TY - JOUR
T1 - Estimating the generation interval and inferring the latent period of COVID-19 from the contact tracing data
AU - Zhao, Shi
AU - Tang, Biao
AU - Musa, Salihu S.
AU - Ma, Shujuan
AU - Zhang, Jiayue
AU - Zeng, Minyan
AU - Yun, Qingping
AU - Guo, Wei
AU - Zheng, Yixiang
AU - Yang, Zuyao
AU - Peng, Zhihang
AU - Chong, Marc KC
AU - Javanbakht, Mohammad
AU - He, Daihai
AU - Wang, Maggie H.
N1 - Funding Information:
DH was supported by General Research Fund (Grant Number 15205119 ) of the Research Grants Council (RGC) of Hong Kong, China , and an Alibaba (China) Co. Ltd. Collaborative Research grant.
Publisher Copyright:
© 2021
PY - 2021/9
Y1 - 2021/9
N2 - 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.
AB - 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.
KW - Contact tracing
KW - COVID-19
KW - Generation interval
KW - Incubation period
KW - Latent period
KW - Serial interval
KW - Statistical inference
UR - http://www.scopus.com/inward/record.url?scp=85108581537&partnerID=8YFLogxK
U2 - 10.1016/j.epidem.2021.100482
DO - 10.1016/j.epidem.2021.100482
M3 - Journal article
AN - SCOPUS:85108581537
SN - 1755-4365
VL - 36
SP - 1
EP - 7
JO - Epidemics
JF - Epidemics
M1 - 100482
ER -