The flexibility and relatively low cost of mobile devices make educational systems more accessible for learners and educators worldwide. When incorporated with the internet, it creates a better learning environment than the conventional classroom lecture. Many studies have been done to shed insight into the existing state of mobile learning (M-learning) studies. However, further research is needed into this topic at a specific time, i.e., during the COVID-19 pandemic. This study aims to retrieve, review, investigate, and critically assess the existing literature on M-learning that was conducted during the COVID-19 concerning our research theme. This study considered publications from four databases, narrowed our initial search results of 4056 articles down to 83 that are relevant to our research questions, and did an in-depth analysis based on the systematic review protocol. The findings explored the major focusing areas of M-learning applications, the regional sentiment of M-learning users, the determinants and perceptions of M-learning, as well as the benefits, challenges, and opportunities associated with M-learning. This systematic literature review (SLR) was performed to apportion a contribution toward an improved understanding of the basic principles that underpin the rethinking of M-learning applications for policymakers, online course designers, and blended learning facilitators.

Travel and tourism have been hard-hit by COVID-19, and people have been forced to cancel travel plans due to governments being forced to implement travel bans to curb infection dissemination. Therefore, this study investigates the influence of situational awareness for adopting health-protective behaviours such as postponed travel plans. We used the theory of situational awareness (SAT) coupled with the health belief model (HBM) to analyze health-related behaviors. Data from a cross-sectional survey of 705 Pakistani and international adults were analyzed using Structural Equation Modelling, during the peak of the COVID-19. The results confirmed that perceived understanding of COVID-19 cause influences perceived severity, understanding of contracting COVID-19 influences perceived susceptibility, and perceived understanding of COVID-19 prevention influences both perceived barriers and perceived benefits. The results further showed that perceived understanding of COVID-19 prevention is a strong predictor of self-efficacy, which ultimately influences health-protective behaviour. In addition, the sentiment analysis revealed that as COVID-19 continues to rise, people are becoming more sentimental and avoiding travel, even during festivals. The outcome of this study demonstrates that situational awareness has an effect on the postulates of health-protective measures and plays a key role in the implementation of protective measures such as canceling travel plans to ensure protection.

Forecasting disease outbreaks in real-time using time-series data can help for the planning of public health interventions. We used a support vector machine (SVM) model using epidemiological data provided by Johns Hopkins University Centre for Systems Science and Engineering (JHU CCSE), World Health Organization (WHO), and the Centers for Disease Control and Prevention (CDC) to predict upcoming records before the WHO made an official declaration. Our study, conducted on the time series data available from 22 January till 10 March 2020, revealed that COVID-19 was spreading at an alarming rate and progressing towards a pandemic. The initial insight that confirmed COVID-19 cases were increasing was because these received the highest number of effects for our selected dataset from 22 January to 10 March 2020, i.e., 126,344 (64%). The recovered cases were 68289 (34%), and the death rate was around 2%. Moreover, we classified the tweets from 22 January to 15 April 2020 into positive and negative sentiments to identify the emotions (stress or relaxed) posted by Twitter users related to the COVID-19 pandemic. Our analysis identified that tweets mostly conveyed a negative sentiment with a high frequency of words for #coronavirus and #lockdown amid COVID-19. However, these anxiety tweets are an alarm for healthcare authorities to devise plans accordingly.