Data Driven Computing by the Morphing Fast Fourier Transform Ensemble Kalman Filter in Epidemic Spread Simulations

TL;DR

This paper introduces a novel FFT EnKF data assimilation method combined with morphing techniques, applied to epidemic spread simulations, offering a computationally efficient approach for real-time epidemic modeling.

Contribution

It presents a new FFT EnKF ensemble filtering method integrated with morphing for epidemic simulations, reducing computational costs and handling positional changes effectively.

Findings

Efficient data assimilation in epidemic models.

Effective handling of spatial shifts in epidemic spread.

Reduced ensemble size needed for accurate results.

Abstract

The FFT EnKF data assimilation method is proposed and applied to a stochastic cell simulation of an epidemic, based on the S-I-R spread model. The FFT EnKF combines spatial statistics and ensemble filtering methodologies into a localized and computationally inexpensive version of EnKF with a very small ensemble, and it is further combined with the morphing EnKF to assimilate changes in the position of the epidemic.