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This paper aims to analyze the socioeconomic impacts of infectious diseases based on uncertain behaviors of social and effective subsystems in the countries. The economic impacts of infectious diseases in comparison with predicted gross domestic product (GDP) in future years could be beneficial for this aim along with predicted social impacts of infectious diseases in countries.

The proposed uncertain SEIAR (susceptible, exposed, infectious, asymptomatic and removed) model evaluates the impacts of variables on different trends using scenario base analysis. This model considers different subsystems including healthcare systems, transportation, contacts and capacities of food and pharmaceutical networks for sensitivity analysis. Besides, an adaptive neuro-fuzzy inference system (ANFIS) is designed to predict the GDP of countries and determine the economic impacts of infectious diseases. These proposed models can predict the future socioeconomic trends of infectious diseases in each country based on the available information to guide the decisions of government planners and policymakers.

The proposed uncertain SEIAR model predicts social impacts according to uncertain parameters and different coefficients appropriate to the scenarios. It analyzes the sensitivity and the effects of various parameters. A case study is designed in this paper about COVID-19 in a country. Its results show that the effect of transportation on COVID-19 is most sensitive and the contacts have a significant effect on infection. Besides, the future annual costs of COVID-19 are evaluated in different situations. Private transportation, contact behaviors and public transportation have significant impacts on infection, especially in the determined case study, due to its circumstance. Therefore, it is necessary to consider changes in society using flexible behaviors and laws based on the latest status in facing the COVID-19 epidemic.

The proposed methods can be applied to conduct infectious diseases impacts analysis.

In this paper, a proposed uncertain SEIAR system dynamics model, related sensitivity analysis and ANFIS model are utilized to support different programs regarding policymaking and economic issues to face infectious diseases. The results could support the analysis of sensitivities, policies and economic activities.

• A new system dynamics model is proposed in this paper based on an uncertain SEIAR model (Susceptible, Exposed, Infectious, Asymptomatic, and Removed) to model population behaviors;

• Different subsystems including healthcare systems, transportation, contacts, and capacities of food and pharmaceutical networks are defined in the proposed system dynamics model to find related sensitivities;

• Different scenarios are analyzed using the proposed system dynamics model to predict the effects of policies and related costs. The results guide lawmakers and governments' actions for future years;

• An adaptive neuro-fuzzy inference system (ANFIS) is designed to estimate the gross domestic product (GDP) in future years and analyze effects of COVID-19 based on them;

• A real case study is considered to evaluate the performances of the proposed models.

A new system dynamics model is proposed in this paper based on an uncertain SEIAR model (Susceptible, Exposed, Infectious, Asymptomatic, and Removed) to model population behaviors;

Different subsystems including healthcare systems, transportation, contacts, and capacities of food and pharmaceutical networks are defined in the proposed system dynamics model to find related sensitivities;

Different scenarios are analyzed using the proposed system dynamics model to predict the effects of policies and related costs. The results guide lawmakers and governments' actions for future years;

An adaptive neuro-fuzzy inference system (ANFIS) is designed to estimate the gross domestic product (GDP) in future years and analyze effects of COVID-19 based on them;

A real case study is considered to evaluate the performances of the proposed models.

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.

Students pursuing different professional courses at the higher education level during 2021–2022 saw the first-time occurrence of a pandemic in the form of coronavirus disease 2019 (COVID-19), and their mental health was affected. Many works are available in the literature to assess mental health severity. However, it is necessary to identify the affected students early for effective treatment.

Predictive analytics, a part of machine learning (ML), helps with early identification based on mental health severity levels to aid clinical psychologists. As a case study, engineering and medical course students were comparatively analysed in this work as they have rich course content and a stricter evaluation process than other streams. The methodology includes an online survey that obtains demographic details, academic qualifications, family details, etc. and anxiety and depression questions using the Hospital Anxiety and Depression Scale (HADS). The responses acquired through social media networks are analysed using ML algorithms – support vector machines (SVMs) (robust handling of health information) and J48 decision tree (DT) (interpretability/comprehensibility). Also, random forest is used to identify the predictors for anxiety and depression.

The results show that the support vector classifier produces outperforming results with classification accuracy of 100%, 1.0 precision and 1.0 recall, followed by the J48 DT classifier with 96%. It was found that medical students are affected by anxiety and depression marginally more when compared with engineering students.

