat the impact of the pandemic and simultaneously win consumers' trust in green hotels.

Although the researchers have previously emphasized consumers' intention toward green practices embraced by hotels, the impact of the COVID-19 pandemic on the green hotel industry gained noticeable attention from researchers. Furthermore, there is a scarcity of literature providing insights on the behavioral dynamism of hotel customers' trust, attitude and willingness to pay for green hotels during the repetitive waves of the COVID-19 pandemic. The study will support the existing literature gap by enlightening the associations among the various antecedents of green hotels' behavioral intention, COVID-19 and environmental concern.

The paper aims to assess the impact and responses to coronavirus disease 2019 in six European heritage labs (Horizon 2020 Framework Programme) selected for their adaptive heritage re-use practices based on participation, self-organisation and self-management. As they are naturally oriented towards building resilient urban systems, the hypothesis is that the co-production of cultural values and places promoted by these projects could create the conditions for equitable perspectives of resilience in the normality of contemporary urban life.

The paper draws on data collected through a survey of six European Living Labs between January and May 2021. The survey results are framed by a literature review that defines adaptive reuse in terms of resilience. The five resilience characteristics described by Judith Rodin (awareness, diversification, integration, self-regulation and adaptability) are used to navigate the literature and organise the survey results.

Combining survey results and insights from the literature, some modes and elements (territorial, social, financial) are presented that contribute to creating the conditions for resilience through adaptive heritage reuse according to community-based approaches. Without claiming to be exhaustive, this evidence should be considered in the design phase of resilience programmes, policies or projects related to cultural heritage.

The concepts of community and resilience are becoming increasingly important in the field of cultural heritage. This paper makes a creative contribution to the ongoing debate by presenting and evaluating the contribution of adaptive reuse practices to resilience building.

Using COVID-19 pandemic as a more immediate empirical reference, this paper aims to understand the biosecurity risks arising from tourist activities and, through a more prospective analysis, to consider the relevance of public health issues in the context of tourism-sustainability nexuses.

The text assumes a hybrid format, incorporating elements resulting from empirical research and essayistic viewpoints. The collection of empirical elements was based on documental research in several sources, such as newspapers, international institutions of an intergovernmental nature and the discussion forum of the travel platform TripAdvisor.

By assuming mobility and large agglomerations of people from different origins, mass tourism has fostered multiple outbreaks of COVID-19 and the rapid global spread of contagion chains. The pandemic clearly exemplified the responsibility of tourism in the dispersion of biotic agents with severe ecological, economic, social and public health repercussions. It is, therefore, urgent to rethink the tourism growth trajectory and more effectively consider the biosecurity risks associated with mobility in discussions on tourism and sustainability. At the same time, tourism must be delineated in terms of the great aims of sustainability, and this transversal purpose to which it contributes should be considered an intrinsic condition of its own sectorial sustainability as an economic activity.

The biosecurity challenges posed by mass tourism are a very topical issue, still little considered in sustainability policies and on which there is a marked deficit in scientific research.

This paper aims to examine tourists' behavioral changes in response to health crises, this study examines the individual's uncertainty and adaptability to the challenges using behavioral coping strategies.

The study combines the theory of planned behavior (TPB) and protection motivation theory. Using the PLS-SEM technique, this study examines the relationship between the destination's competitive profits and travel intention of Iranian tourists in the post-Covid-19 pandemic.

The social-support coping (Instrumental) does not incorporate tourists' adaptive behaviors. Vulnerable vaccination significantly affects the extremeness of an individual's problem-focused coping, which affects tourist's adaptive behaviors in crisis time, indicating the effectiveness of the Covid-19 vaccination on travel intention.

The findings may assist tourism authorities and planners develop unique tourism products and services based on tourist behavior following the health crises.

This study contributes to development of the TPB method, indicating that visa exemption and competitive profits of a destination would motivate travel intention existing inefficacy of local government and its negative background, reshaping and thus influencing changing behavior.

