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Estimating the generation interval for coronavirus disease (COVID-19) based on symptom onset data, March 2020.
Euro Surveill. 2020 04; 25(17)ES

Abstract

BackgroundEstimating key infectious disease parameters from the coronavirus disease (COVID-19) outbreak is essential for modelling studies and guiding intervention strategies.AimWe estimate the generation interval, serial interval, proportion of pre-symptomatic transmission and effective reproduction number of COVID-19. We illustrate that reproduction numbers calculated based on serial interval estimates can be biased.MethodsWe 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.ResultsThe mean generation interval was 5.20 days (95% credible interval (CrI): 3.78-6.78) for Singapore and 3.95 days (95% CrI: 3.01-4.91) for Tianjin. The proportion of pre-symptomatic transmission was 48% (95% CrI: 32-67) for Singapore and 62% (95% CrI: 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.ConclusionHigh 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.

Authors+Show Affiliations

I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium.I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium.Leiden University Medical Center, Leiden, the Netherlands. Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands.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.I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium.Leiden University Medical Center, Leiden, the Netherlands. Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands.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.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32372755

Citation

Ganyani, Tapiwa, et al. "Estimating the Generation Interval for Coronavirus Disease (COVID-19) Based On Symptom Onset Data, March 2020." Euro Surveillance : Bulletin Europeen Sur Les Maladies Transmissibles = European Communicable Disease Bulletin, vol. 25, no. 17, 2020.
Ganyani T, Kremer C, Chen D, et al. Estimating the generation interval for coronavirus disease (COVID-19) based on symptom onset data, March 2020. Euro Surveill. 2020;25(17).
Ganyani, T., Kremer, C., Chen, D., Torneri, A., Faes, C., Wallinga, J., & Hens, N. (2020). Estimating the generation interval for coronavirus disease (COVID-19) based on symptom onset data, March 2020. Euro Surveillance : Bulletin Europeen Sur Les Maladies Transmissibles = European Communicable Disease Bulletin, 25(17). https://doi.org/10.2807/1560-7917.ES.2020.25.17.2000257
Ganyani T, et al. Estimating the Generation Interval for Coronavirus Disease (COVID-19) Based On Symptom Onset Data, March 2020. Euro Surveill. 2020;25(17) PubMed PMID: 32372755.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Estimating the generation interval for coronavirus disease (COVID-19) based on symptom onset data, March 2020. AU - Ganyani,Tapiwa, AU - Kremer,Cécile, AU - Chen,Dongxuan, AU - Torneri,Andrea, AU - Faes,Christel, AU - Wallinga,Jacco, AU - Hens,Niel, PY - 2020/5/7/entrez PY - 2020/5/7/pubmed PY - 2020/5/12/medline KW - COVID-19 KW - generation interval KW - incubation period KW - reproduction number KW - serial interval JF - Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin JO - Euro Surveill. VL - 25 IS - 17 N2 - BackgroundEstimating key infectious disease parameters from the coronavirus disease (COVID-19) outbreak is essential for modelling studies and guiding intervention strategies.AimWe estimate the generation interval, serial interval, proportion of pre-symptomatic transmission and effective reproduction number of COVID-19. We illustrate that reproduction numbers calculated based on serial interval estimates can be biased.MethodsWe 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.ResultsThe mean generation interval was 5.20 days (95% credible interval (CrI): 3.78-6.78) for Singapore and 3.95 days (95% CrI: 3.01-4.91) for Tianjin. The proportion of pre-symptomatic transmission was 48% (95% CrI: 32-67) for Singapore and 62% (95% CrI: 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.ConclusionHigh 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. SN - 1560-7917 UR - https://www.unboundmedicine.com/medline/citation/32372755/full_citation L2 - http://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2020.25.17.2000257 DB - PRIME DP - Unbound Medicine ER -