On January 5, 2020, the World Health Organization (WHO) reported on the outbreak of pneumonia of unknown cause in Wuhan, China. Two weeks later, a 35-year-old Washington resident checked into a local urgent care clinic with a 4-day cough and fever. Laboratory testing would confirm this individual as the first case of the novel coronavirus in the U.S., and on January 20, 2020, the Center for Disease Control (CDC) reported this case to the public. In the days and weeks to follow, Twitter, a social media platform with 450 million active monthly users as of 2020, provided many American residents the opportunity to share their thoughts on the developing pandemic online. Social media sites like Twitter are a prominent source of discourse surrounding contemporary political issues, allowing for direct communication between users in real-time. As more population centers around the world gain access to the internet, most democratic discussion, both nationally and internationally, will take place in online spaces. The activity of elected officials as private citizens in these online spaces is often overlooked. I find the ability of publics—which philosopher John Dewey defines as groups of people with shared needs—to communicate effectively and monitor the interests of political elites online to be lacking. To best align the interests of officials and citizens, and achieve transparency between publics and elected officials, we need an efficient way to measure and record these interests. Through this thesis, I found that natural language processing methods like sentiment analyses can provide an effective means of gauging the attitudes of politicians towards contemporary issues.

In January of 2020, the first cases of COVID-19 were recorded in the United States with one of them being an Arizona State University student. Since these initial cases, over 2.4 million more cases have been recorded in Arizona alone. As of March of 2020, Arizona State University Ambulatory Health Services moved to adjust their healthcare delivery methods in response to the COVID-19 pandemic. We aim to identify areas of ASU Ambulatory Health Services as of March of 2020 that need improvement based on an anonymous survey carried out among ASU students, faculty, and staff. The survey was created through Survey Monkey and consisted of 20 questions about the participant’s experience with ASU’s Ambulatory Health Services; while the survey was being created, a literature review was being conducted concerning ASU’s health care delivery in the past and the health care delivery in the greater Maricopa County region. The overall consensus of the 54 participants who took the survey was that approximately 55% of participants had an overall very satisfactory experience through ASU Ambulatory Health Services with a net promoter score of 87% satisfaction and approximately 47% of participants were very likely to recommend ASU Ambulatory Health Services with a net promoter score of about 79%. Most participants reported overall satisfactory experiences but when asked for further commentary they provided more specific criticisms of their experience that could be improved. The specific frustrations that were mentioned were issues with insurance, lack of awareness with available services, instructions on updated regulations and scheduling, and issues with patients’ visits being logged into their medical records. We recommend that ASU Ambulatory Health Services improve in these areas highlighted by the survey answers; as most of these issues are results from communication issues between ASU Ambulatory Health Services and the public, we suggest better means of communication between the public and the health services.

The climate conversation is growing more important and necessary than ever. The media has a way of promoting a "doom and gloom" sentiment over conservation efforts and what the public has the power to do in terms of making a change. Now due to the effects of COVID-19 on the population's attention spans and memories, there is a need for a way to communicate climate science effectively and to encourage those who feel discouraged by climate change to find their inner power. The answer lies in photography. Making science accessible and intriguing through the art of photography is what can get people more interested and empowered to fight against climate change and alter their attitudes towards environmentalism. This thesis explains psychological research and the reasons why people feel helpless in terms of our global future. In then dives into human subjects research conducted on ASU's campus and how the survey results argue in favor of the paper's hypothesis. Additionally, ways to get involved and reasons why we need to remain hopeful are discussed.

As 2020 unfolded, a new headline began taking over front pages: “COVID-19”. In the months that followed, waves of fear, sorrow, isolation, and grief gripped the population in the viruses’ wake. We have all heard it, we have all felt it, indeed because we were all there. Trailing a few months behind those initial headlines, more followed that only served to breed misinformation and ludicrous theories. Even with study after study, quality, scientific data about this new virus could not come fast enough. There was somehow both too much information and also not enough. We were scrambling to process the abundance of raw numbers into some semblance of an explanation. After those first few months of the pandemic, patterns in the research are beginning to emerge. These horrific patterns tell much more than just the pathology of COVID-19. As the number of sick, surviving, and deceased patients began to accumulate, it became clear that some populations were left devastated, while others seemed unscathed. The reasons for these patterns were present long before the COVID-19 Pandemic. Disparities in health care were highlighted by the pandemic – not caused by it. The roots of these disparities lie in the five Social Determinants of Health (SDOH): (1) economic stability, (2) neighborhood and built environment, (3) education, (4) social and community context, and (5) health and health care. Minority populations, namely Black Americans, Hispanic Americans, Native Americans, and Pacific Islanders consistently have higher diagnosis rates and poorer patient outcomes compared to their White American and Asian American counterparts. This is partly because minority populations tend to have jobs that pay lower, increase exposure risk, and provide little healthcare. When unemployment increased in the wake of the pandemic, minorities were the first to lose their jobs and their health insurance. In addition, these populations tend to live in densely populated neighborhoods, where social isolation is harder. Higher poverty rates encourage work DISPROPORTIONATE EFFECTS OF COVID-19 ON MINORITY POPULATIONS 3 rather than education, often perpetuating the cycle. The recent racial history and current aggressions towards minority people might produce a social attitude against healthcare Health care itself can be expensive, hard to find, and/or tied to employment, leading to poorly controlled comorbidities, which exacerbate poor patient outcomes in the case of COVID-19 infection. The healthcare delivery system plays little part in the SDOH, instead, public policy must be called to reform in order to fix these issues.

