As the return to normality in the wake of the COVID-19 pandemic enters its early stages, the necessity for accurate, quick, and community-wide surveillance of SARS-CoV-2 has been emphasized. Wastewater-based epidemiology (WBE) has been used across the world as a tool for monitoring the pandemic, but studies of its efficacy in comparison to the best-known method for surveillance, randomly selected COVID-19 testing, has limited research. This study evaluated the trends and correlations present between SARS-CoV-2 in the effluent wastewater of a large university campus and random COVID-19 testing results published by the university. A moderately strong positive correlation was found between the random testing and WBE surveillance methods (r = 0.63), and this correlation was strengthened when accommodating for lost samples during the experiment (r = 0.74).

In mid-March of 2020, Arizona State University transformed one of its research labs into ASU Biodesign Clinical Testing Laboratory (ABCTL) to meet the testing needs of the surrounding community during the COVID-19 pandemic. The lab uses RT-qPCR, or reverse transcription polymerase chain reaction, to match the components of a biosample to a portion of the SARS-CoV-2 genome. The ABCTL uses the TaqPath™ COVID-19 Combo Kit, which has undergone many different types of efficacy and efficiency tests and can successfully denote saliva samples as positive even when an individual is infected with various emerging strains of the SARS-CoV-2. Samples are collected by volunteers at testing sites with stringent biosafety precautions and processed in the lab using specific guidelines. As the pandemic eventually becomes less demanding, the ABCTL plans to utilize the Devil’s Drop-off program at various school districts around Arizona to increase testing availability, transfer to the SalivaDirect method, and provide other forms of pathogen testing to distinguish COVID-19 from other types of infections in the ASU community.

This thesis explores the ethical implications of using facial recognition artificial intelligence (AI) technologies in medicine, with a focus on both the opportunities and challenges presented by the use of this technology in the diagnosis and treatment of rare genetic disorders. We highlight the positive outcomes of using AI in medicine, such as accuracy and efficiency in diagnosing rare genetic disorders, while also examining the ethical concerns including bias, misdiagnosis, the issues it may cause within patient-clinician relationships, misuses outside of medicine, and privacy. This paper draws on the opinions of medical providers and other professionals outside of medicine, which finds that while many are excited about the potential of AI to improve medicine, concerns remain about the ethical implications of these technologies. We discuss current legislation controlling the use of AI in healthcare and its ambiguity. Overall, this thesis highlights the need for further research and public discourse to address the ethical implications of using facial recognition and AI technologies in medicine, while also providing recommendations for its future use in medicine.

This thesis explores the ethical implications of using facial recognition artificial intelligence (AI) technologies in medicine, with a focus on both the opportunities and challenges presented by the use of this technology in the diagnosis and treatment of rare genetic disorders. We highlight the positive outcomes of using AI in medicine, such as accuracy and efficiency in diagnosing rare genetic disorders, while also examining the ethical concerns including bias, misdiagnosis, the issues it may cause within patient-clinician relationships, misuses outside of medicine, and privacy. This paper draws on the opinions of medical providers and other professionals outside of medicine, which finds that while many are excited about the potential of AI to improve medicine, concerns remain about the ethical implications of these technologies. We discuss current legislation controlling the use of AI in healthcare and its ambiguity. Overall, this thesis highlights the need for further research and public discourse to address the ethical implications of using facial recognition and AI technologies in medicine, while also providing recommendations for its future use in medicine.

An exploration into the history of the 1918 Influenza Pandemic and the societal impacts associated with it, as well as an analysis of the developing SARS-CoV-2 pandemic today. Based upon these analyses, similarities were drawn between the two pandemics which suggested a lack of innovation in preventative measures over the last century. Given this conclusion a series of proposals were made that should be further explored to give not only the United States, but the world at large, a better chance in the face of the next emerging disease.