ABSTRACT
OBJECTIVES: The aim of the current study was to describe COVID-19's epidemiological characteristics in Oman during the initial stages of the outbreak and compare findings with other countries' reports. METHODS: Data were drawn from a descriptive, records-based review of reported cases of COVID-19 collected through the national COVID-19 Surveillance System from February to April 2020. RESULTS: A total of 2,443 confirmed cases were reported during the study period. The overall first-time testing rate for this period was 851.7 per 100,000, the positivity rate was 53.1 (confidence intervals [CI]: 51.0-55.2) and the death rate was 0.32 (CI: 0.20-0.54) per 100,000 population, respectively. The overall national positive ratio was 5.7% and ranged from 2.2-7.1% across various governorates. Muscat Governorate had the highest positive ratio (12.5%). People in the 51-60 year old age group (RR = 1.97), males (RR = 1.24), non-Omanis (RR = 2.33) and those living in Muscat (RR = 2.14) emerged as categories with significant demographic risk for COVID-19 cases when compared to the national average. The mean age was 35.6 ± 13.4. Asymptomatic cases accounted for nearly 16%. CONCLUSION: The overall rate of COVID-19 cases and deaths were low in Oman compared to the rest of the world during the study period.
Subject(s)
COVID-19/epidemiology , Pandemics , Adolescent , Adult , Aged , Aged, 80 and over , Child , Child, Preschool , Cough/epidemiology , Female , Fever/epidemiology , Fever/etiology , Humans , Infant , Male , Middle Aged , Oman/epidemiology , SARS-CoV-2 , Young AdultABSTRACT
Corona virus or simply Corona is the current leading pandemic of the world. It has affected students and their in education in higher numbers than any other sector putting them into a depression. Hence this research attempts to suggest solutions for reducing depression amongst students amidst the pandemic. This work proposes ESVMs (Enhanced Support Vector Machines) model for its predictions. Identifying student performances is complex issue as the numbers are voluminous and hence the objective of this research is to assess student performance prediction model by using an efficient clustering method. Missing values and irrelevant data are resolved in this work using SCCs (Statistical correlation Coefficients) which work on subject wise manner or student wise data. This work also provides a novel solution for data pre-processing. IFCM (Improved Fuzzy C-means clustering) proposed in this work identifies high quality clusters with robustness. Further, the use of PSO (Particle Swarm Optimization) in feature selections improves its efficiency of the given data. Classifications are executed by the proposed ESVMs which predicts student's grade with accuracy. The evaluation results of this study improve classification accuracy significantly when compared to existing prediction models. © 2021 Karadeniz Technical University. All rights reserved.