About: COVID-Net: A deep learning based and interpretable predication model for the county-wise trajectories of COVID-19 in the United States

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About: COVID-Net: A deep learning based and interpretable predication model for the county-wise trajectories of COVID-19 in the United States 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
has title	COVID-Net: A deep learning based and interpretable predication model for the county-wise trajectories of COVID-19 in the United States
Creator	Tian, Ting Wang, Xueqin Zhang, Heping Jiang, Yuknag Li, Zhongfei Zhang, Yuting
Source	MedRxiv
abstract	The confirmed cases of novel coronavirus disease (COVID-19) have been reported in the United States since late January 2020. There were over 4.8 million confirmed cases and about 320,000 deaths as of May 19, 2020 in the world. We examined the characteristics of the confirmed cases and deaths of COVID-19 in all affected counties of the United States. We proposed a COVID-Net combining the architecture of both Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) by using the trajectories of COVID-19 during different periods until May 19, 2020, as the training data. The validation of the COVID-Net was performed by predicting the numbers of confirmed cases and deaths in subsequent 3-day, 5-day, and 7-day periods. The COVID-Net produced relatively smaller Mean Relative Errors (MREs) for the 10 counties with the most severe epidemic as of May 19, 2020. On average, MREs were 0.01 for the number of confirmed cases in all validation periods, and 0.01, 0.01, and 0.03 for the number of deaths in the 3-day, 5-day, and 7-day periods, respectively. The COVID-Net incorporated five risk factors of COVID-19 and was used to predict the trajectories of COVID-19 in Hudson County, New Jersey and New York County, New York until June 28, 2020. The risk factors include the percentage of the population with access to exercise opportunities, average daily PM2.5, population size, preventable hospitalization rate, and violent crime rate. The expected number of cumulative confirmed cases and deaths depends on the dynamics of these five risk factors.
has issue date	2020-05-27(xsd:dateTime)
bibo:doi	10.1101/2020.05.26.20113787
has license	medrxiv
sha1sum (hex)	8577130c09d6b406187e7ea2c78b46416d5b8f3d
schema:url	https://doi.org/10.1101/2020.05.26.20113787
resource representing a document's title	COVID-Net: A deep learning based and interpretable predication model for the county-wise trajectories of COVID-19 in the United States
resource representing a document's body	covid:8577130c09d6b406187e7ea2c78b46416d5b8f3d#body_text
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