A Machine Learning Analysis of COVID-19 Mental Health Data

Mapping Intimacies ◽

10.21203/rs.3.rs-1129807/v1 ◽

2022 ◽

Author(s):

Mostafa Rezapour ◽

Lucas Hansen

Keyword(s):

Mental Health ◽

United States ◽

Machine Learning ◽

Survey Data ◽

Social Research ◽

Multinomial Logistic Regression ◽

The United States ◽

Support Vector ◽

K Nearest Neighbors ◽

The Individual

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.

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Learning the Mental Health Impact of COVID-19 in the United States With Explainable Artificial Intelligence: Observational Study

JMIR Mental Health ◽

10.2196/25097 ◽

2021 ◽

Vol 8 (4) ◽

pp. e25097

Author(s):

Indra Prakash Jha ◽

Raghav Awasthi ◽

Ajit Kumar ◽

Vibhor Kumar ◽

Tavpritesh Sethi

Keyword(s):

Mental Health ◽

United States ◽

Machine Learning ◽

Mental Illness ◽

Social Security ◽

Bayesian Network ◽

Age Groups ◽

The United States ◽

Supervised Machine Learning ◽

Key Factors

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.

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Stress, Mental Health & Job Satisfaction among Nurse Managers

Health Services Management Research ◽

10.1177/095148488800100106 ◽

1988 ◽

Vol 1 (1) ◽

pp. 51-58 ◽

Cited By ~ 6

Author(s):

Cary L. Cooper ◽

Carol A. Manning ◽

Gaye Poteet ◽

Peter Hingley

Keyword(s):

Mental Health ◽

United States ◽

Job Satisfaction ◽

Multivariate Analysis ◽

Occupational Stress ◽

Nurse Managers ◽

High Stress ◽

The United States ◽

Person Environment Fit ◽

The Individual

This study investigated sources of stress and their effects on mental health and job satisfaction among nurse managers in the United States. One hundred and forty-four nurse managers completed questionnaires containing information concerning three kinds of variables: the degree of stress experienced at work, the personality of the individual, and characteristics of life situations away from work. It was found that in general the nurse managers were satisfied with their jobs, although they reported high stress on the job. They reported better mental health than normative groups. Using multivariate analysis, it was found that all three types of variables were necessary for prediction of mental health and job satisfaction. This finding supports the person-environment fit theory of occupational stress.

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Forecasting COVID 19 Confirmed Cases Using Machine Learning: the Case of America

10.20944/preprints202009.0228.v1 ◽

2020 ◽

Author(s):

Mario Jojoa ◽

Begoña Garcia-Zapirain

Keyword(s):

United States ◽

Machine Learning ◽

Open Data ◽

Absolute Error ◽

The United States ◽

Percentage Error ◽

Support Vector ◽

Learning Tools ◽

The European Union ◽

Testing Stage

This paper presents a Multilayer Perceptron and Support Vector Machine algorithms approach to predict the number of COVID19 infections in different countries of America. It intends to serve as a tool for decision-making and tackling the pandemic that the world is currently facing. The models were trained and tested using open data from the European Union repository where a time series of confirmed contagious cases was modeled until May 25, 2020. The hyperparameters as number of neurons per layer were set up using a tabu list algorithm. The countries selected to carry out the study were Brazil, Chile, Colombia, Mexico, Peru and the United States. The metrics used are Pearson's correlation coefficient (CP), Mean Absolute Error (MAE), and Mean Percentage Error (MPE). For the testing stage we obtained the following results: Brazil, CP=0.65, MAE=2508 and MPE=17%; Chile, CP=0.64, MAE=504, MPE=16%; Colombia, CP=0.83, MAE=76, MPE=9%; Mexico, CP=0.77, MAE=231, MPE=9%; Peru, CP=0.76, MAE=686, MPE=18% and the United States of America, CP=0.93, MAE=799, MPE=4%. This resulted in powerful machine learning tools although it is necessary to use specific algorithms depending on the data and the stage of the country’s pandemic.

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Learning the Mental Health Impact of COVID-19 in the United States With Explainable Artificial Intelligence: Observational Study (Preprint)

10.2196/preprints.25097 ◽

2020 ◽

Author(s):

Indra Prakash Jha ◽

Raghav Awasthi ◽

Ajit Kumar ◽

Vibhor Kumar ◽

Tavpritesh Sethi

Keyword(s):

Mental Health ◽

United States ◽

Machine Learning ◽

Mental Illness ◽

Social Security ◽

Bayesian Network ◽

Age Groups ◽

The United States ◽

Supervised Machine Learning ◽

Key Factors

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.