This study investigates the effect of COVID-19 pandemic perceived health risk on traveller's post-pandemic perception and future travel intention. The study aims to provide insight into the unprecedented COVID-19 pandemic and its potential influence on tourist behaviour.

Two hundred and forty-four responses were gathered quantitatively through an online survey. The research hypotheses were analysed using the partial least square structural equation modelling (PLS-SEM).

This study found that COVID-19 affects tourists' travel behaviour. Key findings found that perceived health risk discourages travel attitudes and eventually lessens their future travel intentions. Results also suggest future strategies/directions for restarting the tourism industry.

The study outcome assists tourism stakeholders in understanding the changes in tourist behaviour amid the heightened perceived health risk of COVID-19. Tourism policymakers and industry players should consider exploring how to mitigate similar health crises in the future.

By extending the theory of planned behaviour (TPB), this study establishes a theoretical framework in exploring the interrelationships between perceived risk, post-pandemic perception and future travel intention. This study sets a significant research agenda for future tourism research in understanding the mechanism behind health risk perceptions and tourist behaviour.

The main purpose of this paper is to find out the perception of different respondents' groups related to the factors that influence the online learning for higher education in Bangladesh during the COVID-19 pandemic.

A survey through a structured questionnaire was conducted to gather qualitative information from the 250 respondents (university students, faculty members and administrative officers) in Bangladesh. A questionnaire has been used for collecting primary data, which have been selected using the justification method under the non-probability sampling technique.

The findings of this study indicated that majority of the respondents told that online classes could be more challenging than the traditional classroom because of the technological constraints, digital divide, insufficient data pack to access the material to attend the class, poor connectivity, lack of device, poor learning environment, technophobia, delayed response and incapability of the teacher to handle efficiently the material and communication machineries.

Due to time restriction and the COVID-19 pandemic, the study was constrained only to Dhaka region in Bangladesh.

The outcomes of the work can be supportive to the governing bodies and proprietors of the higher schooling organizations who are forecasting to adopt online education as a consistent movement in the future.

At last, based on outcomes, investigators have presented some recommendations that can be taken into consideration at policy level. The study would help universities to comply with the pressing need to impart experiential learning through online education during the COVID-19 pandemic.

This research aims to evaluate the accuracy of several Value-at-Risk (VaR) approaches for determining the Minimum Capital Requirement (MCR) for Islamic stock markets during the pandemic health crisis.

This research evaluates the performance of numerous VaR models for computing the MCR for market risk in compliance with the Basel II and Basel II.5 guidelines for ten Islamic indices. Five models were applied—namely the RiskMetrics, Generalized Autoregressive Conditional Heteroskedasticity, denoted (GARCH), fractional integrated GARCH, denoted (FIGARCH), and SPLINE-GARCH approaches—under three innovations (normal (N), Student (St) and skewed-Student (Sk-t) and the extreme value theory (EVT).

The main findings of this empirical study reveal that (1) extreme value theory performs better for most indices during the market crisis and (2) VaR models under a normal distribution provide quite poor performance than models with fat-tailed innovations in terms of risk estimation.

Since the world is now undergoing the third wave of the COVID-19 pandemic, this study will not be able to assess performance of VaR models during the fourth wave of COVID-19.

The results suggest that the Islamic Financial Services Board (IFSB) should enhance market discipline mechanisms, while central banks and national authorities should harmonize their regulatory frameworks in line with Basel/IFSB reform agenda.

Previous studies focused on evaluating market risk models using non-Islamic indexes. However, this research uses the Islamic indexes to analyze the VaR forecasting models. Besides, they tested the accuracy of VaR models based on traditional GARCH models, whereas the authors introduce the Spline GARCH developed by Engle and Rangel (2008). Finally, most studies have focus on the period of 2007–2008 financial crisis, while the authors investigate the issue of market risk quantification for several Islamic market equity during the sanitary crisis of COVID-19.