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Bayesian poisson regression tensor train decomposition model for learning mortality pattern changes during COVID-19

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This study analyzed Italian mortality data from 2015-2020 to understand COVID-19's impact on other causes of death. The findings reveal complex relationships between the pandemic, interventions, and mortality patterns.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • The COVID-19 pandemic has caused significant global excess mortality.
  • The specific impact of COVID-19 on mortality rates from non-COVID causes requires detailed investigation.

Purpose of the Study:

  • To analyze the broader effects of COVID-19 on various causes of death using Italian mortality data.
  • To develop and apply a novel statistical model for dissecting complex mortality patterns.

Main Methods:

  • Utilized official Italian monthly mortality data (January 2015 - December 2020).
  • Developed a Bayesian model combining Poisson regression and tensor train decomposition.
  • Employed Metropolis-Hastings within Gibbs algorithm for posterior inference.

Main Results:

  • Identified differential effects of interventions on cause-specific mortality rates.
  • Revealed insights into the relationship between COVID-19 and other causes of death.
  • Uncovered latent classes associated with demographics, temporal trends, and mortality causes.

Conclusions:

  • The developed statistical approach effectively models complex, high-dimensional mortality data.
  • COVID-19 has multifaceted impacts on mortality beyond direct fatalities, influencing other causes of death.
  • The findings provide a deeper understanding of pandemic-related mortality dynamics.