Abstract
This work has started from the necessity of improving the accuracy of numerical simulations of COVID-19 transmission. Coughing is one of the most effective ways to transmit SARS-CoV-2, the strain of coronavirus that causes COVID-19. Cough is a spontaneous reflex that helps to protect the lungs and airways from unwanted irritants and pathogens and it involves droplet expulsion at speeds close to 50 miles/h. Unfortunately, it’s also one of the most efficient ways to spread diseases, especially respiratory viruses that need host cells in which to reproduce. Computational Fluid Dynamics (CFD) are a powerful way to simulate droplets expelled by mouth and nose when people are coughing and/or sneezing. As with all numerical models, the models for coughing and sneezing introduce uncertainty through the selection of scales and parameters. Considering these uncertainties is essential for the acceptance of any numerical simulation. Numerical forecasting models often use Data Assimilation (DA) methods for uncertainty quantification in the medium to long-term analysis. DA is the approximation of the true state of some physical system at a given time by combining time-distributed observations with a dynamic model in an optimal way. DA incorporates observational data into a prediction model to improve numerically forecast results. In this paper, we develop a Variational Data Assimilation model to assimilate direct observation of the physical mechanisms of droplet formation at the exit of the mouth during coughing. Specifically, we use high-speed imaging, from prior research work, which directly examines the fluid fragmentation at the exit of the mouths of healthy subjects in a sneezing condition. We show the impact of the proposed approach in terms of accuracy with respect to CFD simulations.
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Acknowledgements
This work is supported by the EP/V036777/1 Risk EvaLuatIon fAst iNtelligent Tool (RELIANT) for COVID19 and the EP/T000414/1 PREdictive Modelling with QuantIfication of UncERtainty for MultiphasE Systems (PREMIERE).
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Arcucci, R. et al. (2022). Merging Real Images with Physics Simulations via Data Assimilation. In: Chaves, R., et al. Euro-Par 2021: Parallel Processing Workshops. Euro-Par 2021. Lecture Notes in Computer Science, vol 13098. Springer, Cham. https://doi.org/10.1007/978-3-031-06156-1_21
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