A unified framework of epidemic spreading prediction by empirical mode decomposition based ensemble learning techniques

TL;DR

This paper introduces a novel epidemic prediction framework combining empirical mode decomposition with ensemble learning, incorporating online self-consultation behaviors to improve accuracy on complex data.

Contribution

It presents a unified SEIS-A model integrating online behaviors with epidemic data, utilizing EMD and ensemble learning for enhanced prediction accuracy.

Findings

Outperforms existing methods on complex fluctuating data

Effective in predicting weekly HFMD consultation rates

Integrates online self-consultation data into epidemic modeling

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.