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Calculate excess mortality during heatwaves using Hilbert-Huang transform algorithm.

Gang Xie1, Yuming Guo, Shilu Tong

  • 1CRC for Infrastructure and Engineering Asset Management (CIEAM), Science and Engineering Faculty, Queensland University of Technology, Brisbane, Australia. john.xie@qut.edu.au.

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Summary
This summary is machine-generated.

The Hilbert-Huang Transform (HHT) algorithm effectively estimates excess mortality during heatwaves by analyzing complex time series data. This novel approach offers a powerful tool for public health research and risk factor analysis.

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Area of Science:

  • Public Health
  • Time Series Analysis
  • Signal Processing

Background:

  • Heatwaves cause significant excess mortality, posing a public health challenge.
  • Estimating excess mortality is complex due to non-stationary daily mortality data and non-linear temperature-mortality associations.
  • The Hilbert-Huang Transform (HHT) algorithm, a novel signal processing tool, has not been applied to public health research.

Purpose of the Study:

  • To demonstrate the applicability and effectiveness of the HHT algorithm in analyzing public health data.
  • To apply HHT for estimating excess mortality associated with heatwave events.
  • To showcase HHT's potential in public health research.

Main Methods:

  • Developed specialized R functions to implement the HHT algorithm.
  • Decomposed daily mortality time series into trend and non-trend components using HHT.
  • Calculated excess mortality directly from the non-trend component of the time series.

Main Results:

  • Applied HHT to Brisbane (Australia) and Chicago (USA) heatwave data.
  • Estimated 62 excess deaths for the February 2004 Brisbane heatwave.
  • Estimated 510 excess deaths for the July 1995 Chicago heatwave, addressing mode mixing issues.
  • Utilized HHT decomposition for regression analysis to explore excess mortality risk factors.

Conclusions:

  • The HHT algorithm is a powerful and novel analytical tool for time series data.
  • HHT can consistently and efficiently decompose non-linear, non-stationary time series.
  • HHT has significant potential for diverse applications in public health research.