Abstract

This paper advocates for causal models of the emergency management cycle (EMC) for decision support in epidemic or pandemic scenarios. The model capability is demonstrated for the case of the COVID-19 attack at the NATO flagship USS Theodore Roosevelt in early 2020. Computational intelligence is a reasonable approach for dealing with uncertainties such as low reliability of information and source credibility. The proposed EMC causal models enable the development of countermeasures for epidemiological attacks using the notion of gaps in the four EMC phases: mitigation, preparedness, response, and recovery. In particular, the EMC problem can be formulated and formalized as bridging the identified technology–society gap, e.g., mitigation of risks and biases; and machine reasoning can be incorporated at any level of the EMC decision-making. Using available real-world data on the USS Theodore Roosevelt outbreak, we show how machine reasoning mechanisms can help the captain to make more reliable decisions in critical epidemiological situations.

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