The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was confirmed in China, Coronavirus started to spread all over the United States. Some states and counties reported high number of positive cases and deaths, while some reported lower COVID-19 related cases and death. In this paper, the factors that could affect the risk of COVID-19 infection and death were analyzed in county level. An innovative method by using K-means clustering and several classification models is utilized to determine the most critical factors. Results showed that longitudinal coordinate and population density, latitudinal coordinate, percentage of non-white people, percentage of uninsured people, percent of people below poverty , percentage of Elderly people, number of ICU beds per 10,000 people, percentage of smokers were the most significant attributes.

Anxiety disorder is the most common mental health disorder in the United States. One of the key factors that leads to the development and aggravation of anxiety disorders is mental stress. In this study, we reviewed publications that used physiological responses and symptoms to assess mental stress. This review found that mental stress affects heart rate, hear rate variability, blood pressure, and skin conductance. Fuzzy logic, time series, and Poincare plots are prominent data analysis tools for physiological data. Most studies used a threshold (e.g., Poincaré plot) or variance (e.g., moving average models) to distinguish stress from normal conditions. The variations and thresholds, however, might fluctuate across various activities and individuals. Moreover, most research evaluated lab-generated stress data, which may be biased. Therefore, more naturalistic studies should be conducted for future research.

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The goal of this paper is to review the literature on machine learning (ML) and big data applications for mental health, emphasizing current research and practical implementations. To explore the field of ML in mental health, we used a scoping review process. The literature identified application domains of detection and prediction of stress as a contributor to mental health disorders. We evaluated the articles and data on the mental health application, machine learning approach, type of data (sensor, survey, etc.), and type of sensors. Most studies extracted features before developing AI-based stress detection algorithms. Findings revealed that heart rate, heart rate variability, and skin conductance features are the key indicators of stress. Moreover, among AI stress-detection methods, Random Forest and Neural Networks show promising results.

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