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Coronavirus disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2), is associated with significant morbidity and mortality. The clinical features of COVID-19 were mentioned in previous studies. However, risk factors for COVID-19 are not fully recognized. The aim of this study is to characterize risk factors and clinical features of COVID-19 disease in Jeddah, Saudi Arabia. A retrospective, chart-review, case-control study was conducted at King Abdulaziz University, Jeddah, Saudi Arabia. Demographic, clinical, radiological, and laboratory data on patients diagnosed between March 18 and May 18, 2020 were collected and analyzed. We reviewed medical records on 297 suspected cases of COVID-19. Of these, 175 (59%) tested positive for COVID-19 by polymerase chain reaction (PCR) and considered as cases, while 122 (41%) tested negative and considered as control. COVID-19 positive cases were more likely to be males, and non-health care providers. Hypertension (15%), diabetes (10%) and two or more concurrent comorbidities (54.4%) were more prevalent among COVID-19 patients. Patients presented with fever, cough, and loss of taste/smell were more likely to test positive for COVID-19 (P = 0.001, 0.008, 0.008; respectively). Radiological evidence of pneumonia was associated with confirmed COVID-19 disease (P = 0.001). Shortness of breath and gastrointestinal symptoms were not associated with the risk of COVID-19 at presentation. On admission, white blood cells, neutrophils, lymphocytes, eosinophils, basophils, and platelets were significantly lower among COVID-19 patients compared with controls. Surprisingly, D-Dimer levels were lower among COVID-19 positive patients when compared with controls. Male gender, hypertension, and diabetes are the most commonly observed risk factors associated with COVID-19 disease in Jeddah, Saudi Arabia. COVID-19 patient had significantly lower lymphocyte and neutrophil counts. [ABSTRACT FROM AUTHOR]

Social networks have generated immense amounts of data that have been successfully utilized for research and business purposes. The approachability and immediacy of social media have also allowed ill-intentioned users to perform several harmful activities that include spamming, promoting, and phishing. These activities generate massive amounts of low-quality content that often exhibits duplicate, automated, inappropriate, or irrelevant content that subsequently affects users' satisfaction and imposes a significant challenge for other social media-based systems. Several real-time systems were developed to tackle this problem by focusing on filtering a specific kind of low-quality content. In this paper, we present a fine-grained real-time classification approach to identify several types of low-quality tweets (i.e., phishing, promoting, and spam tweets) written in Arabic. The system automatically extracts textual features using deep learning techniques without relying on hand-crafted features that are often time-consuming to be obtained and are tailored for a single type of low-quality content. This paper also proposes a lightweight model that utilizes a subset of the textual features to identify spamming Twitter accounts in a real-time setting. The proposed methods are evaluated on a real-world dataset (40, 000 tweets and 1, 000 accounts), showing superior performance in both models with accuracy and F1-scores of 0.98. The proposed system classifies a tweet in less than five milliseconds and an account in less than a second. • This research shows that training a deep learning model on a dataset that includes several types of low-quality tweet can be an efficient solution to filter such content on a real-time setting. • Two embedding methods (word- and character-level) are compared for the task of classifying tweets in either a legitimate or low-quality class using a dataset (40,000 tweets) collected through this project. • We also show that Twitter account can be efficiently classified into spam or genuine profile using only the textual data of its recent tweets and a deep learning model. [ABSTRACT FROM AUTHOR]