Airborne pathogen monitoring and dispersion modelling on passenger ships: A review.

The COVID-19 pandemic demonstrated a profound inability of pre-pandemic passenger ship policies implemented by both ship operators and governmental authorities to detect and address newly emerging diseases. The essentiality of maritime transport puts into focus the risk of approach to address known and new emerging airborne infectious diseases that, due to increasing capacity, are likely to occur on passenger ships. In order to enhance the passenger experience, prepare shipping for pandemics like COVID-19, and improve the resilience and safety of the industry, this review critically synthesises existing literature on (1) monitoring ventilation conditions and aerosol dispersion, linking them to airborne transmission risk using airborne aerosols and ventilation performance as input parameters for computational fluid dynamics (CFD) simulations, and (2) modelling airborne disease transmission risk in controlled passenger ship environments. This review analysed 39 studies on aerosol monitoring, thermal comfort, and infection risk modelling on passenger ships (2000-2023). Additionally, 55 papers on CFD modelling of airborne pathogen dispersion were reviewed: 22 included validation, with most focused on built environments and only four specifically addressing ship environments. Two major challenges relate to the complexity and poorly characterised ventilation boundary conditions on passenger ships, and the other is the lack of suitable validation data. For this reason, ship experimental studies are required for CFD model validation. Only a handful of studies were found that have measured aerosol concentrations on board passenger ships. To the best of our knowledge, there have been no studies conducted on aerosol mass or airborne transmission sampling on board passenger ships or other types of vessels. The results of this review have the potential to create synergistic connections between experimental and modelling studies to inform, characterise and improve the development of numerical models that can accurately estimate infection risk on ships for prevention, mitigation and management of outbreaks.