Evaluation of COVID-19 pandemic on components of social and mental health using machine learning, analysing United States data in 2020 - Sadegh-Zadeh, Seyed-Ali; Bahrami, Mahboobe; Najafi, Amirreza; Asgari-Ahi, Meisam; Campion, Russell; Hajiyavand, Amir M. (Frontiers in Psychiatry, 2022)
Background: COVID-19 was named a global epidemic by the World Health Organization in March 2020. Governments issued directives to stay at home at this time. The goal of this study was to document the prevalence of mental health problems and their relationship with the quality and amount of social relationships affected by the epidemic during the USA national lockdown. Methods: We utilized data from a sample of adults from the COVID-19 Impact Survey On April 20–26, 2020, May 4–10, 2020, and May 30–June 8, 2020. Participants' mental health outcomes were compared clinically by looking at the quantity and quality of their social ties. To predict relationships between COVID-19 mental health and demographic and social factors, we employed random forest, support vector machine, naive bayes, and logistic regression. Results: The most obvious result of our research was that people who have previously been diagnosed with any type of mental illness are affected by the new constraints during a pandemic. These people were among the most vulnerable to mental illness due to changes in lifestyle. Conclusions: This study estimates the frequency of mental illness among adults with and without a history of mental disease during COVID-19 preventative limitations. With the persistence of quarantine limitations, the prevalence of psychiatric issues grew. In the third survey, which was done under quarantine/house restrictions, mental health problems and acute stress reactions were substantially greater than in the prior two surveys. The findings of the study reveal that more focused messaging and support are needed for those with a history of mental illness throughout the implementation of the restrictions.