Prediction of influenza rates by particle filtering

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.