A unified framework of epidemic spreading prediction by empirical mode decomposition based ensemble learning techniques
This work addresses epidemic prediction for public health by integrating online behavior data, but it is incremental as it combines existing techniques like EMD and ensemble learning in a new framework.
The authors tackled epidemic spreading prediction by proposing a unified SEIS-A framework that combines epidemic dynamics with online self-consultation behaviors, using empirical mode decomposition and ensemble learning, and validated it on Hand-foot-and-mouth disease data in Hong Kong, showing it outperforms other methods on fluctuating complex data.
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.