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Assessment of risks of pandemics to communities and workplaces requires anintelligent decision support system (DSS). The core of such DSS must be basedon machine reasoning techniques such as inference and shall be capable ofestimating risks and biases in decision making. In this paper, we use a causalnetwork to make Bayesian inference on COVID-19 data, in particular, assessrisks such as infection rate and other precaution indicators. Unlike otherstatistical models, a Bayesian causal network combines various sources of datathrough joint distribution, and better reflects the uncertainty of theavailable data. We provide an example using the case of the COVID-19 outbreakthat happened on board of USS Theodore Roosevelt in early 2020.

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