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Related Experiment Video

Updated: Sep 24, 2025

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Multi-scale causality analysis between COVID-19 cases and mobility level using ensemble empirical mode decomposition

Jung-Hoon Cho1, Dong-Kyu Kim1,2, Eui-Jin Kim1,3

  • 1Department of Civil and Environmental Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea.

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|May 9, 2022
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Summary

Mobility levels significantly impact long-term COVID-19 spread, not short-term. Factors like age and politics influence intrastate spread, while distance and air travel affect interstate transmission. Policymakers should monitor long-term trends.

Keywords:
COVID-19Causal decompositionEnsemble empirical mode decompositionMobilityMulti-scale causality analysis

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Area of Science:

  • Epidemiology
  • Data Science
  • Public Health

Background:

  • The COVID-19 pandemic's global spread necessitated understanding transmission dynamics.
  • Investigating the causal factors of epidemic spread, particularly the link between mobility and disease transmission, remains challenging.

Purpose of the Study:

  • To comprehensively investigate the causal relationship between COVID-19 spread and mobility levels across multiple time scales.
  • To identify influencing factors on intrastate and interstate COVID-19 causal strength.
  • To provide insights for effective public health policy regarding social distancing measures.

Main Methods:

  • Ensemble Empirical Mode Decomposition (EEMD) to analyze multi time-scale data.
  • Causal decomposition approach to determine causality.
  • Linear regression analysis to assess the significance of influencing factors.
  • Clustering analysis to examine adherence to social distancing.

Main Results:

  • Mobility levels are strongly associated with long-term variations in COVID-19 cases, not short-term.
  • Median age and political orientation significantly influence intrastate COVID-19 causal strength at specific time scales.
  • Interstate COVID-19 spread is negatively associated with distance and positively with airline traffic in the long-term.
  • Higher GDP and political democracy correlate with greater adherence to social distancing.

Conclusions:

  • Social distancing policy effectiveness should be evaluated based on long-term COVID-19 trends, not short-term fluctuations.
  • Understanding multi time-scale relationships between mobility and disease spread is crucial for pandemic response.
  • Factors beyond simple proximity, such as demographics and political leanings, play a role in disease transmission patterns.