About: Identifying novel factors associated with COVID-19 transmission and fatality using the machine learning approach

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About: Identifying novel factors associated with COVID-19 transmission and fatality using the machine learning approach Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Identifying novel factors associated with COVID-19 transmission and fatality using the machine learning approach
Creator	Liu, Qian Zhang, Yue Chen, Canping Jiang, Shanmei Li, Mengyuan Uddin, Md Wang, Xiaosheng Zhang, Zhilan Chen, Cai Cao, Wenxiu Du, Beibei Liu, Yijing
topic	covid:a3347e9ba021f0b37f23108d64e05b9bddff0120#this
source	MedRxiv
abstract	The COVID-19 virus has infected millions of people and resulted in hundreds of thousands of deaths worldwide. By using the logistic regression model, we identified novel critical factors associated with COVID19 cases, death, and case fatality rates in 154 countries and in the 50 U.S. states. Among numerous factors associated with COVID-19 risk, we found that the unitary state system was counter-intuitively positively associated with increased COVID-19 cases and deaths. Blood type B was a protective factor for COVID-19 risk, while blood type A was a risk factor. The prevalence of HIV, influenza and pneumonia, and chronic lower respiratory diseases was associated with reduced COVID-19 risk. Obesity and the condition of unimproved water sources were associated with increased COVID-19 risk. Other factors included temperature, humidity, social distancing, smoking, and vitamin D intake. Our comprehensive identification of the factors affecting COVID-19 transmission and fatality may provide new insights into the COVID-19 pandemic and advise effective strategies for preventing and migrating COVID-19 spread.
has issue date	2020-06-12(xsd:dateTime)
bibo:doi	10.1101/2020.06.10.20127472
has license	medrxiv
sha1sum (hex)	a3347e9ba021f0b37f23108d64e05b9bddff0120
schema:url	https://doi.org/10.1101/2020.06.10.20127472
resource representing a document's title	Identifying novel factors associated with COVID-19 transmission and fatality using the machine learning approach
resource representing a document's body	covid:a3347e9ba021f0b37f23108d64e05b9bddff0120#body_text
is http://vocab.deri.ie/void#inDataset of	https://covidontheweb.inria.fr:4443/about/id/http/ns.inria.fr/covid19/a3347e9ba021f0b37f23108d64e05b9bddff0120
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