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(1) Background: Physiological responses, such as heart rate and heart rate variability, have been increasingly utilized to monitor, detect, and predict mental stress. This review summarizes and synthesizes previous studies which analyzed the impact of mental stress on heart activity as well as mathematical, statistical, and visualization methods employed in such analyses. (2) Methods: A total of 119 articles were reviewed following the Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Non-English documents, studies not related to mental stress, and publications on machine learning techniques were excluded. Only peer-reviewed journals and conference proceedings were considered. (3) Results: The studies revealed that heart activities and behaviors changed during stressful events. The majority of the studies utilized descriptive statistical tests, including t-tests, analysis of variance (ANOVA), and correlation analysis, to assess the statistical significance between stress and non-stress events. However, most of them were performed in controlled laboratory settings. (4) Conclusions: Heart activity shows promise as an indicator for detecting stress events. This review highlights the application of time series techniques, such as autoregressive integrated moving average (ARIMA), detrended fluctuation analysis, and autocorrelation plots, to study heart rate rhythm or patterns associated with mental stress. These models analyze physiological data over time and may help in understanding acute and chronic cardiovascular responses to stress.

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.

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 mortality. In this paper, the factors that could affect the risk of COVID-19 infection and mortality 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 mean temperature, percent of people below poverty, percent of adults with obesity, air pressure, population density, wind speed, longitude, and percent of uninsured people were the most significant attributes