JMIR Publications

ABSTRACT

Background:

: COVID-19 pandemic has deeply affected the health, economic, and social fabric of nations. Identification of individual-level susceptibility factors may help people in identifying and managing their emotional, psychological, and social well-being.

Objective:

This work is focused on learning a ranked list of factors that could indicate a predisposition to a mental disorder during the COVID pandemic.

Methods:

In this study, We have used a survey of 17764 adults in the USA at different age groups, genders, and socioeconomic statuses. Through initial statistical analysis followed by Bayesian Network inference, we have identified key factors affecting Mental health during the COVID pandemic. Integrating Bayesian networks with classical machine learning approaches lead to effective modeling of the level of mental health.

Results:

Overall, females are more stressed than males, and people of age-group 18-29 are more vulnerable to anxiety than other age groups. Using the Bayesian Network Model, we found that people with chronic medical condition of mental illness are more prone to mental disorders during the COVID age. The new realities of working from home, home-schooling, and lack of communication with family/friends/neighbors induces mental pressure. Financial assistance from social security helps in reducing mental stress during COVID generated economic crises. Finally, using supervised ML models, we predicted the most mentally vulnerable people with ~80% accuracy.

Conclusions:

Multiple factors such as Social isolation, digital communication, working, and schooling from home, were identified as crucial factors of mental illness during Covid-19. Regular non-virtual communication with friends and family, healthy social life and social security are key factors and especially taking care of people with mental disease history appear to be even more important. Clinical Trial: ...