Estimation of the time-varying reproduction number of COVID-19 outbreak in China

Int J Hyg Environ Health. 2020 Jul:228:113555. doi: 10.1016/j.ijheh.2020.113555. Epub 2020 May 11.

Abstract

Background: The 2019 novel coronavirus (COVID-19) outbreak in Wuhan, China has attracted world-wide attention. As of March 31, 2020, a total of 82,631 cases of COVID-19 in China were confirmed by the National Health Commission (NHC) of China.

Methods: Three approaches, namely Poisson likelihood-based method (ML), exponential growth rate-based method (EGR) and stochastic Susceptible-Infected-Removed dynamic model-based method (SIR), were implemented to estimate the basic and controlled reproduction numbers.

Results: A total of 198 chains of transmission together with dates of symptoms onset and 139 dates of infections were identified among 14,829 confirmed cases outside Hubei Province as reported as of March 31, 2020. Based on this information, we found that the serial interval had an average of 4.60 days with a standard deviation of 5.55 days, the incubation period had an average of 8.00 days with a standard deviation of 4.75 days and the infectious period had an average of 13.96 days with a standard deviation of 5.20 days. The estimated controlled reproduction numbers, Rc, produced by all three methods in all analyzed regions of China are significantly smaller compared with the basic reproduction numbers R0.

Conclusions: The controlled reproduction number in China is much lower than one in all regions of China by now. It fell below one within 30 days from the implementations of unprecedent containment measures, which indicates that the strong measures taken by China government was effective to contain the epidemic. Nonetheless, efforts are still needed in order to end the current epidemic as imported cases from overseas pose a high risk of a second outbreak.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Basic Reproduction Number / statistics & numerical data*
  • Betacoronavirus*
  • COVID-19
  • China / epidemiology
  • Coronavirus Infections / epidemiology*
  • Coronavirus Infections / virology
  • Disease Outbreaks / statistics & numerical data*
  • Humans
  • Models, Statistical*
  • Pandemics
  • Pneumonia, Viral / epidemiology*
  • Pneumonia, Viral / virology
  • SARS-CoV-2