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• W2024923675 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." @default.
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• W2024923675 date "2014-07-01" @default.
• W2024923675 modified "2023-10-17" @default.
• W2024923675 title "Bayesian tracking of emerging epidemics using ensemble optimal statistical interpolation" @default.
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• W2024923675 doi "https://doi.org/10.1016/j.sste.2014.06.004" @default.
• W2024923675 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4143132" @default.
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