Prediction of influenza rates by particle filtering
- Resource Type
- Conference
- Authors
- Closas, Pau; Bugallo, Monica F.; Coma, Ermengol; Mendez, Leonardo
- Source
- 2013 IEEE International Conference on Acoustics, Speech and Signal Processing Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on. :1046-1050 May, 2013
- Subject
- Signal Processing and Analysis
Mathematical model
Data models
Surveillance
Predictive models
Prediction algorithms
Diseases
Databases
Time series prediction
nonlinear systems
particle filtering
influenza
- Language
- ISSN
- 1520-6149
2379-190X
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.