The COVID-19 pandemic has generated alarming increases in psychological distress and alcohol use behaviors and has caused the greatest increases in depression and anxiety symptoms among college students. Prior studies have examined the impact of COVID-19 broadly on mental health and alcohol use outcomes; however, few studies have examined these impacts in college students. Previous studies have examined individual factors that could moderate the relation between COVID-19 related stressors and mental health and alcohol use outcomes, but knowledge is lacking regarding the role of emotion regulation. The present study aimed to examine the role of emotion regulation in the relation between both COVID-19 stressful experiences and COVID-19 related worry and mental health and alcohol use outcomes, and to explore racial/ethnic differences in their associations. Four hierarchical multiple regression models were conducted to assess main effects of COVID-19 stressors and emotion regulation, as well as moderation of the effect of emotion regulation on depression symptoms, anxiety symptoms, alcohol consumption, and alcohol use disorder (AUD) symptoms during the past year. COVID-19 related worry was associated with greater symptoms of both mental health outcomes, whereas COVID-19 related stressful experiences were associated with both mental health outcomes, more alcohol consumption, and more AUD symptoms. Difficulties in emotion regulation had significant main effects on mental health outcomes and AUD symptoms, but not alcohol consumption. Hispanic/Latinx students reported higher experiences of both COVID-19 related stressors, but consumed less alcohol than did White/European students. This study provides further insight into the nature of COVID-19 related stressors and their subsequent impacts. Implications for prevention and intervention on college campuses are discussed.

From 2019, a severe acute respiratory coronavirus 2, SARS-CoV-2, began to be a global pandemic. Many high income countries developed different strategies in response. This analysis intends to highlight how the COVID-19 became a global pandemic and the strategies that account for successes and failures. In identifying key policy differences, the high income countries of the United States, New Zealand and France were examined. The analysis found that New Zealand had proactive elimination strategies that proved highly effective, whereas the United States and France both struggled with mitigation factors that resulted in disproportionately higher confirmed cases and mortality rates. The analysis highlights how the airborne virus became a pandemic and then followed public policies’ effectiveness in terms of existing political institutions,and then their ability to be successful in preventing the spread of the virus.

This thesis is a retrospective study analyzing data from patient implanted cardiac devices in other to determine the effect of SARS-CoV-2 on cardiac arrhythmias. This study is also the first, to the knowledge of the researchers, in which a cohort of undifferentiated hospitalized and non-hospitalized COVID patients were studied using data from cardiac implanted devices. The results from this study has shown that SARS-CoV-2 leads to statistically significant increases in arrhythmic burden, in particular increased overall arrhythmic episodes, increased VT episodes, increased AT Burden percent, and increased SVT Average Ventricular Rate, and a statistically significant decrease in VT Average Ventricular Rate.

Quantum computing is an emerging and promising alternative to classical computing due to its ability to perform rapidly complex computations in a parallel manner. In this thesis, we aim to design an audio classification algorithm using a hybrid quantum-classical neural network. The thesis concentrated on healthcare applications and focused specifically on COVID-19 cough sound classification. All machine learning algorithms developed or implemented in this study were trained using features from Log Mel Spectrograms of healthy and COVID-19 coughing audio. Results are first presented from a study in which an ensemble of a VGG13, CRNN, GCNN, and GCRNN are utilized to classify audio using classical computing. Then, improved results attained using an optimized VGG13 neural network are presented. Finally, our quantum-classical hybrid neural network is designed and assessed in terms of accuracy and number of quantum layers and qubits. Comparisons are made to classical recurrent and convolutional neural networks.