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The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
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Revisiting Pearl's influenza studies by bootstrapping for forward variable selection with a null factor.

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This study re-examines the 1919 Spanish Flu epidemic using advanced statistical methods. Pre-pandemic death rates from heart disease and all causes were found to be key predictors of the epidemic

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Historical Public Health

Background:

  • Raymond Pearl's 1919-1921 studies investigated factors influencing the Spanish Flu's severity in US cities.
  • Pearl utilized partial correlation coefficients to analyze demographic and disease death rates.
  • A need exists to apply modern statistical techniques to historical epidemiological data.

Purpose of the Study:

  • To re-evaluate factors predicting Spanish Flu epidemic severity using contemporary statistical methods.
  • To compare results with Raymond Pearl's original findings from the early 20th century.
  • To demonstrate the utility of advanced variable selection in historical epidemiology.

Main Methods:

  • Applied bootstrap simulation with forward variable selection and a null factor for generalized linear regression.
  • Utilized AICc (Akaike Information Criterion with correction) for model validation.
  • Employed a null factor (a random, independent variable) to assess term significance in model selection.

Main Results:

  • Pre-pandemic death rates from organic heart disease and all causes were identified as highly predictive of pandemic severity.
  • Results largely corroborate Pearl's original conclusions regarding key predictive factors.
  • Substantive nuances were identified, suggesting refinements to historical epidemiological models.

Conclusions:

  • State-of-the-art variable selection methodology is valuable for analyzing historical epidemic data.
  • Pre-existing health conditions, specifically heart disease mortality, significantly influenced Spanish Flu's impact.
  • Modern statistical approaches can enhance understanding of past public health crises.