The entire work is dependent on the social media-displayed online questionnaire, and the participants were not met in person. This indicates that the response rate could not be evaluated appropriately. Due to the medical restrictions imposed by COVID-19, which remain in effect in 2022, this is the only method found to collect primary data from college students. Additionally, students self-selected themselves to participate in this survey, which raises the possibility of selection bias.

The responses acquired through social media networks are analysed using ML algorithms. This will be a big support for understanding the mental issues of the students due to COVID-19 and can taking appropriate actions to rectify them. This will improve the quality of the learning process in higher education in Oman.

Furthermore, this study aims to provide recommendations for mental health screening as a regular practice in educational institutions to identify undetected students.

Comparing the mental health issues of two professional course students is the novelty of this work. This is needed because both studies require practical learning, long hours of work, etc.

This study, a narrative literature review, aims to examine the combined benefits of the active and passive use of social media (SM) for well-being (WB), physical and mental health during the COVID-19 pandemic.

A search strategy has been carried out in the databases: Riss, PubMed, Medline, Scopus and Google Scholar, including all the articles published until 19 October 2023.

SM offers various benefits, including global risk awareness, health information, social connections and support. With the natural increase in physical inactivity due to COVID-19 social restrictions, SM has been identified as an appropriate tool for promoting physical activity (PA) at home to improve health.

It suggests that the combined use of active and passive benefits of SM could potentially play an important role in public health by increasing individuals’ health behaviours. In addition, dissemination, sharing and social interaction of information provided by YouTube can encourage healthy behaviours, contribute to WB, physical and mental health and raise public health awareness.

The findings presented in this study highlight the combined benefits of differentiating the features of SM use. Compared to other SM platforms, YouTube can be used as a useful tool for home-based PA that promotes health by enabling people to remain active and avoid barriers to PA due to social restrictions during the global crisis. In addition, some recommendations from the findings may help protect against potential risks and improve public health outcomes during global crises, such as the COVID-19 pandemic, among the general public using SM.

Even with the Saudi Arabian Government's discretionary measures to mitigate the spread of the coronavirus disease 2019 (COVID-19), the economic sectors were not spared from the damage. Thus, the paper aims to use a computable general equilibrium (CGE) model to evaluate the impact of COVID-19 on the Kingdom of Saudi Arabia's (KSA) economy, with a special focus on small and medium enterprises (SMEs) and production. These influence the level of poverty.

The paper adopted the social accounting matrix (SAM) for Saudi Arabia built in 2021 by Imtithal Althumairi from Saudi Arabia's 2017 SAM. The model represents a snapshot of the economy and different flows that exist within the tasks and institutions. Two simulations (mild and severe) were conducted because of the focus on the distributional outcomes.

Decrease in job creation and economic growth were significant evidence from the study's findings. Findings show that more families hit below the poverty line because the negative impacts of the pandemic have shifted the income allocation curve. Findings show that the weakest of the poor are mitigated by government social grants during the pandemic.

The paper is restricted to the relevant literature relating to the impact of COVID-19 on Saudi Arabia's economy and evaluated using the SAM model. Moreover, the COVID-19 is still an ongoing scenario; thus, the model should be updated as data utilised for the operationalisation are made available.

The information from the suggested model can be suitable to measure the degree of the harm, and thus, the likely extent of the desirable policy feedback. Also, the model can be updated, as data are made available and formulated policies based on the updated data implemented by the policymakers.

Apart from the recovery planning of SMEs during the pandemic, the paper intends to stir up Saudi Arabia's policymakers through the macro-micro model to recovery planning and resilience of the economy with emphasis on mitigating unemployment.

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.

The COVID-19 outbreak reached a critical stage when it became imperative for public health systems to act decisively and design potential behavioral operational strategies aimed at containing the pandemic. Isolation through social distancing played a key role in achieving this objective. This research study examines the factors affecting the intention of individuals toward social distancing in India.

A correlation study was conducted on residents from across Indian states (N = 499). Online questionnaires were floated, consisting of health belief model and theory of planned behavior model, with respect to social distancing behavior initially. Finally, structural equation modeling was used to test the hypotheses.

The results show that perceived susceptibility (PS), facilitating conditions (FC) and subjective norms are the major predictors of attitude toward social distancing, with the effect size of 0.277, 0.132 and 0.551, respectively. The result also confirms that the attitude toward social distancing, perceived usefulness of social distancing and subjective norms significantly predict the Intention of individuals to use social distancing with the effect size of 0.355, 0.197 and 0.385, respectively. The nonsignificant association of PS with social distancing intention (IN) (H1b) is rendering the fact that attitude (AT) mediates the relationship between PS and IN; similarly, the nonsignificant association of FC with IN (H5) renders the fact that AT mediates the relationship between FC and IN.

