Measuring the impact of suspending Umrah, a global mass gathering in Saudi Arabia on the COVID-19 pandemic.

Since the early days of the coronavirus (COVID-19) outbreak in Wuhan, China, Saudi Arabia started to implement several preventative measures starting with the imposition of travel restrictions to and from China. Due to the rapid spread of COVID-19, and with the first confirmed case in Saudi Arabia in March 2019, more strict measures, such as international travel restriction, and suspension or cancellation of major events, social gatherings, prayers at mosques, and sports competitions, were employed. These non-pharmaceutical interventions aim to reduce the extent of the epidemic due to the implications of international travel and mass gatherings on the increase in the number of new cases locally and globally. Since this ongoing outbreak is the first of its kind in the modern world, the impact of suspending mass gatherings on the outbreak is unknown and difficult to measure. We use a stratified SEIR epidemic model to evaluate the impact of Umrah, a global Muslim pilgrimage to Mecca, on the spread of the COVID-19 pandemic during the month of Ramadan, the peak of the Umrah season. The analyses shown in the paper provide insights into the effects of global mass gatherings such as Hajj and Umrah on the progression of the COVID-19 pandemic locally and globally.

Deep Learning Approaches for Detecting COVID-19 From Chest X-Ray Images: A Survey.

Chest X-ray (CXR) imaging is a standard and crucial examination method used for suspected cases of coronavirus disease (COVID-19). In profoundly affected or limited resource areas, CXR imaging is preferable owing to its availability, low cost, and rapid results. However, given the rapidly spreading nature of COVID-19, such tests could limit the efficiency of pandemic control and prevention. In response to this issue, artificial intelligence methods such as deep learning are promising options for automatic diagnosis because they have achieved state-of-the-art performance in the analysis of visual information and a wide range of medical images. This paper reviews and critically assesses the preprint and published reports between March and May 2020 for the diagnosis of COVID-19 via CXR images using convolutional neural networks and other deep learning architectures. Despite the encouraging results, there is an urgent need for public, comprehensive, and diverse datasets. Further investigations in terms of explainable and justifiable decisions are also required for more robust, transparent, and accurate predictions.

Tb-SAC: Topic-based and Sentiment Classification for Saudi Dialects Tweets

Recently, sentiment analysis has received a lot of attention from researchers in text mining and data analysis. The studies have significantly expanded to include different languages from several sources that were employed to create a corpus to serve researchers in various shapes, sizes, and purposes. Locally, a lot of effort is spent on analyzing sentiment for Arabic texts, for both Modern Standard Arabic (MSA) and vernacular dialects. However, the researches concerned with creating a corpus based on the topic was relatively few. In this paper, we present Tb-SAC as extracted corpora from Twitter, especially from Saudi dialects. The corpus contains 4301 tweets, which labeled based on sentiments using a three-point scale: positive, negative, and neutral. The corpus classify based on tweet topics into five main topics obtained from analyzing the gold set with 200 tweets. The topics were Personal, Religion, Coronavirus, Entertainment, Other (Education, Economy, Sport, Food, Health, Social Media, Distance Working, Technology, Comedy, and Politics). Then, we performed the annotation process manually, besides applying eleven different classification models and validate the corpus by cross-validation model.