Estimating the serial interval of the novel coronavirus disease (COVID-19) based on the public surveillance data in Shenzhen, China, from 19 January to 22 February 2020

Kai Wang, Shi Zhao, Ying Liao, Tiantian Zhao, Xiaoyan Wang, Xueliang Zhang, Haiyan Jiao, Huling Li, Yi Yin, Maggie H. Wang, Li Xiao, Lei Wang, Daihai He

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)

Abstract

The novel coronavirus disease (COVID-19) poses a serious threat to global public health and economics. Serial interval (SI), time between the onset of symptoms of a primary case and a secondary case, is a key epidemiological parameter. We estimated SI of COVID-19 in Shenzhen, China based on 27 records of transmission chains. We adopted three parametric models: Weibull, lognormal and gamma distributions, and an interval-censored likelihood framework. The three models were compared using the corrected Akaike information criterion (AICc). We also fitted the epidemic curve of COVID-19 to the logistic growth model to estimate the reproduction number. Using a Weibull distribution, we estimated the mean SI to be 5.9 days (95% CI: 3.9–9.6) with a standard deviation (SD) of 4.8 days (95% CI: 3.1–10.1). Using a logistic growth model, we estimated the basic reproduction number in Shenzhen to be 2.6 (95% CI: 2.4–2.8). The SI of COVID-19 is relatively shorter than that of SARS and MERS, the other two betacoronavirus diseases, which suggests the iteration of the transmission may be rapid. Thus, it is crucial to isolate close contacts promptly to effectively control the spread of COVID-19.

Original languageEnglish
Pages (from-to)1-5
Number of pages5
JournalTransboundary and Emerging Diseases
DOIs
Publication statusPublished - 26 May 2020

Keywords

  • basic reproduction number
  • coronavirus disease
  • COVID-19
  • outbreak
  • serial interval
  • Shenzhen

ASJC Scopus subject areas

  • Immunology and Microbiology(all)
  • veterinary(all)

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