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Evaluating Mental Health–Related Symptoms Among Cancer Survivors During the COVID-19 Pandemic: An Analysis of the COVID Impact Survey

JCO Oncology Practice ◽

10.1200/op.20.00752 ◽

2021 ◽

pp. OP.20.00752

Author(s):

Jessica Yasmine Islam ◽

Denise C. Vidot ◽

Marlene Camacho-Rivera

Keyword(s):

Mental Health ◽

United States ◽

High School ◽

Cancer Survivors ◽

Multinomial Logistic Regression ◽

The United States ◽

Mental Health Symptoms ◽

Health Symptoms ◽

High School Degree ◽

Health Related

PURPOSE: The COVID-19 pandemic has affected the mental health of adults in the United States because of recommended preventive behaviors such as physical distancing. Our objective was to evaluate mental health symptoms and identify associated determinants among cancer survivors during the COVID-19 pandemic in the United States. METHODS: We used nationally representative data of 10,760 US adults from the COVID-19 Impact Survey. We defined cancer survivors as adults with a self-reported diagnosis of cancer (n = 854, 7.6%). We estimated associations of mental health symptoms among cancer survivors using multinomial logistic regression. We estimated determinants of reporting at least one mental health symptom 3-7 times in the 7 days before survey administration among cancer survivors using multivariable Poisson regression. RESULTS: Cancer survivors were more likely to report feeling nervous, anxious, or on edge (adjusted odds ratio [aOR], 1.42; 95% CI, 1.07 to 1.90); depressed (aOR, 1.57; 95% CI, 1.18 to 2.09); lonely (aOR, 1.42; 95% CI, 1.05 to 1.91); and hopeless (aOR, 1.51; 95% CI, 1.11 to 2.06) 3-7 days per week in the last 7 days when compared with adults without cancer. Among cancer survivors, adults of age 30-44 years (adjusted prevalence ratio [aPR], 1.87; 95% CI, 1.18 to 2.95), females (aPR, 1.55, 95% CI, 1.12 to 2.13), adults without a high school degree (aPR, 1.79; 95% CI, 1.05 to 3.04), and adults with limited social interaction (aPR, 1.40, 95% CI, 1.01 to 1.95) were more likely to report at least one mental health–related symptom in the last 7 days (3-7 days/week). CONCLUSION: Cancer survivors are reporting mental health symptoms during the COVID-19 pandemic, particularly young adults, adults without a high school degree, women, and survivors with limited social support.

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Who Stays Physically Active during COVID-19? Inequality and Exercise Patterns in the United States

Socius Sociological Research for a Dynamic World ◽

10.1177/2378023120987710 ◽

2021 ◽

Vol 7 ◽

pp. 237802312098771

Author(s):

Chloe Sher ◽

Cary Wu

Keyword(s):

Mental Health ◽

United States ◽

Physical Exercise ◽

Social Inequalities ◽

Social Research ◽

Health Impact ◽

The United States ◽

Physical And Mental Health ◽

Nationally Representative ◽

The Rich

Exercising is crucial to keeping up physical and mental health during the coronavirus disease 2019 (COVID-19) pandemic. In this visualization, the authors consider how existing social inequalities may create unequal physical exercise patterns during COVID-19 in the United States. Analyzing data from a nationally representative Internet panel of the University of Southern California Center for Economic and Social Research Understanding Coronavirus in America project (March to December), the authors find that although all Americans have become physically more active since the outbreak, the pandemic has also exacerbated the inequality in physical exercise. Specifically, the authors show that the gaps in physical exercise have widened substantially between men and women, whites and nonwhites, the rich and the poor, and the educated and the less educated. Policy interventions addressing the widening inequality in physical activity can help minimize the disproportionate mental health impact of the pandemic on disadvantaged populations.

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Estimating Avocado Sales Using Machine Learning Algorithms and Weather Data

Sustainability ◽

10.3390/su10103498 ◽

2018 ◽

Vol 10 (10) ◽

pp. 3498 ◽

Cited By ~ 3

Author(s):

Juan Rincon-Patino ◽

Emmanuel Lasso ◽

Juan Corrales

Keyword(s):

United States ◽

Machine Learning ◽

Prediction Model ◽

Multivariate Regression ◽

The United States ◽

Persea Americana ◽

Machine Learning Algorithms ◽

Weather Data ◽

Support Vector ◽

Multivariate Regression Prediction

Persea americana, commonly known as avocado, is becoming increasingly important in global agriculture. There are dozens of avocado varieties, but more than 85% of the avocados harvested and sold in the world are of the Hass one. Furthermore, information on the market of agricultural products is valuable for decision-making; this has made researchers try to determine the behavior of the avocado market, based on data that might affect it one way or another. In this paper, a machine learning approach for estimating the number of units sold monthly and the total sales of Hass avocados in several cities in the United States, using weather data and historical sales records, is presented. For that purpose, four algorithms were evaluated: Linear Regression, Multilayer Perceptron, Support Vector Machine for Regression and Multivariate Regression Prediction Model. The last two showed the best accuracy, with a correlation coefficient of 0.995 and 0.996, and a Relative Absolute Error of 7.971 and 7.812, respectively. Using the Multivariate Regression Prediction Model, an application that allows avocado producers and sellers to plan sales through the estimation of the profits in dollars and the number of avocados that could be sold in the United States was created.

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