The results of the study are helpful to policymakers to handle operations management of nudges like social distancing.

The research is one of its kind that explores the behavioral aspects of handling social nudges through FC.

This study aims to explore the long- and short-run effects of daily confirmed cases of COVID-19 (C_t) on daily stock returns (R_t) for Kuwait. This is the first study that was applied to the case of Kuwait.

We employed the autoregressive distributed lag (ARDL) model of Pesaran et al. (2001) and the nonlinear autoregressive distributed lag (NARDL) model of Shin et al. (2001) for daily data over the period March 2020 to August 2021.

The findings first document the existence of a long-run relationship (cointegration). Second, the findings of the ARDL model show a significant positive long-run effect of daily confirmed cases of COVID-19 (C_t) on daily stock returns (R_t) but a significant negative short-run effect. As for the NARDL model, the findings showed that the increase and decrease of daily confirmed cases of COVID-19 (Ct−1+,Ct−1−) have symmetric long-run effects on daily stock returns but asymmetric short-run effects. Finally, the vector error correction model causality test shows significant long- and short-run unidirectional causality running from daily confirmed cases of COVID-19 (C_t) to daily stock returns (R_t).

To the best of the author’s knowledge, this is the first study that was applied to the case of Kuwait.

The objective of this study was to investigate the impacts of personality traits and the ability to detect fake news on information avoidance behavior. It also examined the effect of personality traits on the ability to detect fake news.

The sample population included Shiraz University students who were studying in the second semester of academic year 2021 in different academic levels. It consisted of 242 students of Shiraz University. The Big Five theory was used as the theoretical background of the study. Moreover, the research instrument was an electronic questionnaire consisting of the three questionnaires of the ability to detect fake news (Esmaeili et al., 2019, inspired by IFLA, 2017), the Big Five personality traits (Goldberg, 1999) and information avoidance (Howell and Shepperd, 2016). The statistical methods used to analyze the data were Pearson correlation and stepwise regression, which were performed through SPSS software (version 26).

The results showed that from among the five main personality factors, only neuroticism had a positive and significant effect on information avoidance. In addition, the ability to detect fake news had a significant negative effect on information avoidance behavior. Further analyses also showed positive and significant effects of openness to experience and extraversion on the ability to detect fake news. In fact, the former had more predictive power.

Following the Big Five theory considering COVID-19 information avoidance and the ability to detect COVID-19 fake news, this study shifted the focus from environmental factors to personality factors and personality traits. Furthermore, this study introduced the ability to detect fake news as an influential factor in health information avoidance behaviors, which can be a prelude for new research studies.

The present study applied the five main personality factors theory in the context of information avoidance behavior and the ability to detect fake news, and supported the effect of personality traits on these variables.

This purpose of this study was to investigate the role of nurses’ resilience as an indicator of their mental health on sick leave absenteeism during the COVID-19 pandemic.

This descriptive-analytical study was conducted in 2020 to identify the predictors of absenteeism among 260 nurses working in two training hospitals delivering specialized services in the treatment of COVID-19 patients. Data was collected through the use of standard questionnaires including demographic information, nurses’ resilience, intention for job turnover and absenteeism from the workplace. To predict sick leave absenteeism, regression analyses were implemented.

Study results revealed that the most influencing features for predicting the probability of taking sick leave among nurses were marital status, tenacity, age, work experience and optimism. Logistic regression also depicted that nurses who had less faith in God or less self-control were more likely to take sick leave.

The resilience of nurses working in the COVID-19 pandemic was relatively low, which needs careful consideration to apply for organizational support. Main challenge that most of the health systems face include an inadequate supply of nurses which consequently lead to reduced efficiency, poor quality of care and decreased job performance. Thus, hospital managers need to put appropriate managerial interventions into practice, such as building a pleasant and healthy work environment, to improve nurses’ resilience in response to heavy workloads and stressful conditions.

To the best of the authors’ knowledge, this is the first study to examine such a relationship, thus contributing findings will provide a clear contribution to nursing management and decision-making processes. Resilience is an important factor for nurses who constantly face challenging situations in a multifaceted health-care system.