Estimating the time interval between transmission generations when negative values occur in the serial interval data: using COVID-19 as an example

Shi Zhao; Shi Zhao

doi:10.3934/mbe.2020198

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 4: 3512-3519. doi: 10.3934/mbe.2020198

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Estimating the time interval between transmission generations when negative values occur in the serial interval data: using COVID-19 as an example

Shi Zhao ^{1,2,3,4
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1.
Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
2.
School of Nursing, Hong Kong Polytechnic University, Hong Kong, China
3.
JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China
4.
CUHK Shenzhen Research Institute, Shenzhen, China

Received: 29 March 2020 Accepted: 06 May 2020 Published: 11 May 2020

The coronavirus disease 2019 (COVID-19) emerged in Wuhan, China in the end of 2019, and soon became a serious public health threat globally. Due to the unobservability, the time interval between transmission generations (TG), though important for understanding the disease transmission patterns, of COVID-19 cannot be directly summarized from surveillance data. In this study, we develop a likelihood framework to estimate the TG and the pre-symptomatic transmission period from the serial interval observations from the individual transmission events. As the results, we estimate the mean of TG at 4.0 days (95%CI: 3.3-4.6), and the mean of pre-symptomatic transmission period at 2.2 days (95%CI: 1.3-4.7). We approximate the mean latent period of 3.3 days, and 32.2% (95%CI: 10.3-73.7) of the secondary infections may be due to pre-symptomatic transmission. The timely and effectively isolation of symptomatic COVID-19 cases is crucial for mitigating the epidemics.
- coronavirus disease 2019,
- COVID-19,
- time of generation,
- serial interval,
- epidemic,
- modelling
Citation: Shi Zhao. Estimating the time interval between transmission generations when negative values occur in the serial interval data: using COVID-19 as an example[J]. Mathematical Biosciences and Engineering, 2020, 17(4): 3512-3519. doi: 10.3934/mbe.2020198

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Abstract

The coronavirus disease 2019 (COVID-19) emerged in Wuhan, China in the end of 2019, and soon became a serious public health threat globally. Due to the unobservability, the time interval between transmission generations (TG), though important for understanding the disease transmission patterns, of COVID-19 cannot be directly summarized from surveillance data. In this study, we develop a likelihood framework to estimate the TG and the pre-symptomatic transmission period from the serial interval observations from the individual transmission events. As the results, we estimate the mean of TG at 4.0 days (95%CI: 3.3-4.6), and the mean of pre-symptomatic transmission period at 2.2 days (95%CI: 1.3-4.7). We approximate the mean latent period of 3.3 days, and 32.2% (95%CI: 10.3-73.7) of the secondary infections may be due to pre-symptomatic transmission. The timely and effectively isolation of symptomatic COVID-19 cases is crucial for mitigating the epidemics.

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