Name
Title | ||
|---|---|---|
Data driven computing by the morphing fast Fourier transform ensemble Kalman filter in epidemic spread simulations. |
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. (C) 2010 Published by Elsevier Ltd. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.procs.2010.04.136 | Procedia Computer Science |
Keywords | Field | DocType |
Data assimilation,FFT,EnKF,Epidemic spread,Cell model,Covariogram | Spatial analysis,Data mining,Morphing,Data-driven,Computer science,Filter (signal processing),Fast Fourier transform,Artificial intelligence,Data assimilation,Ensemble Kalman filter,Cell model,Machine learning | Journal |
Volume | Issue | ISSN |
1 | 1 | 1877-0509 |
Citations | PageRank | References |
3 | 0.64 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jan Mandel | 1 | 444 | 69.36 |
| Jonathan D. Beezley | 2 | 101 | 14.55 |
| Loren Cobb | 3 | 3 | 0.98 |
| Ashok Krishnamurthy | 4 | 455 | 56.47 |