Modelling the Psychological Impact of COVID-19 in Saudi Arabia Using Machine Learning

This article aims to assess health habits, safety behaviors, and anxiety factors in the community during the novel coronavirus disease (COVID-19) pandemic in Saudi Arabia based on primary data collected through a questionnaire with 320 respondents. In other words, this paper aims to provide empirical insights into the correlation and the correspondence between sociodemographic factors (gender, nationality, age, citizenship factors, income, and education), and psycho-behavioral effects on individuals in response to the emergence of this new pandemic. To focus on the interaction between these variables and their effects, we suggest different methods of analysis, comprising regression trees and support vector machine regression (SVMR) algorithms. According to the regression tree results, the age variable plays a predominant role in health habits, safety behaviors, and anxiety. The health habit index, which focuses on the extent of behavioral change toward the commitment to use the health and protection methods, is highly affected by gender and age factors. The average monthly income is also a relevant factor but has contrasting effects during the COVID-19 pandemic period. The results of the SVMR model reveal a strong positive effect of income, with R2 values of 99.59%, 99.93% and 99.88% corresponding to health habits, safety behaviors, and anxiety.