Development of a mathematical model to predict the health impact and duration of SARS-CoV-2 outbreaks on board cargo vessels

Kok Yew Ng, Tudor A. Codreanu, Meei Mei Gui, Pardis Biglarbeigi, Dewar Finlay, James McLaughlin

Research output: Other contribution

Abstract

The Coronavirus Disease (COVID-19) pandemic has brought significant impact onto the maritime activities worldwide, including disruption to global trade and supply chains. The ability to predict the evolution and duration of a COVID-19 outbreak on cargo vessels would inform a more nuanced response to the event and provide a more precise return-to-trade date. A SEIQ(H)R (Susceptibility-–Exposed–Infected—Quarantine-–(Hospitalisation)—Removed/Recovered) model is developed and fit-tested to simulate the transmission dynamics of COVID-19 on board cargo vessels of up to 60 crew. Due to specific living and working circumstances on board cargo vessels, instead of utilising the reproduction number, we consider the highest fraction of crew members who share the same nationality to quantify the transmissibility of the disease. The performance of the model is verified using case studies based on data collected during COVID-19 outbreaks on three cargo vessels in Western Australia during 2020. The simulations show that the model can forecast the time taken for the transmission dynamics on each vessel to reach their equilibriums, providing informed predictions on the evolution of the outbreak, including hospitalisation rates and duration. The model demonstrates that (a) all crew members are susceptible to infection; (b) their roles on board is a determining factor in the evolution of the outbreak; (c) an unmitigated outbreak could affect the entire crew and continue on for many weeks. The ability to model the evolution of an outbreak, both in duration and severity, is essential to predict outcomes and to plan for the best response strategy. At the same time, it offers a higher degree of certainty regarding the return to trade, which is of significant importance to multiple stakeholders.
Original languageEnglish
TypeStudy - preprint
PublisherCold Spring Harbor Laboratory Press
Number of pages16
DOIs
Publication statusPublished - 27 Jun 2022

Bibliographical note

Funding Statement
This study did not receive any funding

Keywords

  • COVID-19
  • Coronavirus
  • Mathematical modelling
  • Cargo vessels
  • Western Australia

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