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• W3081229384 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." @default.
• W3081229384 created "2020-09-01" @default.
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• W3081229384 date "2020-08-24" @default.
• W3081229384 modified "2023-09-25" @default.
• W3081229384 title "Machine Reasoning to Assess Pandemics Risks: Case of USS Theodore Roosevelt" @default.
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• W3081229384 doi "https://doi.org/10.48550/arxiv.2008.11040" @default.
• W3081229384 hasPublicationYear "2020" @default.
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