Department of Mathematics, Wake Forest University, Winston-Salem, NC, USA.
Sci Rep. 2022 Sep 2;12(1):14965. doi: 10.1038/s41598-022-19314-1.
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.
2019 年 12 月下旬，新型冠状病毒（Sars-CoV-2）及其引发的疾病 COVID-19 首次在中国武汉被发现。该疾病突破了控制措施，美国首例已知病例于 2020 年 1 月 20 日被发现。在本文中，我们利用了大学间政治和社会研究联合会的调查数据，并应用了几种统计和机器学习模型和技术，如决策树、多项逻辑回归、朴素贝叶斯、k-最近邻、支持向量机、神经网络、随机森林、梯度提升树、XGBoost、CatBoost、LightGBM、合成少数过采样和卡方检验，来分析 COVID-19 大流行对美国一线工作人员心理健康的影响。通过对应用于心理健康调查数据的众多模型的解释，我们得出结论，预测一线工作人员心理健康下降的最重要因素是个人所担任的医疗角色（护士、急诊室工作人员、外科医生等），其次是个人上周的睡眠时间、个人平均每天接触的 COVID-19 相关新闻量、工人的年龄以及酒精和大麻的使用情况。
