The COVID-19 pandemic has had a tremendous impact on people’s lives and it has reemphasized health inequities in the United States. Historically, minority communities have faced barriers to accessing health care and demonstrated hesitancies to getting vaccinated for various diseases. This has led to disparities in terms of how different diseases affect different communities. This same pattern has been seen regarding how COVID-19 has affected different ethnicities in the U.S. Statistics have shown vaccination disparities for COVID-19 among different ethnicities and organizations in the U.S. have employed different strategies to address this health inequity. This thesis analyzes the hesitancies and barriers to getting vaccinated for COVID-19, specifically among African Americans and Hispanics. Additionally, this thesis looks at the strategies that have been used to address the vaccination inequities that have affected these two ethnicities with a focus specifically on how mass vaccination sites and mobile health clinics try to address the vaccination disparity.

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.

The COVID 19 pandemic has highlighted the necessity of accurately and simply relying scientific discovery and information to the public. Among scientists, the practice is to reduce jargon, engage the audience through storytelling, and include enough detail to give a broad understanding of a narrow topic. Conflict between journalists and scientists leads to a creation of a different narrative for the general public. The news site CNN.com was searched with the google archive function by year for articles that included the keyword vaccine. Articles were sorted into categories of main focus such as political, cultural and scientific or mixed. Results were analyzed and conclusions made about the amount of content in each category for the kind of narrative being written about vaccines, with most years having most articles in the political category. Possible effectiveness of mixed categories were discussed and areas future research identified.

Our thesis project is a 5-person group thesis that was created over the span of two years. In the summer of 2020, at the height of the first wave of the COVID-19 pandemic, our group first met and discussed our shared interests in mask-wearing and individual factors that we each thought had significant impacts on mask-wearing among Barrett students. We each decided on factors that we wanted to investigate and subsequently split into three main groups based on our interests: culture and geography, medical humanities, and medical and psychological conditions. Despite these different interests, we continued to treat our thesis as a five-person project rather than three different projects. We then constructed a survey, followed by several focus group sessions and interview questions to ask Honors students. In January 2021, we received approval from the IRB for our project, and we quickly finalized our survey, focus group and interview questions. In February 2021, we sent out our survey via the Barrett Digest, which we kept open for approximately one month. We also sent out advertisements for our survey via social media platforms such as Twitter and Discord. Following completion of the survey, we contacted all of the respondents who stated that they were interested in participating in focus groups and interviews. Focus groups and interviews were conducted in March and April 2021, and results were analyzed and correlated to our individual subtopics. Each of the focus group and interview participants received $50 each, and three randomly-selected students who completed the survey received $25 each. From April 2021 until April 2022, we analyzed our results, came to conclusions based on our initial topics of interest, and constructed our paper.

Our thesis project is a 5-person group thesis that was created over the span of two years. In the summer of 2020, at the height of the first wave of the COVID-19 pandemic, our group first met and discussed our shared interests in mask-wearing and individual factors that we each thought had significant impacts on mask-wearing among Barrett students. We each decided on factors that we wanted to investigate and subsequently split into three main groups based on our interests: culture and geography, medical humanities, and medical and psychological conditions. Despite these different interests, we continued to treat our thesis as a five-person project rather than three different projects. We then constructed a survey, followed by several focus group sessions and interview questions to ask Honors students. In January 2021, we received approval from the IRB for our project, and we quickly finalized our survey, focus group and interview questions. In February 2021, we sent out our survey via the Barrett Digest, which we kept open for approximately one month. We also sent out advertisements for our survey via social media platforms such as Twitter and Discord. Following completion of the survey, we contacted all of the respondents who stated that they were interested in participating in focus groups and interviews. Focus groups and interviews were conducted in March and April 2021, and results were analyzed and correlated to our individual subtopics. Each of the focus group and interview participants received $50 each, and three randomly-selected students who completed the survey received $25 each. From April 2021 until April 2022, we analyzed our results, came to conclusions based on our initial topics of interest, and constructed our paper.

The COVID-19 pandemic caused uncertainty and changing public health recommendations across the world as our understanding of the SARS-CoV-2 virus changed. Following a preliminary assessment by the World Health Organization, non-steroidal anti-inflammatory drugs were said to worsen symptoms and should be avoided before the recommendation was subsequently revoked. There also was pain associated with infection, leading to the hypothesis that use of over-the-counter pain medication increases may correlate with increases of SARS-CoV-2 infections. Wastewater samples were collected from two communities in Tempe, AZ from December 2019 to July 2020 (n = 35) and were analyzed by liquid chromatography tandem mass spectrometry (LC-MS/MS) to identify levels of acetaminophen, ibuprofen and their metabolites, acetaminophen sulfate and carboxy-ibuprofen. Results showed 100% detection frequency of all analytes in all samples across the duration of the study. Mass loadings of acetaminophen (918.4 g day-1 +/- 354.8 g day-1) were higher than ibuprofen (182.9 g day-1 +/- 49.8 g day-1), potentially driven by flushing behaviors rather than consumption activities. However, ibuprofen was more heavily consumed than acetaminophen across all days of the study period. Comparisons to COVID-19 clinical cases data showed increased use in ibuprofen with increases in clinical cases loads, while acetaminophen showed no change, suggesting ibuprofen was the over the counter (OTC) medication of choice during the first wave of the pandemic.