COVID crisis-aware maritime risk assessment: a Bayesian network analysis
COVID crisis-aware maritime risk assessment: a Bayesian network analysis
Maritime transportation is a vital component of global trade, yet maritime accidents pose significant risks with far-reaching consequences, including human casualties, economic losses, and environmental damage. The high-risk nature of this sector calls for in-depth, data-driven analysis to enhance risk assessment and accident prevention. While traditional approaches such as probabilistic risk analysis have advanced the understanding of maritime safety, they often overlook the evolving nature of risk under global crises, such as the COVID-19 pandemic (2020), the Ever Given blockage in the Suez Canal (March 2021), ongoing geopolitical conflicts (e.g., Russia-Ukraine since 2022), and the recent Red Sea crisis (2024). To overcome this critical research gap, this study proposes a crisis-aware maritime risk assessment framework based on Bayesian Network (BN), operationalised through a Tree-Augmented Naïve Bayes (TAN) model, using the COVID-19 pandemic as a case study. By analysing maritime accident patterns before and after the pandemic, the model reveals shifts in accident dynamics and emerging risk factors. The BN approach enables objective, interpretable analysis of how underlying causes and safety interventions have evolved in response to the crisis. Additionally, this study indirectly assesses the effectiveness of safety measures implemented during the pandemic and highlights areas for improvement to enhance future resilience. The findings provide actionable insights for policymakers, regulators, and industry stakeholders, supporting the development of more adaptive and robust maritime safety strategies to address future global disruptions.
Bayesian network, Maritime accidents, Maritime safety, Maritime transportation, Risk analysis
Li, Huanhuan
5e806b21-10a7-465c-9db3-32e466ae42f1
Jiao, Hang
af70e929-ff8d-4020-92a5-34c55f6aba7c
Chen, Zhong Shuo
59533721-057c-40d4-92ee-e849e993b704
Lam, Jasmine Siu Lee
8781a433-1624-44fb-90e9-934cf87083bc
Yang, Zaili
82d4eebc-4532-4343-8555-35169e79bb6d
27 October 2025
Li, Huanhuan
5e806b21-10a7-465c-9db3-32e466ae42f1
Jiao, Hang
af70e929-ff8d-4020-92a5-34c55f6aba7c
Chen, Zhong Shuo
59533721-057c-40d4-92ee-e849e993b704
Lam, Jasmine Siu Lee
8781a433-1624-44fb-90e9-934cf87083bc
Yang, Zaili
82d4eebc-4532-4343-8555-35169e79bb6d
Li, Huanhuan, Jiao, Hang, Chen, Zhong Shuo, Lam, Jasmine Siu Lee and Yang, Zaili
(2025)
COVID crisis-aware maritime risk assessment: a Bayesian network analysis.
Reliability Engineering & System Safety, 266, [111783].
(doi:10.1016/j.ress.2025.111783).
Abstract
Maritime transportation is a vital component of global trade, yet maritime accidents pose significant risks with far-reaching consequences, including human casualties, economic losses, and environmental damage. The high-risk nature of this sector calls for in-depth, data-driven analysis to enhance risk assessment and accident prevention. While traditional approaches such as probabilistic risk analysis have advanced the understanding of maritime safety, they often overlook the evolving nature of risk under global crises, such as the COVID-19 pandemic (2020), the Ever Given blockage in the Suez Canal (March 2021), ongoing geopolitical conflicts (e.g., Russia-Ukraine since 2022), and the recent Red Sea crisis (2024). To overcome this critical research gap, this study proposes a crisis-aware maritime risk assessment framework based on Bayesian Network (BN), operationalised through a Tree-Augmented Naïve Bayes (TAN) model, using the COVID-19 pandemic as a case study. By analysing maritime accident patterns before and after the pandemic, the model reveals shifts in accident dynamics and emerging risk factors. The BN approach enables objective, interpretable analysis of how underlying causes and safety interventions have evolved in response to the crisis. Additionally, this study indirectly assesses the effectiveness of safety measures implemented during the pandemic and highlights areas for improvement to enhance future resilience. The findings provide actionable insights for policymakers, regulators, and industry stakeholders, supporting the development of more adaptive and robust maritime safety strategies to address future global disruptions.
Text
1-s2.0-S0951832025009834-main
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More information
Accepted/In Press date: 21 August 2025
e-pub ahead of print date: 22 October 2025
Published date: 27 October 2025
Keywords:
Bayesian network, Maritime accidents, Maritime safety, Maritime transportation, Risk analysis
Identifiers
Local EPrints ID: 507294
URI: http://eprints.soton.ac.uk/id/eprint/507294
ISSN: 0951-8320
PURE UUID: ef554195-e772-4cef-a6c9-370d2bcf936c
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Date deposited: 03 Dec 2025 17:35
Last modified: 04 Dec 2025 03:09
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Contributors
Author:
Huanhuan Li
Author:
Hang Jiao
Author:
Zhong Shuo Chen
Author:
Jasmine Siu Lee Lam
Author:
Zaili Yang
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