标题： 基于形态快速傅里叶变换集合卡尔曼滤波的流行病传播模拟数据驱动计算 显示英文标题

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

摘要： 提出的FFT EnKF数据同化方法应用于基于S-I-R传播模型的随机细胞模拟。FFT EnKF将空间统计和集合过滤方法结合，形成一种局部化且计算成本低的EnKF版本，使用非常小的集合，并进一步与变形EnKF结合，以同化疫情位置的变化。 显示英文摘要

摘要： 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.