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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.

© Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
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/content/10.2807/1560-7917.ES.2020.25.17.2000257
2020-04-30
2024-12-22
/content/10.2807/1560-7917.ES.2020.25.17.2000257
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Estimating the generation interval for coronavirus disease (COVID-19) based on symptom onset data, March 2020
<p class="citation"> <span>Citation style for this article:</span> <span class="meta-value authors"> <a href="/search?value1=Tapiwa+Ganyani&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Ganyani Tapiwa</a>, <a href="/search?value1=C%C3%A9cile+Kremer&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Kremer Cécile</a>, <a href="/search?value1=Dongxuan+Chen&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Chen Dongxuan</a>, <a href="/search?value1=Andrea+Torneri&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Torneri Andrea</a>, <a href="/search?value1=Christel+Faes&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Faes Christel</a>, <a href="/search?value1=Jacco+Wallinga&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Wallinga Jacco</a>, <a href="/search?value1=Niel+Hens&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Hens Niel</a></span>. Estimating the generation interval for coronavirus disease (COVID-19) based on symptom onset data, March 2020. <a href="/content/ecdc">Euro Surveill.</a> 2020;25(17):pii=2000257. <a href="https://doi.org/10.2807/1560-7917.ES.2020.25.17.2000257" title="doi link" target="_blank">https://doi.org/10.2807/1560-7917.ES.2020.25.17.2000257</a> <span class="ES_Article_citation"> <span class="generated">Received</span>: 06 Mar 2020;&nbsp;&nbsp; <span class="generated">Accepted</span>: 07 Apr 2020 </span> </p>
/content/10.2807/1560-7917.ES.2020.25.17.2000257
/content/10.2807/1560-7917.ES.2020.25.17.2000257
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