Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/31370
Title: Estimating the generation interval for coronavirus disease (COVID-19) based on symptom onset data, March 2020
Authors: GANYANI, Tapiwa 
KREMER, Cécile 
Chen, Dongxuan
TORNERI, Andrea 
FAES, Christel 
Wallinga, Jacco
HENS, Niel 
Issue Date: 2020
Publisher: EUR CENTRE DIS PREVENTION & CONTROL
Source: EUROSURVEILLANCE, 25 (17) , p. 12 -19
Abstract: Background: Estimating key infectious disease parameters from the coronavirus disease (COVID-19) outbreak is essential for modelling studies and guiding intervention strategies. Aim: We estimate the generation interval, serial interval, proportion of presymptomatic transmission and effective reproduction number of COVID-19. We illustrate that reproduction numbers calculated based on serial interval estimates can be biased. 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 serial interval, proportions of pre-symptomatic transmission and reproduction numbers. Results: The mean generation interval was 5.20 days (95% credible interval (Crl): 3.78-6.78) for Singapore and 3.95 days (95% Crl: 3.01-4.91) for Tianjin. The proportion of pre-symptomatic transmission was 48% (95% Crl: 32-67) for Singapore and 62% (95% Crl: 50-76) for Tianjin. Reproduction number estimates based on the generation interval distribution were slightly higher than those based on the serial interval distribution. Sensitivity analyses showed that estimating these quantities from outbreak data requires detailed contact tracing information. Conclusion: High estimates of the proportion of pre-symptomatic transmission imply that case finding and contact tracing need to be supplemented by physical distancing measures in order to control the COVID-19 outbreak. Notably, quarantine and other containment measures were already in place at the time of data collection, which may inflate the proportion of infections from pre-symptomatic individuals.
Notes: Ganyani, T (reprint author), Hasselt Univ, Data Sci Inst, I BioStat, Hasselt, Belgium.
tapiwa.ganyani@uhasselt.be
Other: Ganyani, T (corresponding author), Hasselt Univ, Data Sci Inst, I BioStat, Hasselt, Belgium. tapiwa.ganyani@uhasselt.be
Document URI: http://hdl.handle.net/1942/31370
ISSN: 1025-496X
e-ISSN: 1560-7917
DOI: 10.2807/1560-7917.ES.2020.25.17.2000257
ISI #: WOS:000530880200003
Rights: This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0) Licence. You may share and adapt the material, but must give appropriate credit to the source, provide a link to the licence and indicate if changes were made. Any supplementary material referenced in the article can be found in the online version. This article is copyright of the authors or their affiliated institutions, 2020.
Category: A1
Type: Journal Contribution
Validations: ecoom 2021
Appears in Collections:Research publications

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