COVID-19 Prediction Using LSTM Algorithm: GCC case study

Coronavirus-19 (COVID-19) is the black swan of 2020. Still, the human response to restrain the virus is also creating massive ripples through different systems, such as health, economy, education, and tourism. This paper focuses on research and applying Artificial Intelligence (AI) algorithms to predict COVID-19 propagation using the available time-series data and study the effect of the quality of life, the number of tests performed, and the awareness of citizens on the virus in the Gulf Cooperation Council (GCC) countries at the Gulf area. So we focused on cases in the Kingdom of Saudi Arabia (KSA), United Arab of Emirates (UAE), Kuwait, Bahrain, Oman, and Qatar. For this aim, we accessed the time-series real-datasets collected from Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). The timeline of our data is from January 22, 2020 to January 25, 2021. We have implemented the proposed model based on Long Short-Term Memory (LSTM) with ten hidden units (neurons) to predict COVID-19 confirmed and death cases. From the experimental results, we confirmed that KSA and Qatar would take the most extended period to recover from the COVID-19 virus, and the situation will be controllable in the second half of March 2021 in UAE, Kuwait, Oman, and Bahrain. Also, we calculated the root mean square error (RMSE) between the actual and predicted values of each country for confirmed and death cases, and we found that the best values for both confirmed and death cases are 320.79 and 1.84, respectively, and both are related to Bahrain. While the worst values are 1768.35 and 21.78, respectively, and both are related to KSA. On the other hand, we also calculated the mean absolute relative errors (MARE) between the actual and predicted values of each country for confirmed and death cases, and we found that the best values for both confirmed and deaths cases are 37.76 and 0.30, and these are related to Kuwait and Qatar respectively. While the worst values are 71.45 and 1.33, respectively, and both are related to KSA

Role of high resolution computed tomography (HRCT) of the chest in the diagnosis of lymphangioleiomyomatosis (LAM) – A serial study of 15 patients

AbstractAim of workTo highlight the characteristic high resolution computed tomography (HRCT) findings in 15 patients diagnosed with lymphangioleiomyomatosis (LAM), narrowing the wide range of ILD and allowing accurate diagnosis preventing unnecessary interventional procedures.Patients and methods15 female patients ranged in age from 17 to 55years (mean age=40.33years). ILD was suspected based on clinical examination and chest radiographs. They were referred to do HRCT chest for further assessment. A 64 MSCT scanner was used.ResultsAll patients showed bilateral multiple cysts showing upper lobar predominance in 13.3% of cases and lower lobar one in 6.7%. The size of the cysts ranged from few mms to 3cm with variable wall thickness. Pneumothorax was reported in three patients and pulmonary hypertension in 15 cases.ConclusionHRCT is a valued diagnostic tool for diagnosis of LAM showing characteristic features for the disease

The Socio-Demographic Characteristics Associated with Non-Communicable Diseases among the Adult Population of Dubai: Results from Dubai Household Survey 2019.

BACKGROUND: Non-communicable diseases (NCDs) are the leading causes of death worldwide. In the UAE, NCDs account for nearly 77% of all deaths. There is limited empirical research on this topic in the UAE. We aimed to examine the association of non-communicable diseases and the sociodemographic characteristics among the adult population of Dubai. METHODS: The study used secondary data from the Dubai Household Health Survey (DHHS), 2019. DHHS is a cross-sectional complex design, stratified by geographic area, and uses multistage probability sampling. In this survey, 2247 families were interviewed and only adults aged 18+ were included for the analysis. The quasi-binomial distribution was used to identify the socio-demographic characteristics association with NCDs. RESULTS: The prevalence of NCDs among the adult population of Dubai was 15.01%. Individuals aged 60+, local Arabs (Emirati), divorced and widowed individuals, and individuals who were not currently working reported NCDs more than the other groups. In the regression analysis, the association with NCDs were reported among elderly people, males, unmarried individuals, older individuals who are unmarried, and Emiratis. CONCLUSION: The study identified several socio-demographic characteristics associated with reporting NCDs. This is one of the few studies related to NCDs in Dubai. Allocating appropriate resources to the population groups identified is crucial to reduce the incidence of NCDs in the Emirate