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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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A machine learning and clustering-based approach for county-level COVID-19 analysis.

Charles Nicholson1,2, Lex Beattie2, Matthew Beattie2

  • 1School of Industrial and Systems Engineering, University of Oklahoma, Norman, Oklahoma, United States of America.

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Summary
This summary is machine-generated.

This study identifies key county-level factors influencing COVID-19 spread before vaccines. It clusters regions to improve disease analysis and forecasting for public health.

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Area of Science:

  • Epidemiology
  • Public Health
  • Data Science

Background:

  • The COVID-19 pandemic presented unprecedented challenges for disease forecasting.
  • Regional variations in human behavior and environmental factors complicate accurate predictive modeling.
  • Limited historical data and unique regional characteristics hinder precise forecasting efforts.

Purpose of the Study:

  • To identify critical county-level factors influencing COVID-19 propagation before widespread vaccine availability.
  • To develop a feature subspace for analyzing disease spread.
  • To aggregate counties into meaningful clusters for refined disease analysis.

Main Methods:

  • Employed supervised and unsupervised machine learning methods.
  • Analyzed county-level demographic, mobility, weather, medical capacity, and health data.
  • Utilized feature selection to identify critical predictive factors.

Main Results:

  • Identified key demographic, mobility, weather, and health factors impacting COVID-19 spread.
  • Successfully clustered counties based on identified critical factors.
  • Established a refined feature subspace for improved disease analysis.

Conclusions:

  • County-level factors significantly influence COVID-19 transmission dynamics.
  • Clustering facilitates more targeted and effective public health interventions.
  • The identified feature subspace supports enhanced regional disease modeling and forecasting efforts.