Performance features and mortality prediction of the 4C Score early in COVID-19 infection: a retrospective study in Saudi Arabia.

The ISARIC4C consortium developed and internally validated the 4C Score for prediction of mortality only in hospitalized patients. We aimed to assess the validity of the 4C Score in mortality prediction of patients with COVID-19 who had been home isolated or hospitalized.This retrospective cross-sectional study was performed after the first wave of COVID-19. Data of all PCR-positive COVID-19 patients who had been discharged, hospitalized, or died were retrospectively analyzed. Patients were classified into four risk groups according to the 4C Mortality Score. A total of (506) patients were classified as follows: low (57.1%), intermediate (27.9%), high (13%), and very high (2%) risk groups. Clinical, radiological, and laboratory data were significantly more severe in the high and very high-risk groups compared with other groups (p<0.001 for all). Mortality rate was correctly estimated by the model with 71% sensitivity, 88.6% specificity, and area under the curve of 0.9. The mortality rate was underestimated among the very high-risk group (66.2% vs 90%). The odds of mortality were significantly greater in the presence of hypoxia (OR 2.6, 95% CI 1.5 to 4.6, p<0.001) and high respiratory rate (OR 5.3, 95% CI 1.6 to 17.9, p<0.007), C reactive protein (CRP) (OR 3.5, 95% CI 1.8 to 6.8, p<0.001), and blood urea nitrogen (BUN) (OR 1.9, 95% CI 1.3 to 3.1, p<0.002). Other components of the model had non-significant predictions. In conclusion, the 4C Mortality Score has good sensitivity and specificity in early risk stratification and mortality prediction of patient with COVID-19. Within the model, only hypoxia, tachypnea, high BUN, and CRP were the independent mortality predictors with the possibility of overlooking other important predictors.

Novel coronavirus disease-related knowledge, attitudes, and practices among the residents of Al-Jouf region in Saudi Arabia.

INTRODUCTION: Efforts have been made to contain COVID-19. Human behavior, affected by knowledge and perceptions, may influence the course of disease. METHODOLOGY: A structured questionnaire was used to collect data from 422 participants. It consisted of 28 questions in four sections; seven questions about sociodemographic characteristics of participants, 12 questions to estimate level of knowledge about COVID-19, six questions to evaluate attitudes toward disease, and three questions to assess practices to prevent disease transmission. RESULTS: Their overall understanding of COVID-19 was satisfactory. 69% of the participants had satisfactory levels of knowledge, and the main sources of information were social media platforms (79.70%) and television (70.90%). There was a significant difference in knowledge as a function of gender (p = 0.50), occupation (p = 0.012), and smoking (p = 0.041). The participants held optimistic attitudes and adopted appropriate protective measures. Most participants agreed that COVID-19 can cause death (64.7%), poses greater risks to elderly (93.4%) and those with chronic diseases (96.7%), it is mandatory to quarantine infected individuals (98.1%), preventive health measures are important (97.6%), and health authorities will succeed in controlling the pandemic (67.5%). There was a statistically significant association between satisfactory levels of knowledge and the practice of wearing masks and the adoption of protective measures (avoiding crowded places, frequent hand washing). CONCLUSIONS: Residents of Al-Jouf region in Saudi Arabia have satisfactory levels of knowledge, optimistic attitudes, and good practice during the rapid rise period of the pandemic. Awareness campaigns will improve any misbeliefs and risky behaviors.

Evaluate the impact of COVID-19 prevention policies on supply chain aspects under uncertainty.

Supply chain operations are disrupted due to natural disasters or epidemics. In the recent period, the supply chain suffers from obstacles and major challenges that affect its stages directly due to the spread of the COVID-19 epidemic around the world. The impact of this epidemic on supply chain performance is clear in terms of supply, demand, or logistics. This epidemic is characterized by a rapid spread, so countries have taken preventive policies in an attempt to limit its spread. These policies are direct impacts on the performance of the supply chain in all scopes. The extent of its impact varies from one supply chain to another, according to the activities that the supply chain provides. In order to provide a more accurate study of the impact of the measures taken to limit the spread of the epidemic, this research presents a proposed framework that evaluates the impact of these policies on the three main aspects of the supply chain (supply, demand, and logistics). The proposed framework is build using BWM and TOPSIS based on plithogenic set. Plithogenic set provides a more accurate evaluation result that addresses the uncertainty problem. Supply chain aspects were evaluated for the food industry, electronics industry, pharmaceutical industry, and textile industry.

A model for the effective COVID-19 identification in uncertainty environment using primary symptoms and CT scans.

The rapid spread of the COVID-19 virus around the world poses a real threat to public safety. Some COVID-19 symptoms are similar to other viral chest diseases, which makes it challenging to develop models for effective detection of COVID-19 infection. This article advocates a model to differentiate between COVID-19 and other four viral chest diseases under uncertainty environment using the viruses primary symptoms and CT scans. The proposed model is based on a plithogenic set, which provides higher accurate evaluation results in an uncertain environment. The proposed model employs the best-worst method (BWM) and the technique in order of preference by similarity to ideal solution (TOPSIS). Besides, this study discusses how smart Internet of Things technology can assist medical staff in monitoring the spread of COVID-19. Experimental evaluation of the proposed model was conducted on five different chest diseases. Evaluation results demonstrate that the proposed model effectiveness in detecting the COVID-19 in all five cases achieving detection accuracy of up to 98%.