Estimating the generation interval for COVID-19 based on symptom onset data

medRxiv

Abstract

Abstract Background Estimating key infectious disease parameters from the COVID-19 outbreak is quintessential for modelling studies and guiding intervention strategies. Whereas different estimates for the incubation period distribution and the serial interval distribution have been reported, estimates of the generation interval for COVID-19 have not been provided. Methods We used outbreak data from clusters in Singapore and Tianjin, China to estimate the generation interval from symptom onset data while acknowledging uncertainty about the incubation period distribution and the underlying transmission network. From those estimates we obtained the proportions pre-symptomatic transmission and reproduction numbers. Results The mean generation interval was 5.20 (95%CI 3.78-6.78) days for Singapore and 3.95 (95%CI 3.01-4.91) days for Tianjin, China when relying on a previously reported incubation period with mean 5.2 and SD 2.8 days. The proportion of pre-symptomatic transmission was 48% (95%CI 32-67%) for Singapore and 62% (95%CI 50-76%) for Tianjin, China. Estimates of the reproduction number based on the generation interval distribution were slightly higher than those based on the serial interval distribution. Conclusions Estimating generation and serial interval distributions from outbreak data requires careful investigation of the underlying transmission network. Detailed contact tracing information is essential for correctly estimating these quantities.

Authors 7

1. Tapiwa Ganyani corresponding

   Hasselt University

   Affiliation as printed

   I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium

2. Kremer Cécile

   Hasselt University

   Affiliation as printed

   I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium

3. Dongxuan Chen Aachen

   Leiden University Medical Center · National Institute for Public Health and the Environment

   Affiliation as printed

   Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands

   Leiden University Medical Center, The Netherlands

4. Torneri Andrea

   University of Antwerp · Hasselt University

   Affiliation as printed

   Centre for Health Economics Research and Modelling Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium

   I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium

5. Christel Faes

   Hasselt University

   Affiliation as printed

   I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium

6. Wallinga Jacco Aachen

   Leiden University Medical Center · National Institute for Public Health and the Environment

   Affiliation as printed

   Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands

   Leiden University Medical Center, The Netherlands

7. Hens Niel

   University of Antwerp · Hasselt University

   Affiliation as printed

   Centre for Health Economics Research and Modelling Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium

   I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium