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A Statistical Definition of Epidemic Waves.

Levente Kriston1

  • 1Department of Medical Psychology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, D-20246 Hamburg, Germany.

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|July 25, 2023
PubMed
Summary

This study introduces a simple method to detect infectious disease epidemic waves early using daily case counts. It compares exponential and linear growth models to identify potential surges for better public health interventions.

Keywords:
Bayesian analysisCOVID-19SARS-CoV-2computational biologyepidemiologic methods

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health

Background:

  • Timely identification of infectious disease surges is crucial for effective resource allocation and intervention planning.
  • Delayed detection of epidemic phases can lead to severe public health consequences.
  • Existing methods may not always provide early warnings for epidemic wave detection.

Purpose of the Study:

  • To present a straightforward method for evaluating the likelihood of an epidemic wave using only daily new case count data.
  • To develop a quantitative measure for early epidemic wave detection.
  • To compare exponential and linear growth models for characterizing epidemic dynamics.

Main Methods:

  • The study proposes a measure comparing two models: one assuming exponential growth and the other linear growth in daily new cases.
  • The approach utilizes the coefficient of determination from two regression analyses to approximate a Bayes factor.
  • This Bayes factor quantifies support for the exponential model over the linear model, aiding in epidemic wave detection.

Main Results:

  • The proposed measure effectively detects epidemic waves at an early stage.
  • The method identified early epidemic waves in the coronavirus epidemic data from three analyzed countries.
  • Detected waves were not apparent until retrospective analysis of case count data.

Conclusions:

  • The developed approach offers a simple yet effective tool for early epidemic wave detection.
  • This method can inform public health decision-making and resource allocation during epidemics.
  • Further research is needed to assess generalizability and compare performance against other epidemic detection methods.