Comparison of filtering methods for the modeling and retrospective forecasting of influenza epidemics.

ABSTRACT: A variety of filtering methods enable the recursive estimation of system state variables and inference of model parameters. These methods have found application in a range of disciplines and settings, including engineering design and forecasting, and, over the last two decades, have been applied to infectious disease epidemiology. For any system of interest, the ideal filter depends on the nonlinearity and complexity of the model to which it is applied, the quality and abundance of observations being entrained, and the ultimate application (e.g. forecast, parameter estimation, etc.). Here, we compare the performance of six state-of-the-art filter methods when used to model and forecast influenza activity. Three particle filters--a basic particle filter (PF) with resampling and regularizati

SUBMITTER: Yang W 

PROVIDER: S-EPMC3998879 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature