About: Estimating the generation interval for coronavirus disease (COVID-19) based on symptom onset data, March 2020

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Estimating the generation interval for coronavirus disease (COVID-19) based on symptom onset data, March 2020
Creator	Hens, Niel Wallinga, Jacco Faes, Christel Torneri, Andrea Chen, Dongxuan Kremer¹, Cécile Ganyani, Tapiwa
source	Medline; PMC
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 pre-symptomatic 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 (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. 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.
has issue date	2020-04-30(xsd:dateTime)
bibo:doi	10.2807/1560-7917.es.2020.25.17.2000257
bibo:pmid	32372755
has license	cc-by
sha1sum (hex)	33a4240a248a314d4b6153c42d81e412fdf994e2
schema:url	https://doi.org/10.2807/1560-7917.es.2020.25.17.2000257
resource representing a document's title	Estimating the generation interval for coronavirus disease (COVID-19) based on symptom onset data, March 2020
has PubMed Central identifier	PMC7201952
has PubMed identifier	32372755
schema:publication	Euro Surveill
resource representing a document's body	covid:33a4240a248a314d4b6153c42d81e412fdf994e2#body_text
is schema:about of	named entity 'control' named entity 'analyses' named entity 'interval' named entity 'distribution' named entity 'onset' named entity 'estimating' named entity 'Results' named entity 'Tianjin' named entity 'disease' named entity 'China' named entity 'COVID-19' named entity 'days' named entity 'guiding' named entity 'estimates' named entity 'individuals' named entity 'time' named entity 'transmission' named entity 'contact tracing' named entity 'proportion' named entity 'Reproduction' named entity 'Singapore' named entity 'symptom' named entity 'infectious disease' named entity 'modelling' named entity 'quarantine' named entity '95%' named entity 'COVID-19 outbreak' named entity 'Tianjin' named entity 'pre-symptomatic' named entity 'China' named entity 'credible interval' named entity 'serial interval' named entity 'Singapore' named entity 'contact tracing' named entity 'physical distancing' named entity 'COVID' named entity 'COVID-19' named entity 'essential' named entity 'disease' named entity 'days' named entity 'Markov chain Monte Carlo' named entity 'serial interval' named entity 'credible intervals' named entity 'serial interval' named entity 'renewal equation' named entity 'point estimates' named entity 'serial interval' named entity 'credible interval' named entity 'percentiles' named entity 'EM algorithm' named entity 'severe acute respiratory syndrome coronavirus' named entity '95%' named entity '26 February' named entity 'serial interval' named entity 'symptom' named entity 'parameter vector' named entity 'variance' named entity 'statistical modelling' named entity 'serial interval' named entity '95% CI' named entity '95%' named entity 'incubation period' named entity 'serial interval' named entity 'serial interval' named entity 'generation time' named entity 'sensitivity analyses' named entity 'percentiles' named entity 'serial interval' named entity 'Vienna' named entity 'posterior distribution' named entity 'prior probabilities' named entity '5.2' named entity 'MCMC' named entity 'Tianjin' named entity 'incubation period'

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