2nd African International Conference on Industrial Engineering and Operations Management

Predict 2020 USA Presidential Election COVID-19 Correlation

Saloni Patel & Mason Chen
Publisher: IEOM Society International
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Track: High School STEM Poster Competition
Abstract

This project investigates different strategies Trump can utilize in reopening states during the COVID-19 pandemic. During the pandemic, the President and state governors face the challenge of deciding when to reopen the states and whether they should all be opened all at once or based on their individual situations. Based on several calculated variables, such as past win margins of swing states, infected cases, deaths, and unemployment increases for 16 different swing states from past elections, the authors draw conclusions on which states President Trump should be put into consideration to “liberate” or “reopen” to not only safely reopen, but to maximize his chances of winning the 2020 election. With the calculated and collected variables, a statistical model is created to aid in decision making. Although the safest option is to stay closed, many state economies and the overall national economy suffer due to the closure. Trump’s pro-economy campaign must wisely select which states to liberate based not only on unemployment rates, but chances of winning that state in the upcoming 2020 election. This project plays special attention to Michigan, Minnesota, and Virginia, which were called out in President Trump’s tweet on April 17, 2020. Ultimately, this project concludes that Minnesota is a safe state to liberate, Michigan is too risky, and Virginia can be liberated, but the authors advise against it. Additionally, two possible scenarios are also This project demonstrates a practical and statistical modeling framework considering social and political science factors.

Published in: 2nd African International Conference on Industrial Engineering and Operations Management, Harare, Zimbabwe

Publisher: IEOM Society International
Date of Conference: December 7-10, 2020

ISBN: 978-1-7923-6123-4
ISSN/E-ISSN: 2169-8767