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Multi-step influenza forecasting through singular value decomposition and kernel ridge regression with MARCOS-guided

Guo Hongliang1, Zhang Zhiyao1, Iman Ahmadianfar2

  • 1College of Information Technology, Jilin Agricultural University, Changchun, 130118, China.

Computers in Biology and Medicine
|December 29, 2023
PubMed
Summary
This summary is machine-generated.

Accurate influenza forecasting is crucial for public health. A new hybrid model, MVMD-H-SKRR-GBO, significantly improves predictions of weekly Influenza-like illness (ILI) rates.

Keywords:
GBO algorithmInfluenza forecastingKernel ridge regressionMARCOS methodMulti-step aheadSingular value decomposition (SVD)

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Area of Science:

  • Public Health
  • Epidemiology
  • Computational Science

Background:

  • Influenza outbreaks pose a significant public health challenge.
  • Accurate forecasting of Influenza-like illness (ILI) rates is essential for effective public health interventions.
  • Existing models often struggle with the complexity and non-stationarity of ILI data.

Purpose of the Study:

  • To develop and validate a novel hybrid machine learning model for improved multi-step-ahead prediction of weekly ILI rates.
  • To enhance the accuracy of influenza outbreak forecasting in Southern and Northern China.
  • To address the non-stationarity and complexity inherent in ILI time-series data.

Main Methods:

  • A hybrid model (MVMD-H-SKRR-GBO) was developed, combining multivariate variational mode decomposition (MVMD), singular value decomposition with kernel ridge regression (SKRR), and gradient-based optimization (GBO).
  • Feature selection was performed using XGBoost to identify optimal precursor information.
  • The model decomposes ILI signals, incorporates lagged components, and aggregates forecasts for 4- and 7-weekly ahead predictions.
  • Model performance was validated against deep random vector functional link (dRVFL), Ridge regression, and gated recurrent unit neural network (GRU) models using the MARCOS multi-criteria decision-making method.

Main Results:

  • The MVMD-H-SKRR-GBO model demonstrated superior accuracy in predicting weekly ILI rates compared to other benchmark models.
  • Key performance metrics included R=0.946, RMSE=0.388, IA=0.970, and U95% =1.075 at the t + 7 horizon.
  • The study successfully addressed data non-stationarity and complexity through signal decomposition techniques.

Conclusions:

  • The developed MVMD-H-SKRR-GBO model offers a highly accurate and robust approach for multi-step-ahead forecasting of ILI rates.
  • This advanced forecasting capability can significantly aid public health planning and response to influenza outbreaks.
  • The hybrid machine learning paradigm presents a promising direction for epidemiological time-series prediction.