| Cruise ships constitute a popular means of vacationing for millions of people each year. However, due to the on-board conditions, e.g., densely populated areas, highly transmissible respiratory diseases, such as COVID-19, are a common cause of outbreaks. Hence, accurate assessment of the transmission risk (TR) is crucial. Recent approaches focus on long-term forecasting of such events; however, the limited availability and inconsistency of relevant data poses a challenge for developing short-term and data-driven methods. To this end, this work proposes a novel short-term knowledge-based method implemented through fuzzy rules for assessing the TR in cruise ships. The use of fuzzy rules, developed by domain experts and information extracted from the literature, assists in dealing with the data limitations. In contrast to previous approaches, the proposed method considers information deriving from various sensors and the ship information system in accord with a recently proposed smart ship design. Moreover, the fuzzy TR assessment estimates the confidence of an inferred decision, quantifying the uncertainty regarding its results. Evaluation via agent-based simulations demonstrates the effectiveness of the proposed method across different scenarios. |
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