Your browser doesn't support javascript.
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.
Wang, Kai; Zhao, Shi; Liao, Ying; Zhao, Tiantian; Wang, Xiaoyan; Zhang, Xueliang; Jiao, Haiyan; Li, Huling; Yin, Yi; Wang, Maggie H; Xiao, Li; Wang, Lei; He, Daihai.
  • Wang K; Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, China.
  • Zhao S; JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China.
  • Liao Y; Shenzhen Research Institute of Chinese University of Hong Kong, Shenzhen, China.
  • Zhao T; College of Public Health, Xinjiang Medical University, Urumqi, China.
  • Wang X; College of Public Health, Xinjiang Medical University, Urumqi, China.
  • Zhang X; College of Public Health, Xinjiang Medical University, Urumqi, China.
  • Jiao H; Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, China.
  • Li H; College of Public Health, Xinjiang Medical University, Urumqi, China.
  • Yin Y; College of Public Health, Xinjiang Medical University, Urumqi, China.
  • Wang MH; Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, China.
  • Xiao L; JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China.
  • Wang L; Shenzhen Research Institute of Chinese University of Hong Kong, Shenzhen, China.
  • He D; College of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Transbound Emerg Dis ; 67(6): 2818-2822, 2020 Nov.
Article in English | MEDLINE | ID: covidwho-378330
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.
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Main subject: Population Surveillance / Basic Reproduction Number / Epidemiological Monitoring / SARS-CoV-2 / COVID-19 Type of study: Observational study Limits: Adolescent / Adult / Aged / Child / Child, preschool / Female / Humans / Male / Middle aged / Young adult Country/Region as subject: Asia Language: English Journal: Transbound Emerg Dis Journal subject: Veterinary Medicine Year: 2020 Document Type: Article Affiliation country: Tbed.13647

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Population Surveillance / Basic Reproduction Number / Epidemiological Monitoring / SARS-CoV-2 / COVID-19 Type of study: Observational study Limits: Adolescent / Adult / Aged / Child / Child, preschool / Female / Humans / Male / Middle aged / Young adult Country/Region as subject: Asia Language: English Journal: Transbound Emerg Dis Journal subject: Veterinary Medicine Year: 2020 Document Type: Article Affiliation country: Tbed.13647