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Multi-step ahead meningitis case forecasting based on decomposition and multi-objective optimization methods.

Matheus Henrique Dal Molin Ribeiro1, Viviana Cocco Mariani2, Leandro Dos Santos Coelho3

  • 1Graduate Program in Industrial & Systems Engineering (PPGEPS), Pontifical Catholic University of Parana (PUCPR), 1155, Rua Imaculada Conceicao, Curitiba, Parana, 80215-901, Brazil; Department of Mathematics, Federal Technological University of Parana (UTFPR), Via do Conhecimento, KM 01 - Fraron, Pato Branco, Parana, 85503-390, Brazil.

Journal of Biomedical Informatics
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PubMed
Summary

This study introduces a hybrid learning framework for forecasting meningitis cases, improving accuracy and stability in public health planning. The novel approach significantly reduces forecast errors compared to existing methods.

Keywords:
Ensemble empirical mode decompositionEnsemble learning modelsMeningitisMulti-objective optimizationTime series forecasting

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Area of Science:

  • Epidemiology
  • Computational Health Science
  • Time Series Analysis

Background:

  • Epidemiological time series forecasting is crucial for public health systems to prevent epidemics.
  • Accurate forecasting allows for strategic planning and resource allocation to mitigate disease outbreaks.

Purpose of the Study:

  • To develop and evaluate a hybrid learning framework for multi-step-ahead forecasting of monthly meningitis cases in Brazil.
  • To enhance forecasting accuracy and stability using ensemble methods and multi-objective optimization.

Main Methods:

  • Ensemble Empirical Mode Decomposition (EEMD) to decompose time series data into intrinsic mode functions.
  • Integration of five forecasting models with decomposed components using Weighted Integration (WI).
  • Multi-Objective Optimization (MOO) via Non-Dominated Sorting Genetic Algorithm II and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for weight selection.

Main Results:

  • The proposed hybrid framework achieved more accurate and stable forecasts across twelve scenarios.
  • Forecast errors were statistically lower than alternative methods in 89.17% of cases.
  • The optimized heterogeneous ensemble approach demonstrated superior performance in out-of-sample generalization.

Conclusions:

  • Combining EEMD, heterogeneous ensembles, and optimized weighted integration yields precise and stable epidemiological forecasts.
  • The developed model offers a promising tool for public health managers to support evidence-based decision-making.
  • This framework advances the field of epidemiological forecasting by integrating advanced decomposition and optimization techniques.