A Comparative Analysis of Health Outcomes Before, During, and After COVID-19 Using CDC’s BRFSS Data Sharlene Pioquinto Project Mentor(s): Dominic Klyve, PhD The COVID-19 pandemic spanned from January 2020 to May 2023, forever altering life in the United States, causing lasting consequences across all demographic and socioeconomic groups. This study investigates the effects of the pandemic on mental and physical health outcomes using data from the Center for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System (BRFSS). The data pulled for this study highlights three different years to capture the before, during, and after of the COVID-19 pandemic: 2018, 2021, 2024. The analysis of this data aims to quantify disparities in health outcomes throughout the time of the pandemic with a specific focus on differences by gender, race, and socioeconomic status. Statistical methods include descriptive statistics, logistic regression, and time series analysis. The data is weighted accordingly to ensure national representativeness. Preliminary expectations suggest that the pandemic highlighted existing inequalities, with increases in mental distress and delayed care affecting more vulnerable populations. Presentation Type: Oral Presentation (May 20, 9:30am–5:00pm) Keywords: Mental Health, Physical Health, COVID-19, Comparative Analysis SOURCE Form ID: 171 Joint-Truck Drone Delivery Cost Optimization Using Discrete Chimp Optimization Algorithm With CUDA Jonathan Rescorla Project Mentor(s): Donald Davendra, PhD Package delivery has been a significant challenge for delivery networks in rural areas, work is idled while the driver travels significant distances between delivery points. Because of this, delivering to rural areas comes at a greater cost to services on a point-by-point basis compared to urban cities. This research examines joint truck-drone methodologies for delivery in rural areas in comparison to standalone truck delivery for potential reduction in operating costs. The methodology utilizes a drone to deliver between points to reduce the time driven by the truck with the constraint that the distance between the delivery point and the truck must not exceed thirty miles. A discretized version of the Chimp Optimization Algorithm (ChOA) meta-heuristic is applied on a CUDA program to thoroughly examine the search space. The algorithm is configured for a population of ten chimps on a pseudo- randomly generated search space of ten nodes with pseudo-random distances between delivery points and the distribution center. Pseudo-randomly generated numbers are provided by the Mersenne Twister algorithm, and all delivery points are generated within a radius of 150 miles from the origin. Results favor a joint approach and show an improvement in operating costs if the area is flexible under the constraint of the drone’s delivery span, results are stifled otherwise. Application is context dependent, implementing a joint approach presents a significant upfront cost to delivery services with contextual benefits based on the area. Presentation Type: Oral Presentation (May 20, 9:30am–5:00pm) Keywords : Discrete Optimization, Package Delivery, DChOA, CUDA, Traveling Salesman SOURCE Form ID: 41
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