Analyzing the effects of observation function selection in ensemble Kalman filtering for epidemic models

The Ensemble Kalman Filter (EnKF) is a popular sequential data assimilation method that has been increasingly used for parameter estimation and forecast prediction in epidemiological studies. The observation function plays a critical role in the EnKF framework, connecting the unknown system variable...

Full description

Bibliographic Details
Main Authors:	Arnold, A. (Author), Mitchell, L. (Author)
Format:	Article
Language:	English
Published:	Elsevier Inc. 2021
Subjects:	Article biological model Computational analysis covariance Covariance matrix data assimilation Data assimilation disease transmission Ensemble Kalman Filter Ensemble Kalman filtering epidemic Epidemics Epidemiologic Methods Epidemiological studies epidemiology Epidemiology forecasting Forecasting human incidence inverse problem Inverse problems Kalman filter Kalman filtering Kalman filters modeling Models, Biological mortality rate Numerical experiments Observation model Observation model uncertainty parameter estimation Parameter estimation prevalence procedures signal noise ratio simulation State and parameter estimations Time varying parameter uncertainty analysis
Online Access:	View Fulltext in Publisher

Internet

View Fulltext in Publisher

Analyzing the effects of observation function selection in ensemble Kalman filtering for epidemic models

Internet

Similar Items