Factors affecting the COVID-19 risk in the US counties: an innovative approach by combining unsupervised and supervised learning. Academic Article

abstract

• The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was confirmed in China, Coronavirus started to spread all over the United States. Some states and counties reported high number of positive cases and deaths, while some reported lower COVID-19 related cases and death. In this paper, the factors that could affect the risk of COVID-19 infection and death were analyzed in county level. An innovative method by using K-means clustering and several classification models is utilized to determine the most critical factors. Results showed that longitudinal coordinate and population density, latitudinal coordinate, percentage of non-white people, percentage of uninsured people, percent of people below poverty, percentage of Elderly people, number of ICU beds per 10,000 people, percentage of smokers were the most significant attributes.

authors

• Erraguntla, Madhav

published proceedings

• Stoch Environ Res Risk Assess

author list (cited authors)

• Ziyadidegan, S., Razavi, M., Pesarakli, H., Javid, A. H., & Erraguntla, M.

citation count

• 3

complete list of authors

• Ziyadidegan, Samira||Razavi, Moein||Pesarakli, Homa||Javid, Amir Hossein||Erraguntla, Madhav

publication date

• May 2022

publisher

• Springer Nature  Publisher

published in

• Stochastic Environmental Research and Risk Assessment  Journal

keywords

• Covid-19
• K-means Clustering
• Meteorological Variables
• Multinomial Logistic Regression
• Sars-cov-2

Digital Object Identifier (DOI)

• 10.1007/s00477-021-02148-0

start page

• 1469

end page

• 1484

volume

• 36

issue

• 5

URL

• http://dx.doi.org/10.1007/s00477-021-02148-0