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Dynamic models for Coronavirus Disease 2019 and data analysis.

Nian Shao1, Min Zhong2, Yue Yan3

  • 1School of Mathematical Sciences Fudan University Shanghai China.

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Two dynamic models, TDD-NCP and Fudan-CCDC, track Coronavirus Disease 2019 (COVID-19) data. Findings show early, effective isolation is crucial for epidemic containment.

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Area of Science:

  • Epidemiology
  • Mathematical Modeling

Background:

  • The Coronavirus Disease 2019 (COVID-19) pandemic necessitated robust tracking and prediction methods.
  • Existing models required refinement to account for disease-specific characteristics like incubation periods.

Purpose of the Study:

  • To introduce and evaluate two novel time delay dynamic models for tracking COVID-19.
  • To assess the impact of epidemic control strategies based on model outputs.

Main Methods:

  • Development of the Time Delay Dynamical-Novel Coronavirus Pneumonia (TDD-NCP) model, incorporating a latent period into differential equations.
  • Establishment of the Fudan-Chinese Center for Disease Control and Prevention (CCDC) model by parameterizing the TDD-NCP model using public CDCC data.
  • Application of both models to cumulative confirmed case data from various regions in China and globally.

Main Results:

  • Both the TDD-NCP and Fudan-CCDC models effectively tracked COVID-19 data.
  • Model analyses demonstrated a strong correlation between early and effective isolation measures and epidemic containment.

Conclusions:

  • Time delay dynamic models offer valuable insights into epidemic progression.
  • The study underscores the critical importance of timely and efficient isolation strategies in controlling infectious disease outbreaks like COVID-19.