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Current trends and future prediction of novel coronavirus disease (COVID-19) epidemic in China: a dynamical modeling

Kai Wang1, Zhen Zhen Lu2, Xiao Meng Wang3

  • 1College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830011, China.

Mathematical Biosciences and Engineering : MBE
|September 29, 2020
PubMed

Insights

This study models COVID-19 transmission dynamics, predicting peak infections around February 11, 2020, and control by May 18, 2020. Effective measures include reducing contact and enhancing risk population tracing.

Area of Science:

  • Epidemiology
  • Mathematical Modeling

Background:

  • The COVID-19 pandemic emerged in December 2019, rapidly spreading across China.
  • Urgent need to predict infection peak, final size, and control timelines.

Purpose of the Study:

  • To propose a dynamical transmission model for COVID-19.
  • To predict the peak time and final size of daily confirmed cases.
  • To estimate the basic reproductive number (R0) of COVID-19.

Main Methods:

  • Developed a dynamical transmission model incorporating contact tracing and quarantine.
  • Employed Markov Chain Monte Carlo (MCMC) algorithm for predictions.
  • Estimated the basic reproductive number (R0) for COVID-19.

Main Results:

  • Estimated COVID-19 basic reproductive number (R0) at 5.78 (95% CI: 5.71-5.89).
  • Predicted daily confirmed cases to peak around February 11, 2020 (4066 cases; 95% CI: 3898-4472).
  • Projected COVID-19 control by approximately May 18, 2020.

Conclusions:

  • Contact reduction and enhanced risk population tracing are effective control measures.
  • The model provides valuable insights for managing the COVID-19 outbreak.
  • Timely predictions aid public health response strategies.

Keywords:
Markov Chain Monte Carlobasic reproduction numberdynamical modelnovel coronaviruspeak time

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