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Risk Assessment of COVID-19 Transmission on Cruise Ships Using Fuzzy Rules

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Artificial Intelligence Applications and Innovations (AIAI 2024)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 713))

Abstract

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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Notes

  1. 1.

    https://hs4u.eu/

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Acknowledgment

This paper is supported by the European Union’s Horizon Europe Research and Innovation Actions programme under grant agreement No 101069937, project name: HS4U (HEALTHY SHIP 4U). Views and opinions expressed are those of the author(s) only and do not necessarily reflect those of the European Union or the European Climate, Infrastructure, and Environment Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.

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Authors and Affiliations

  1. Department of Computer Science and Biomedical Informatics, University of Thessaly, 35100, Lamia, Greece

    Georgia Sovatzidi, Georgios Triantafyllou, George Dimas, Panagiotis G. Kalozoumis & Dimitris K. Iakovidis

  2. Institute for Advanced Modelling and Simulation, University of Nicosia, 2417, Nicosia, Cyprus

    Dimitris Drikakis & Ioannis W. Kokkinakis

  3. Health Policy Institute, 15123, Maroussi, Greece

    Ioannis A. Markakis & Christina Golna

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  1. Georgia Sovatzidi
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  2. Georgios Triantafyllou
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  3. George Dimas
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  4. Panagiotis G. Kalozoumis
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  5. Dimitris Drikakis
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  6. Ioannis W. Kokkinakis
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  7. Ioannis A. Markakis
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  8. Christina Golna
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  9. Dimitris K. Iakovidis
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Correspondence to Dimitris K. Iakovidis .

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Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  2. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  3. University of Abertay, Dundee, UK

    John Macintyre

  4. Ionian University, Corfu, Greece

    Markos Avlonitis

  5. Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

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Sovatzidi, G. et al. (2024). Risk Assessment of COVID-19 Transmission on Cruise Ships Using Fuzzy Rules. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Avlonitis, M., Papaleonidas, A. (eds) Artificial Intelligence Applications and Innovations. AIAI 2024. IFIP Advances in Information and Communication Technology, vol 713. Springer, Cham. https://doi.org/10.1007/978-3-031-63219-8_25

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  • DOI: https://doi.org/10.1007/978-3-031-63219-8_25

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-63219-8

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