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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Data-driven mathematical modeling approaches for COVID-19: A survey.

Jacques Demongeot1, Pierre Magal2

  • 1Université Grenoble Alpes, AGEIS EA7407, La Tronche, F-38700, France.

Physics of Life Reviews
|August 14, 2024
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Summary

This review details phenomenological modeling methods for COVID-19 epidemic waves, including single and multiple waves. It analyzes reported and unreported cases, aiding in understanding disease dynamics and forecasting future trends.

Keywords:
Contagious diseaseEpidemic modelsEpidemic waveParameters identificationPhenomenological modelsSocial changesTime dependent modelsTime series

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health

Background:

  • The COVID-19 pandemic necessitated robust methods for tracking and predicting disease spread.
  • Understanding epidemic dynamics is crucial for effective public health interventions.

Purpose of the Study:

  • To review and present phenomenological modeling approaches for COVID-19 case evolution.
  • To analyze single and multiple epidemic waves, including endemic periods.
  • To compare phenomenological and mechanistic modeling strategies.

Main Methods:

  • Systematic literature review of 260 articles across 11 sections.
  • Phenomenological modeling of reported and unreported COVID-19 cases.
  • Development and application of multi-compartmental models (with/without age structure).

Main Results:

  • Detailed presentation of modeling methods for exponential growth, complete waves, and successive waves.
  • Simulations of COVID-19 case evolution for 10 diverse geographical regions.
  • Emphasis on the utility of phenomenological over mechanistic approaches for this data.

Conclusions:

  • Phenomenological modeling provides a valuable framework for understanding and forecasting COVID-19 dynamics.
  • The review synthesizes a broad range of modeling techniques relevant to infectious disease outbreaks.
  • The findings support the application of these models for public health surveillance and planning.