STATISTICAL METHODS TO REPRESENT THE ANXIETY AND DEPRESSION EXPERIENCED IN ALMADINH KSA DURING COVID-19

Abstract

Background: The COVID-19 pandemic is an issue of global concern. It has been nine months since the first confirmed case of the coronavirus disease in Saudi Arabia. The recent COVID-19 outbreak has had a devastating impact on education, economic, stability and health. This study investigates the prevalence of anxiety and depression among individuals in Almadinh KSA during COVID-19. Method: A cross-sectional questionnaire was distributed to public in Amdadina KSA via Google forms collect the data. The responds included 78 female and 352 male, socio-demographic information including age, gender, and education levels was collected. Three mathematical models were determined to be powerful statistical techniques for classifying and predicting anxiety and depression: logistic regression, decision tree, and analysis. Results: The prevalence rates of anxiety and depression were 92.6 % and 91.4.0%, respectively. The decision tree and linear discriminate analysis yielded the same results. The accuracy of correctly classified cases was the same in all three methods. This analysis reveals significant structural differences between three methods. There is a wide range of Saudi citizens who are at higher risk for dysfunctional behavior during COVID-19 pandemic.