Design of Uninterrupted Oxygen Supply for Field Hospital in Jordan

Field hospitals were a great help in global pandemics and catastrophes such as earthquakes and the spread of airborne viruses. This study focused on the design of an interrupted oxygen supply since continuous oxygen provision for covid-19 patients is a huge problem facing field hospitals around the world, three methods to avoid any oxygen supply interruptions are discussed, where the outlet of the oxygen concentrator is lowered to 4.5 bar, and the outlet of the liquid oxygen vaporizer is regulated at 4.25 bar, and the outlet of the oxygen cylinders is set to 4 bars, a final one-way valve connecting the three lines of oxygen which are set to 4 bars. © 2022 IEEE.

Clinical characteristics and risk factors of patients with severe COVID-19 in Riyadh, Saudi Arabia: A retrospective study

Background: COVID-19 is newly emerging infectious disease that spread globally at unpredictable and unique pattern to the extent that the World Health Organization announced COVID-19 as a pandemic in the first couple months of 2020. This study aims to describe clinical and demographic features of COVID-19 patients and the influence of various risk factors on the severity of disease. Methods: This research is a retrospective study based on Saudi Arabia's ministry of health's Covid-19 data. The analysis relies on data of all COVID-19 patients recorded in Riyadh between 1st, March 2020 and 30th, July 2020. Statistical analyses were performed to investigate the effect of demographic characteristic, clinical presentation, and comorbidities on infection severity. Results: A total number of 1026 COVID-19 patients were identified based on the demographic data as follows: 709 cases (69% of cases) were males and 559 cases (54% of cases) were Saudi. Most of patients were diagnosed with mild signs and symptoms 697 (68% of cases), while 164 patient (16% of cases) demonstrated moderate signs and symptoms, and 103 cases (10%) were severe and 62 (6%) had critical febrile illness. Fever, cough, sore throat, and shortness of breath were the most common symptoms among patients with COVID-19. Among studied comorbidities in COVID-19 patients, diabetes mellitus and hypertension were the most prevalent. The results from the bivariate logistic regression analysis revealed that older age, diabetes mellitus, asthma, smoking, and fever are associated with severe or critically ill cases. Conclusion: The findings of this study show that old age, fever, and comorbidities involving diabetes mellitus, asthma, and smoking were significantly associated with infection severity. (c) 2021 The Author(s). Published by Elsevier Ltd on behalf of King Saud Bin Abdulaziz University for Health Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Public Response, Anxiety and Behaviour during the First Wave of COVID-19 Pandemic in Saudi Arabia

This study aims to investigate public response attitude, anxiety, practices and trust in the authorities' mitigation plan during the first wave of COVID-19 pandemic. A national cross sectional phone survey was conducted among Saudi residents aged 16 years and above. A total of 90,421 (45.2%) individuals participated in the study. Of those, the overall rate of COVID-19 correct knowledge was 82% (mean: 9.84);social media was the most reported source of knowledge. Younger age, low levels of education and foreign residents were associated with poor knowledge. Overall, 49.5% scored 5 or more on the GAD-7 test, indicating anxiety symptoms, 19.2% of them scored 10 and above, suggesting moderate to severe anxiety. Majority of participants (>78%) trusted and supported the interventions implemented by the government to control COVID-19. Social distancing practices among participants was as following, 72.5% stayed at home and avoid going out for nonessential business and 49.5% avoided attending social events and family gatherings. Trust in authorities, being anxious, worry and levels of knowledge about the disease, were the most common factors affecting adoption of the recommended practices. Continuous evaluation of public response about COVID-19, and the effectiveness of protective measures is essential to better inform policy-makers and identify ways of encouraging behaviour change among public during pandemic.

Deep Learning and Holt-Trend Algorithms for Predicting Covid-19 Pandemic

The Covid-19 epidemic poses a serious public health threat to the world, where people with little or no pre-existing human immunity can be more vulnerable to its effects. Thus, developing surveillance systems for predicting the Covid-19 pandemic at an early stage could save millions of lives. In this study, a deep learning algorithm and a Holt-trend model are proposed to predict the coronavirus. The Long-Short Term Memory (LSTM) and Holt-trend algorithms were applied to predict confirmed numbers and death cases. The real time data used has been collected from the World Health Organization (WHO). In the proposed research, we have considered three countries to test the proposed model, namely Saudi Arabia, Spain and Italy. The results suggest that the LSTM models show better performance in predicting the cases of coronavirus patients. Standard measure performance Mean squared Error (MSE), Root Mean Squared Error (RMSE), Mean error and correlation are employed to estimate the results of the proposed models. The empirical results of the LSTM, using the correlation metrics, are 99.94%, 99.94% and 99.91% in predicting the number of confirmed cases in the three countries. As far as the results of the LSTM model in predicting the number of death of Covid-19, they are 99.86%, 98.876% and 99.16% with respect to Saudi Arabia, Italy and Spain respectively. Similarly, the experiment's results of the Holt-Trend model in predicting the number of confirmed cases of Covid-19, using the correlation metrics, are 99.06%, 99.96% and 99.94%, whereas the results of the Holt-Trend model in predicting the number of death cases are 99.80%, 99.96% and 99.94% with respect to the Saudi Arabia, Italy and Spain respectively. The empirical results indicate the efficient performance of the presented model in predicting the number of confirmed and death cases of Covid-19 in these countries. Such findings provide better insights regarding the future of Covid-19 this pandemic in general. The results were obtained by applying time series models, which need to be considered for the sake of saving the lives of many people.