The computational fluid dynamics-based epidemic model and the pandemic scenarios

This study presents a computational fluid dynamics, susceptible-infected-recovered-based epidemic model that relates weather conditions to airborne virus transmission dynamics. The model considers the relationship between weather seasonality, airborne virus transmission, and pandemic outbreaks. We examine multiple scenarios of the COVID-19 fifth wave in London, United Kingdom, showing the potential peak and the period occurring. The study also shows the importance of fluid dynamics and computational modeling in developing more advanced epidemiological models in the future. Published under an exclusive license by AIP Publishing.

Automated summary

This study presents a computational fluid dynamics-based epidemic model that explores the relationship between weather conditions and airborne virus transmission dynamics. The model examines the impact of weather seasonality on airborne virus transmission and pandemic outbreaks, using multiple scenarios of the COVID-19 fifth wave in London, United Kingdom to demonstrate potential peak and occurrence period. The study highlights the significance of fluid dynamics and computational modeling in advancing epidemiological models.