A machine learning analysis of COVID-19 mental health data

Abstract

In late December 2019, the novel coronavirus (Sars-Cov-2) and the resulting disease COVID-19 were first identified in Wuhan China. The disease slipped through containment measures, with the first known case in the United States being identified on January 20th, 2020. In this paper, we utilize survey data from the Inter-university Consortium for Political and Social Research and apply several statistical and machine learning models and techniques such as Decision Trees, Multinomial Logistic Regression, Naive Bayes, k-Nearest Neighbors, Support Vector Machines, Neural Networks, Random Forests, Gradient Tree Boosting, XGBoost, CatBoost, LightGBM, Synthetic Minority Oversampling, and Chi-Squared Test to analyze the impacts the COVID-19 pandemic has had on the mental health of frontline workers in the United States. Through the interpretation of the many models applied to the mental health survey data, we have concluded that the most important factor in predicting the mental health decline of a frontline worker is the healthcare role the individual is in (Nurse, Emergency Room Staff, Surgeon, etc.), followed by the amount of sleep the individual has had in the last week, the amount of COVID-19 related news an individual has consumed on average in a day, the age of the worker, and the usage of alcohol and cannabis.

Machine learning-based analytics of the impact of the Covid-19 pandemic on alcohol consumption habit changes among United States healthcare workers

“…Using Machine learning, in one of our previous works, we utilized several machine learning models to analyze the impacts the COVID-19 pandemic has had on the mental health of frontline workers in the United States 2 . In another previous work, we used machine learning methods and statistical tests to investigate the relationship between the COVID-19 vaccines and boosters and the total case count for the Coronavirus across multiple states in the USA as well as the relationship between several selected underlying health conditions with COVID-19 35 .…”

“…COVID-19, caused by a virus named SARS-CoV-2, was first discovered in December 2019 in Wuhan, China 1 . The Covid-19 pandemic caused an unprecedented health crisis worldwide and negatively affected the mental health of healthcare workers [2][3][4] . A rise in the rate of positive COVID-19 tests and the number of hospitalizations, reduction of proper personal protection equipment, working under severe pressures, and an increase in fears related to contracting the virus and transmitting it to others could contribute to a mental health decline among healthcare workers [5][6][7] .…”

Adoption of Artificial Intelligence in the Ugandan Health Sector: a review of Literature

“…AI application Title Rezapour and Hansen [29] Machine Learning A machine learning analysis of COVID-19 mental health data. Nareeba et al [30] Machine Learning Machine learning algorithm for identifying the predictors of childhood immunization in rural Uganda.…”

“…Implementing an AI-based system does not come cheap as many associated activities and requirements are costly or even hard to obtain especially in developing countries like Uganda. Authors of the reviewed papers highlighted the financial hardships faced in acquiring and annotating datasets [17,24], hardware and computing resources [24,43,49], and system maintenance and upgrading [29,42]. Modern AI-driven systems leverage deep learning neural networks that require very high computing hardware compared to traditional machine learning algorithms.…”

“…Most of the AI systems reviewed in this study focused on training, validation, and testing of the models; they were never used or deployed beyond an academic setting [24,29,42,43,49]. This shows the wide gap between AI systems development and deployment in healthcare practice.…”