Background E‐learning has been adopted internationally as the alternative teaching or learning strategy during the coronavirus disease 2019 (COVID‐19) quarantine to fill the academic gap that has been created by the existing reality of the pandemic due to nationwide closures. Aim This study aims at evaluating the prevalence of depression, anxiety, and stress symptomatology among Lebanese University Students during the COVID‐19 Quarantine. Methodology A quantitative cross sectional research design, where 520 undergraduate university students were surveyed regarding their satisfaction with e‐learning and the prevalence of depression, anxiety, and stress symptomatology using depression, anxiety, and stress scale‐21 elements. Results Learning through online platforms have given rise to depression and anxiety disorders among undergraduate university students, where there was a significant correlation between student satisfaction and prevalence of depression, anxiety, and stress. Conclusion The sudden shift to exclusive e‐learning methods of instruction have produced anxiety and depression symptoms among a significant portion of the students due to the stressful load of work required.

Background: Since the outbreak of the novel Coronavirus (COVID-19), health care professionals in Lebanon have been diligently serving as the frontline of defense. In the light of challenging economic and political circumstances, putting their community wellbeing as a priority, and abiding by quarantine and strict infection control measures, health care professionals risk both their physical and mental wellbeing. Objective: The aim of this study is to explore the psychosocial effects of being quarantined following exposure to COVID-19 among Lebanese health care professionals. Method: An exploratory qualitative research design was employed, where semi-structured interviews were carried out involving a sample of 13 Lebanese health care providers working at various COVID-19 units. Results: The qualitative analysis has revealed four themes namely ‘Fears of contracting and spreading the virus’, ‘Conflict between professional duty and family obligation’, ‘Stigma of being infected’, and ‘Inadequate or inaccurate information’. Conclusion: COVID-19 quarantine has been posing intense psychological challenges among Lebanese health care workers which are worsened at times by the economic instability; thus, health care policymakers are urged to take proper action nationwide to alleviate longlisting implications and support the health care providers in fulfilling their mission.

Background: In the light of the global spread of the novel Coronavirus known as COVID-19 and in the absence of an approved treatment and vaccination, Lebanon has taken national measures, among which was home quarantine of the general public in an attempt to flatten the epidemic curve and avoid flooding the health care system. Aim: This study aimed at evaluating the prevalence of post-traumatic stress symptomatology (PTSS) during the times of COVID-19 quarantine among Lebanese citizens. Method: This quantitative cross-sectional study recruited 950 civilians and is aimed at measuring the prevalence of PTSS among the Lebanese citizens at an interval of 2 weeks and 1 month of COVID-19 quarantine. Results: The results have shown that quarantine in Lebanon has started to give rise to Post-traumatic Stress Disorder symptomatology during the second week which was worsened in the fourth week of COVID-19 quarantine. Conclusion: COVID-19 quarantine has influenced the psychology of Lebanese citizens and might have persistent effects after the end of this phase which is recommended to be explored.

Background: The unprecedented global spread of coronavirus disease 2019 (COVID-19) has imposed huge challenges on the healthcare facilities, and impacted every aspect of life. This has led to the development of several vaccines against COVID-19 within one year. This study aimed to assess the attitudes and the side effects among Arab communities after receiving a COVID-19 vaccine and use of machine learning (ML) tools to predict post-vaccination side effects based on predisposing factors. Methods: An online-based multinational survey was carried out via social media platforms from 14 June to 31 August 2021, targeting individuals who received at least one dose of a COVID-19 vaccine from 22 Arab countries. Descriptive statistics, correlation, and chi-square tests were used to analyze the data. Moreover, extensive ML tools were utilized to predict 30 post vaccination adverse effects and their severity based on 15 predisposing factors. The importance of distinct predisposing factors in predicting particular side effects was determined using global feature importance employing gradient boost as AutoML. Results: A total of 10,064 participants from 19 Arab countries were included in this study. Around 56% were female and 59% were aged from 20 to 39 years old. A high rate of vaccine hesitancy (51%) was reported among participants. Almost 88% of the participants were vaccinated with one of three COVID-19 vaccines, including Pfizer-BioNTech (52.8%), AstraZeneca (20.7%), and Sinopharm (14.2%). About 72% of participants experienced post-vaccination side effects. This study reports statistically significant associations (p < 0.01) between various predisposing factors and post-vaccinations side effects. In terms of predicting post-vaccination side effects, gradient boost, random forest, and XGBoost outperformed other ML methods. The most important predisposing factors for predicting certain side effects (i.e., tiredness, fever, headache, injection site pain and swelling, myalgia, and sleepiness and laziness) were revealed to be the number of doses, gender, type of vaccine, age, and hesitancy to receive a COVID-19 vaccine. Conclusions: The reported side effects following COVID-19 vaccination among Arab populations are usually non-life-threatening; flu-like symptoms and injection site pain. Certain predisposing factors have greater weight and importance as input data in predicting post-vaccination side effects. Based on the most significant input data, ML can also be used to predict these side effects; people with certain predicted side effects may require additional medical attention, or possibly hospitalization.