Parental Willingness for COVID-19 Vaccination among Children Aged 5 to 11 Years in Riyadh City, Saudi Arabia: A Cross-Sectional Study.

To manage the COVID-19 outbreak, the WHO recommends adult and child vaccination. Vaccine skepticism has been a major worldwide health concern for decades, and the situation is worsening. The primary purpose of this study was to investigate parental willingness to vaccinate their children (aged 5 to 11 years) against COVID-19 and to describe its relationship with attitude, barriers, facilitators, and sources of knowledge regarding the vaccine. Methods: From February to March 2022, a community-based cross-sectional survey was undertaken among the parents of Riyadh city, Saudi Arabia. We employed a convenient sampling procedure to gather the required sample. Using the Raosoft sample size calculator, a minimum sample size of 385 was determined based on a 95% confidence level, a 5% margin of error, and a 5% precision level. The data were analyzed using version 26 of SPSS. A p-value less than 0.05 was judged statistically significant. The Chi-square test and likelihood ratio were utilized to describe the relationship between socio-demographic characteristics, driving factors, and COVID-19 vaccine hesitancy. Vaccine hesitancy associated factors were identified using multivariate binary logistic regression. A total of 528 replies were received. The majority of respondents were mothers (77.7%), aged 26 to 40 years (67.8%), married (91.5%), Saudi nationals (96.2%), college graduates (70.6%), with a monthly family income of more than SAR 10,000 (46.4%), non-healthcare professionals (84.7%), employed in the government sector (33.7%), with three children (23.3%), and children aged 5 to 11 years (88.7%). A little more than half of the parents (55.7%) exhibited considerable vaccination hesitancy. About 16.28% of parents were willing to vaccinate their children as soon as possible, compared to 38.44% who had no interest whatsoever in vaccination. A greater proportion of mothers and unemployed parents were unwilling to vaccinate their children. Parents with a higher monthly income (above SAR 10,000), who worked as healthcare professionals, and whose children suffered from chronic conditions were significantly more ready to vaccinate their children against COVID-19. Parents who were aware of anti-vaccination campaigns and who vaccinated their children with required childhood vaccines were also much more likely to vaccinate their children against COVID-19. Most parents (66.9%) obtained information on COVID-19 via the Saudi Ministry of Health website, followed by social media (48.1%). The vaccine's novelty and the dearth of reliable information about its safety (65%) and insufficient information about its effectiveness (36.2%) were the primary reasons for not vaccinating children against COVID-19, whereas preventing children from contracting COVID-19 (55.9%) and government mandate (38.8%) were the primary reasons for vaccinating children against COVID-19. Conclusions: There was significant parental hesitancy to immunize their children against COVID-19. To involve and educate parents, multi-component interventions must be developed and implemented.

Deep Learning Based Intelligent Classification of Covid-19 Pneumonia Using Cough Auscultations

The World Health Organization has designated COVID-19 a pandemic because its emergence has influenced more than 50 million world's population. Around 14 million deaths have been reported worldwide from COVID-19. In this research work, we have presented a method for autonomous screening of COVID-19 and Pneumonia subjects from cough auscultation analysis. Deep learning-based model (MobileNet v2) is used to analyze a 6757 self-collected cough dataset. The experimentation has demonstrated the efficiency of the proposed technique in distinguishing between COVID-19 and Pneumonia. The results have demonstrated the cumulative accuracy of 99.98%, learning rate of 0.0005 and validation loss of 0.0028. Furthermore, cough analysis can be performed for other patients screening of other pulmonary abnormalities. © 2021 IEEE.

Classification of Pulmonary Viruses X-ray and Detection of COVID-19 Based on Invariant of Inception-V 3 Deep Learning Model

Coronavirus (COVID-19) is a catastrophic illness that has already infected several million individuals and caused thousands of fatalities globally. Any technical technique that enables quick testing of the COVID-19 with high accuracy might be essential for healthcare providers. X-ray imaging is an easily available technique that might be a great option for its quick detection. This research was conducted to examine the usefulness of artificial intelligence (AI) to detect COVID-19 quickly and accurately from chest X-ray scans. The objective of this study is to provide a solid technical method for the automatic identification of COVID-19, Pneumonia, Lung opacity, and Normal digital X-ray scans using pretrained, deep learning algorithms while optimizing detection accuracy. Inception v3 with an additional added dense layer is used with image augmentation to train and validate the selected dataset. The obtained accuracy of 99.72% promises speedy detection of COVID-19. © 2021 IEEE.

The psychological and emotional impact of coronavirus disease on COVID-19 patients in Najran Province, Saudi Arabia: An exploratory study.

BACKGROUND: The COVID-19 pandemic contributed to a significant mental health crisis and caused a widening economic crisis, growing financial loss, and numerous uncertainties. This pandemic brought alarming implications and overall increased risk for psychiatric illness. This study explores the psychological impact experienced by patients who tested positive from coronavirus in the Najran region, Saudi Arabia. DESIGN AND METHODS: This exploratory analysis included 210 COVID-19 positive patients. The study was conducted during a six-month period starting from March to September 2020, in two tertiary government hospitals in Najran, Saudi Arabia. Samples were selected using purposive sampling; survey questionnaire and face-to-face interview to collect the data. Statistical data were calculated using IBM SPSS v. 2.0 to compute the following statistical formulas: percentage distribution, mean, standard deviation, and Chi-square test of independence. RESULTS: The findings of this study revealed that the majority of COVID-19 positive patients were middle-aged adults (n=98 or 46.7%), male (n=178 or 84.8%), and were non-Saudi nationals (n=132 or 62.9%). It was found out that COVID-19 patients experienced bothersome behaviour at a very high level (x̅=2.63±0.6734). Meanwhile, depression (x̅=2.51±0.7070), worry (x̅=2.23±0.8811), and anxiety (x̅=2.21±0.8719) was only at a high level. CONCLUSIONS: The study revealed that the majority of participants had high levels of depression, anxiety and bothersome behaviours. However, demographic characteristics like age, sex, and nationality were not significantly related to coronavirus patients' psychological health problems. Assessments and interventions for psychosocial concerns, integration of mental health considerations, and treatment for severe psychosocial consequences must be administered in COVID-19 care facilities.