A unified framework of epidemic spreading prediction by empirical mode decomposition based ensemble learning techniques
arXiv:1901.01144 · doi:10.1109/TCSS.2019.2915615
Abstract
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.
arXiv commentSome issues need to be addressed in this manuscript
References in corpus (6)
- Epidemic processes in complex networks
- Dynamical interplay between awareness and epidemic spreading in multiplex networks
- Accurate estimation of influenza epidemics using Google search data via ARGO
- Effect of risk perception on epidemic spreading in temporal networks
- Universal mean-field framework for SIS epidemics on networks, based on graph partitioning and the isoperimetric inequality
- Interplay between cost and benefits triggers nontrivial vaccination uptake