Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7496156 | Spatial and Spatio-temporal Epidemiology | 2014 | 10 Pages |
Abstract
We present a preliminary test of the Ensemble Optimal Statistical Interpolation (EnOSI) method for the statistical tracking of an emerging epidemic, with a comparison to its popular relative for Bayesian data assimilation, the Ensemble Kalman Filter (EnKF). The spatial data for this test was generated by a spatial susceptible-infectious-removed (S-I-R) epidemic model of an airborne infectious disease. Both tracking methods in this test employed Poisson rather than Gaussian noise, so as to handle epidemic data more accurately. The EnOSI and EnKF tracking methods worked well on the main body of the simulated spatial epidemic, but the EnOSI was able to detect and track a distant secondary focus of infection that the EnKF missed entirely.
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Authors
Loren Cobb, Ashok Krishnamurthy, Jan Mandel, Jonathan D. Beezley,