BACKGROUND: The COVID-19 pandemic has affected the health, economic, and social fabric of many nations worldwide . Identification of individual-level susceptibility factors may help people in identifying and managing their emotional, psychological, and social well-being .
OBJECTIVE: This study is focused on learning a ranked list of factors that could indicate a predisposition to a mental disorder during the COVID-19 pandemic .
METHODS: In this study, we have used a survey of 17,764 adults in the United States from different age groups, genders, and socioeconomic statuses . Through initial statistical analysis and Bayesian network inference, we have identified key factors affecting mental health during the COVID-19 pandemic . Integrating Bayesian networks with classical machine learning approaches led to effective modeling of the level of mental health prevalence .
RESULTS: Overall, females were more stressed than males, and people in the age group 18-29 years were more vulnerable to anxiety than other age groups . Using the Bayesian network model, we found that people with a chronic mental illness were more prone to mental disorders during the COVID-19 pandemic . The new realities of working from home; homeschooling; and lack of communication with family, friends, and neighbors induces mental pressure . Financial assistance from social security helps in reducing mental stress during the COVID-19-generated economic crises . Finally, using supervised machine learning models, we predicted the most mentally vulnerable people with ~80% accuracy .
CONCLUSIONS: Multiple factors such as social isolation, digital communication, and working and schooling from home were identified as factors of mental illness during the COVID-19 pandemic . Regular in-person communication with friends and family, a healthy social life, and social security were key factors, and taking care of people with a history of mental disease appears to be even more important during this time.