Understanding diagnostic tests and examining important features of novel coronavirus (COVID-19) infection are essential steps for controlling the current pandemic of 2020. In this paper, we study the relationship between clinical diagnosis and analytical features of patient blood panels from the US, Mexico, and Brazil. Our analysis confirms that among adults, the risk of severe illness from COVID-19 increases with pre-existing conditions such as diabetes and immunosuppression. Although more than eight months into pandemic, more data have become available to indicate that more young adults were getting infected. In addition, we expand on the definition of COVID-19 test and discuss sensitivity and specificity measures. As of November 2020, most developed testing methodologies assume that COVID-19 is a respiratory illness and only effective for the first few days of the infection. Hence, a swapping of either mouth, back of throat or nasal cavity is used for detection of the virus and the load. Recent studies involve not only discussions of whether it is respiratory or vascular in nature but also question the airborne nature of this virus. Our machine learning models are specifically useful for the diagnosis and management of COVID-19 for patients with symptoms, yet tested negative, i.e., possibly false negatives. In addition, our models could be useful of treatment for patients who have been sick for longer than 60 days as whether the virus is still in their system.
Tanaydin, Anthony; Liang, Jingchen; and Engels, Daniel W.
"SARS-CoV-2 Pandemic Analytical Overview with Machine Learning Predictability,"
SMU Data Science Review: Vol. 3:
2, Article 17.
Available at: https://scholar.smu.edu/datasciencereview/vol3/iss2/17
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