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The computational fluid dynamics-based epidemic model and the pandemic scenarios
Talib Dbouk1, Dimitris Drikakis2
1IMT Nord Europe, Institut Mines-Télécom, University of Lille, F-59000 Lille, France.
This study introduces a computational fluid dynamics model linking weather to airborne virus spread. It simulates COVID-19 outbreaks, highlighting the role of weather and fluid dynamics in pandemic prediction.
Area of Science:
- Epidemiology and Computational Fluid Dynamics
Background:
- Airborne virus transmission is influenced by weather patterns and seasonality.
- Understanding these dynamics is crucial for predicting pandemic outbreaks.
- Existing models often lack detailed environmental and fluid dynamics integration.
Purpose of the Study:
- To develop a Susceptible-Infected-Recovered (SIR) based epidemic model incorporating computational fluid dynamics (CFD).
- To investigate the relationship between weather conditions, seasonality, and airborne virus transmission dynamics.
- To simulate and predict COVID-19 transmission during its fifth wave in London.
Main Methods:
- Utilized computational fluid dynamics (CFD) principles.
- Developed a SIR-based epidemic model.
- Analyzed weather data and its correlation with virus transmission parameters.
- Simulated multiple COVID-19 fifth wave scenarios for London.
Main Results:
- The model successfully relates weather conditions to airborne virus transmission.
- Simulations provided insights into the potential peak and timing of the COVID-19 fifth wave in London.
- Demonstrated the significant impact of weather seasonality on transmission dynamics.
Conclusions:
- Computational fluid dynamics and advanced modeling are vital for future epidemiological studies.
- Weather conditions play a critical role in the dynamics of airborne virus transmission.
- The developed model offers a framework for enhanced pandemic preparedness and prediction.
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