A machine learning analysis of the relationship between some underlying medical conditions and COVID-19 susceptibility

• URL: http://arxiv.org/abs/2112.12901v1
• Date: Fri, 24 Dec 2021 01:36:57 GMT
• Title: A machine learning analysis of the relationship between some underlying medical conditions and COVID-19 susceptibility
• Authors: Mostafa Rezapour, Colin A. Varady
• Abstract summary: The Coronavirus, commonly known as COVID-19, has significantly affected the lives of all citizens residing in the United States. Several vaccines and boosters have been created as a permanent remedy for individuals to take advantage of.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For the past couple years, the Coronavirus, commonly known as COVID-19, has significantly affected the daily lives of all citizens residing in the United States by imposing several, fatal health risks that cannot go unnoticed. In response to the growing fear and danger COVID-19 inflicts upon societies in the USA, several vaccines and boosters have been created as a permanent remedy for individuals to take advantage of. In this paper, we investigate the relationship between the COVID-19 vaccines and boosters and the total case count for the Coronavirus across multiple states in the USA. Additionally, this paper discusses the relationship between several, selected underlying health conditions with COVID-19. To discuss these relationships effectively, this paper will utilize statistical tests and machine learning methods for analysis and discussion purposes. Furthermore, this paper reflects upon conclusions made about the relationship between educational attainment, race, and COVID-19 and the possible connections that can be established with underlying health conditions, vaccination rates, and COVID-19 total case and death counts.

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