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
Affiliations
Shi Zhao
JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China; CUHK Shenzhen Research Institute, Shenzhen, China; Corresponding author at: JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China.
Biao Tang
School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China; Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, ON, M3J 1P3, Canada
Salihu S Musa
Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China; Department of Mathematics, Kano University of Science and Technology, Wudil, Nigeria
Shujuan Ma
Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
Jiayue Zhang
Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
Minyan Zeng
Department of Neurology, Peking University Shenzhen Hospital, Shenzhen, China
Qingping Yun
Department of Social Medicine and Health Education, School of Public Health, Peking University, Beijing, China
Wei Guo
Department of Neurology, Peking University Shenzhen Hospital, Shenzhen, China
Yixiang Zheng
Department of Infectious Diseases, Key Laboratory of Viral Hepatitis of Hunan, Xiangya Hospital, Central South University, Changsha, China
Zuyao Yang
JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China
Zhihang Peng
Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
Marc KC Chong
JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China; CUHK Shenzhen Research Institute, Shenzhen, China
Mohammad Javanbakht
Nephrology and Urology Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
Daihai He
Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China; Corresponding author.
Maggie H. Wang
JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China; CUHK Shenzhen Research Institute, Shenzhen, China
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.
