About: Machine Reasoning to Assess Pandemics Risks: Case of USS Theodore Roosevelt

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

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Machine Reasoning to Assess Pandemics Risks: Case of USS Theodore Roosevelt
Creator	Lai, Kenneth Yanushkevich, Svetlana
source	ArXiv
abstract	Assessment of risks of pandemics to communities and workplaces requires an intelligent decision support system (DSS). The core of such DSS must be based on machine reasoning techniques such as inference and shall be capable of estimating risks and biases in decision making. In this paper, we use a causal network to make Bayesian inference on COVID-19 data, in particular, assess risks such as infection rate and other precaution indicators. Unlike other statistical models, a Bayesian causal network combines various sources of data through joint distribution, and better reflects the uncertainty of the available data. We provide an example using the case of the COVID-19 outbreak that happened on board of USS Theodore Roosevelt in early 2020.
has issue date	2020-08-24(xsd:dateTime)
has license	arxiv
sha1sum (hex)	2cede6e08d9e3c5dea1b4ede2eada147a3d999c2
resource representing a document's title	Machine Reasoning to Assess Pandemics Risks: Case of USS Theodore Roosevelt
resource representing a document's body	covid:2cede6e08d9e3c5dea1b4ede2eada147a3d999c2#body_text
is schema:about of	named entity 'Pandemics' named entity 'causal model' named entity 'quarantining' named entity 'False Negative' named entity 'COVID-19' »more»

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