Since the novel coronavirus (COVID-19) outbreak in China, and due to the open accessibility of COVID-19 data, several researchers and modellers revisited the classical epidemiological models to evaluate their practical applicability. While mathematical compartmental models can predict various contagious viruses’ dynamics, their efficiency depends on the model parameters. Recently, several parameter estimation methods have been proposed for different models. In this study, we evaluated the Ensemble Kalman filter’s performance (EnKF) in the estimation of time-varying model parameters with synthetic data and the real COVID-19 data of Hubei province, China. Contrary to the previous works, in the current study, the effect of damping factors on an augmented EnKF is studied. An augmented EnKF algorithm is provided, and we present how the filter performs in estimating models using uncertain observational (reported) data. Results obtained confirm that the augumented-EnKF approach can provide reliable model parameter estimates. Additionally, there was a good fit of profiles between model simulation and the reported COVID-19 data confirming the possibility of using the augmented-EnKF approach for reliable model parameter estimation.

Mesh generation is one of key issues in Computational Fluid Dynamics. This paper presents an adaptive two-dimensional mesh refinement method based on the law of mass conservation. The method can be applied to a governing system that includes the law of mass conservation (continuity equation) for incompressible or compressible steady flows. Users can choose how many refinements they want to perform on the original mesh. The more the number of refinements, the less the error of calculations is. The refined meshes can identify the accurate locations of asymptotes and singular points and draw accurate closed streamlines if the number of refinements is big enough. We show two examples that demonstrate the claims.