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

Tapiwa Ganyani, Cécile Kremer, Dongxuan Chen, Andrea Torneri, Christel Faes, Jacco Wallinga, Niel Hens, Tapiwa Ganyani, Cécile Kremer, Dongxuan Chen, Andrea Torneri, Christel Faes, Jacco Wallinga, Niel Hens

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.

Keywords: COVID-19; generation interval; incubation period; reproduction number; serial interval.

Conflict of interest statement

Conflict of interest: None declared.

Figures

Figure
Figure
Three possible coronavirus disease (COVID-19) transmission scenarios: (A) one symptomatic transmission scenario and (B) two pre-symptomatic transmission scenarios

References

    1. Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis. 2020. Feb 19.
    1. Held L, Hens N, O’Neill PD, Wallinga J. Handbook of infectious disease data analysis. 1st ed. New York: Chapman and Hall/CRC;2019.
    1. Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, et al. Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. N Engl J Med. 2020;382(13):1199-207. 10.1056/NEJMoa2001316
    1. Backer JA, Klinkenberg D, Wallinga J. Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20-28 January 2020. Euro Surveill. 2020;25(5). 10.2807/1560-7917.ES.2020.25.5.2000062
    1. Linton NM, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov AR, Jung SM, et al. Incubation period and other epidemiological characteristics of 2019 novel coronavirus infections with right truncation: A statistical analysis of publicly available case data. J Clin Med. 2020;9(2):538. 10.3390/jcm9020538
    1. Zhang J, Litvinova M, Wang W, Wang Y, Deng X, Chen X, et al. Evolving epidemiology of novel coronavirus diseases 2019 and possible interruption of local transmission outside Hubei Province in China: a descriptive and modeling study. medRxiv. 2020;20026328: (Preprint). Available from: 10.1101/2020.02.21.20026328
    1. Liu T, Hu J, Kang M, Lin L, Zhong H, Xiao J, et al. Transmission dynamics of 2019 novel coronavirus (2019-nCoV). SSRN. 2020;3526307: (Preprint). Available from: 10.2139/ssrn.3526307
    1. Britton T, Scalia Tomba G. Estimation in emerging epidemics: biases and remedies. J R Soc Interface. 2019;16(150):20180670. 10.1098/rsif.2018.0670
    1. Wallinga J, Teunis P. Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. Am J Epidemiol. 2004;160(6):509-16. 10.1093/aje/kwh255
    1. Hens N, Calatayud L, Kurkela S, Tamme T, Wallinga J. Robust reconstruction and analysis of outbreak data: influenza A(H1N1)v transmission in a school-based population. Am J Epidemiol. 2012;176(3):196-203. 10.1093/aje/kws006
    1. te Beest DE, Wallinga J, Donker T, van Boven M. Estimating the generation interval of influenza A (H1N1) in a range of social settings. Epidemiology. 2013;24(2):244-50. 10.1097/EDE.0b013e31827f50e8
    1. Mood AM, Graybill FA, Boes DC. Introduction to the Theory of Statistics. McGraw-Hill; 1950.
    1. Robert CP, Casella G. Monte Carlo Statistical Methods. New York: Springer; 2004.
    1. Wallinga J, Lipsitch M. How generation intervals shape the relationship between growth rates and reproductive numbers. Proc Biol Sci. 2007;274(1609):599-604. 10.1098/rspb.2006.3754
    1. Tindale LC, Coombe M, Stockdale JE, Garlock E, Lau WYV, Saraswat M, et al. Transmission interval estimates suggest pre-symptomatic spread of COVID-19. medRxiv. 2020;20029983: (Preprint). Available from: 10.1101/2020.03.03.20029983
    1. Du Z, Xu X, Wu Y, Wang L, Cowling BJ, Meyers LA. Serial interval of COVID-19 among publicly reported confirmed cases. Emerg Infect Dis. 2020;26(6). 10.3201/eid2606.200357
    1. Bell DM, World Health Organization Working Group on International and Community Transmission of SARS Public health interventions and SARS spread, 2003. Emerg Infect Dis. 2004;10(11):1900-6. 10.3201/eid1011.040729
    1. Hellwell J, Abbott S, Gimma A, Bosse NI, Jarvis CI, Russell TW, et al. Feasibility of controlling 2019-nCoV outbreaks by isolation of cases and contacts. medRxiv. 2020;20021162: (Preprint). Available from: 10.1101/2020.02.08.20021162

Source: PubMed

Sponsorer och medarbetare

Medicinska tillstånd

Läkemedelsinterventioner

3
Prenumerera