@inproceedings{3871385606ea4a018c8e33e25081ecc4,
title = "Prediction of influenza rates by particle filtering",
abstract = "Predicting the course of influenza rates is extremely useful for the efficacy of planned vaccination programs. In this paper we address this problem by stating a dynamic state-space model that mathematically describes both the evolution of influenza rates and the observations obtained by a surveillance system. We then propose a prediction method based on particle filtering that accommodates the nonlinear nature of the model. Using real data we estimate the necessary model functions prior to the prediction step. Computer simulations reveal promising results of the proposed method.",
keywords = "influenza, nonlinear systems, particle filtering, Time series prediction",
author = "Pau Closas and Bugallo, \{Monica F.\} and Ermengol Coma and Leonardo Mendez",
year = "2013",
month = oct,
day = "18",
doi = "10.1109/ICASSP.2013.6637809",
language = "English",
isbn = "9781479903566",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "1046--1050",
booktitle = "2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings",
note = "2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 ; Conference date: 26-05-2013 Through 31-05-2013",
}