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Using Random Effect Models to Produce Robust Estimates of Death Rates in COVID-19 Data.

Amani Almohaimeed1, Jochen Einbeck2, Najla Qarmalah3

  • 1Department of Statistics, College of Science, Qassim University, Buraydah 51482, Saudi Arabia.

International Journal of Environmental Research and Public Health
|November 26, 2022
PubMed
Summary

This study introduces a new statistical method using nested Poisson models to reliably estimate COVID-19 death rates, even with limited data. The approach improves accuracy by sharing information across countries, enhancing pandemic tracking.

Keywords:
COVID-19EM algorithmMAP rulePoisson modelcase ratescount datadeath ratesmixture modelrandom effectsrobustnessshrinkage

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Infectious Disease Modeling

Background:

  • Accurate tracking of infectious diseases like COVID-19 is vital during pandemics.
  • Uncertainties in case reporting, diagnosis, and treatment lead to unreliable death rate estimates.
  • Crude death rate calculations are often inaccurate or incomputable with small or zero case counts.

Purpose of the Study:

  • To develop a robust statistical methodology for analyzing COVID-19 data, specifically addressing unreliable death rate estimations.
  • To provide a reliable method for calculating death rates that is robust to small or zero counts in reported data.
  • To identify potential subgroups of countries with similar COVID-19 death rate behaviors.

Main Methods:

  • Utilized two nested Poisson models: an upper model for COVID-19 cases (offset by population size) and a lower model for deaths (offset by cases).
  • Employed a non-parametric maximum likelihood estimation approach, approximating random effect distributions via discrete mixtures.
  • Incorporated a "borrowing" of information across countries to stabilize estimates for both numerators and denominators of death rates.

Main Results:

  • The proposed nested Poisson model approach ensures positivity and robustness for both case and death counts, even with sparse data.
  • The method effectively handles uncertainties in reporting, providing more reliable death rate estimations.
  • The analysis successfully identified latent subpopulations of countries exhibiting similar patterns in their COVID-19 death rates.

Conclusions:

  • The novel nested Poisson modeling framework offers a significant methodological advancement for analyzing infectious disease data, particularly COVID-19.
  • This approach enhances the reliability of death rate calculations, crucial for effective pandemic monitoring and response.
  • The method's ability to detect country subgroups provides deeper insights into disease dynamics and facilitates comparative analysis.