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True epidemic growth construction through harmonic analysis.

Steven G Krantz1, Peter Polyakov2, Arni S R Srinivasa Rao3

  • 1Department of Mathematics, Washington University in St. Louis, Campus Box 1146 One Brookings Drive St. Louis, Missouri, MI 63130, USA.

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This study introduces a novel two-phase method using graph theory and wavelets to accurately model epidemic growth. The approach reconstructs complete epidemic data from partial information, offering a new tool for public health analysis.

Keywords:
Convergence of graphsPartial to complete dataWavelets

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Area of Science:

  • Epidemiology
  • Data Science
  • Computational Biology

Background:

  • Accurate epidemic growth modeling is crucial for public health interventions.
  • Existing methods often struggle with incomplete or sparse data.
  • Developing robust data reconstruction techniques is essential.

Purpose of the Study:

  • To propose a novel two-phase procedure for constructing true epidemic growth.
  • To address the challenge of incomplete data in epidemiological studies.
  • To create an adaptable framework for machine learning applications.

Main Methods:

  • Phase 1: Employed a graph-theory-based approach to update and infer missing data points.
  • Phase 2: Utilized wavelet analysis to generate plausible complete epidemic data from the updated partial data.
  • Validated the procedure with two numerical examples.

Main Results:

  • Successfully demonstrated the reconstruction of complete epidemic growth data from partial datasets.
  • The proposed method proved to be implementable and adaptable.
  • Numerical examples confirmed the efficacy of the two-phase procedure.

Conclusions:

  • The developed two-phase procedure offers a novel and effective method for epidemic growth modeling.
  • This approach enhances the ability to analyze epidemic trends using incomplete data.
  • The framework is adaptable for integration into machine learning models for predictive epidemiology.