Estimating the generation interval and inferring the latent period of COVID-19 from the contact tracing data

Shi Zhao, Biao Tang, Salihu S. Musa, Shujuan Ma, Jiayue Zhang, Minyan Zeng, Qingping Yun, Wei Guo, Yixiang Zheng, Zuyao Yang, Zhihang Peng, Marc KC Chong, Mohammad Javanbakht, Daihai He, Maggie H. Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

Abstract

The coronavirus disease 2019 (COVID-19) emerged by end of 2019, and became a serious public health threat globally in less than half a year. The generation interval and latent period, though both are of importance in understanding the features of COVID-19 transmission, are difficult to observe, and thus they can rarely be learnt from surveillance data empirically. In this study, we develop a likelihood framework to estimate the generation interval and incubation period simultaneously by using the contact tracing data of COVID-19 cases, and infer the pre-symptomatic transmission proportion and latent period thereafter. We estimate the mean of incubation period at 6.8 days (95 %CI: 6.2, 7.5) and SD at 4.1 days (95 %CI: 3.7, 4.8), and the mean of generation interval at 6.7 days (95 %CI: 5.4, 7.6) and SD at 1.8 days (95 %CI: 0.3, 3.8). The basic reproduction number is estimated ranging from 1.9 to 3.6, and there are 49.8 % (95 %CI: 33.3, 71.5) of the secondary COVID-19 infections likely due to pre-symptomatic transmission. Using the best estimates of model parameters, we further infer the mean latent period at 3.3 days (95 %CI: 0.2, 7.9). Our findings highlight the importance of both isolation for symptomatic cases, and for the pre-symptomatic and asymptomatic cases.

Original languageEnglish
Article number100482
Pages (from-to)1-7
Number of pages7
JournalEpidemics
Volume36
DOIs
Publication statusPublished - Sep 2021

Keywords

  • Contact tracing
  • COVID-19
  • Generation interval
  • Incubation period
  • Latent period
  • Serial interval
  • Statistical inference

ASJC Scopus subject areas

  • Epidemiology
  • Parasitology
  • Microbiology
  • Public Health, Environmental and Occupational Health
  • Infectious Diseases
  • Virology

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