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Pandemic Equation for Describing and Predicting COVID19 Evolution.

Michael Shur1,2

  • 1Rensselaer Polytechnic Institute, Troy, NY USA.

Journal of Healthcare Informatics Research
|January 13, 2021
PubMed
Summary

This study introduces a Pandemic Equation to model COVID-19 dynamics, incorporating mitigation measures and vaccination. The model predicts pandemic curves, aiding in forecasting and understanding disease spread with easier parameter extraction.

Keywords:
COVID19MitigationPandemicQuarantine

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health

Background:

  • The COVID-19 pandemic necessitated dynamic modeling to understand transmission.
  • Existing models often lack flexibility in incorporating real-time mitigation effects.

Purpose of the Study:

  • To describe COVID-19 pandemic dynamics, including mitigation measures, quarantine effects, and vaccination.
  • To introduce a novel mathematical model for pandemic prediction.

Main Methods:

  • Derivation of the "Pandemic Equation" using a time-varying growth constant.
  • Modeling asymmetric pandemic curves (steeper rise than fall).
  • Utilizing multiple pandemic locations for parameter extraction and uncertainty quantification.

Main Results:

  • The Pandemic Equation accurately describes pandemic curves influenced by mitigation and vaccination.
  • Quarantine removal and business reopening correlate with predicted pandemic spikes.
  • Vaccination effectively reduces predicted daily infections.
  • Cross-location parameter extraction enables prediction in early-stage pandemics.

Conclusions:

  • The Pandemic Equation offers a flexible and accurate approach to modeling infectious disease dynamics.
  • The model's ease of parameter extraction is suitable for AI integration.
  • This framework aids in predicting and managing future pandemic waves.