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Count regression models for COVID-19.

Stephen Chan1, Jeffrey Chu2, Yuanyuan Zhang3

  • 1Department of Mathematics and Statistics, American University of Sharjah, United Arab Emirates.

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|November 9, 2020
PubMed
Summary
This summary is machine-generated.

Statistical models using negative binomial distribution effectively predicted daily COVID-19 cases globally. These simple models offer a clear overview of trends and support epidemiological analysis.

Keywords:
CoronavirusEpidemiologyNegative binomial distributionPoisson distribution

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Data Science

Background:

  • The COVID-19 pandemic, caused by a novel coronavirus, emerged in late 2019, leading to a global health crisis.
  • Comparisons have been drawn between the current pandemic and the 1918 Spanish flu, highlighting the significant impact of novel respiratory viruses.

Purpose of the Study:

  • To statistically model and analyze the daily incidence of COVID-19 across eighteen countries.
  • To investigate the applicability of count regression models for predicting daily new COVID-19 cases in the short term.

Main Methods:

  • Utilized count regression models to analyze daily new COVID-19 case data from eighteen countries.
  • Identified the best-fitting statistical distribution and link function for modeling COVID-19 incidence.

Main Results:

  • Count regression models, particularly the negative binomial distribution with a log link function, demonstrated effectiveness in fitting and predicting daily COVID-19 cases.
  • These models provide a straightforward approach to understanding global COVID-19 trends.

Conclusions:

  • The negative binomial regression model offers a simple yet effective method for analyzing and forecasting COVID-19 daily cases.
  • While not a replacement for specialized epidemiological models, this approach provides valuable insights and can complement other analyses to inform public health strategies.