  In this paper, a unified susceptible-exposed-infected-susceptible-aware
(SEIS-A) framework is proposed to combine epidemic spreading with individuals'
on-line self-consultation behaviors. An epidemic spreading prediction model is
established based on the SEIS-A framework. The prediction process contains two
phases. In phase I, the time series data of disease density are decomposed
through the empirical mode decomposition (EMD) method to obtain the intrinsic
mode functions (IMFs). In phase II, the ensemble learning techniques which use
the on-line query data as an additional input are applied to these IMFs.
Finally, experiments for prediction of weekly consultation rates of
Hand-foot-and-mouth disease (HFMD) in Hong Kong are conducted to validate the
effectiveness of the proposed method. The main advantage of this method is that
it outperforms other methods on fluctuating complex data.
