Shrinkage in serial intervals across transmission generations of COVID-19

Shi Zhao; Yu Zhao; Biao Tang; Daozhou Gao; Zihao Guo; Marc K C Chong; Salihu S Musa; Yongli Cai; Weiming Wang; Daihai He; Maggie H Wang

doi:10.1016/j.jtbi.2021.110861

Shrinkage in serial intervals across transmission generations of COVID-19

J Theor Biol. 2021 Nov 21:529:110861. doi: 10.1016/j.jtbi.2021.110861. Epub 2021 Aug 11.

Authors

Shi Zhao¹, Yu Zhao², Biao Tang³, Daozhou Gao⁴, Zihao Guo⁵, Marc K C Chong⁶, Salihu S Musa⁷, Yongli Cai⁸, Weiming Wang⁹, Daihai He¹⁰, Maggie H Wang¹¹

Affiliations

¹ JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China; CUHK Shenzhen Research Institute, Shenzhen, China. Electronic address: zhaoshi.cmsa@gmail.com.
² School of Public Health and Management, Ningxia Medical University, Yinchuan, Ningxia, China; Key Laboratory of Environmental Factors and Chronic Disease Control, Xingqing District, Yinchuan, Ningxia, China. Electronic address: zhaoyu@nxmu.edu.cn.
³ 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. Electronic address: btang66@yorku.ca.
⁴ Department of Mathematics, Shanghai Normal University, Shanghai, China. Electronic address: dzgao@shnu.edu.cn.
⁵ JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China. Electronic address: marc@cuhk.edu.hk.
⁶ JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China; CUHK Shenzhen Research Institute, Shenzhen, China. Electronic address: marc@cuhk.edu.hk.
⁷ Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China; Department of Mathematics, Kano University of Science and Technology, Wudil, Nigeria. Electronic address: salihu-sabiu.musa@connect.polyu.hk.
⁸ School of Mathematics and Statistics, Huaiyin Normal University, Huaian, China. Electronic address: yonglicai@hytc.edu.cn.
⁹ School of Mathematics and Statistics, Huaiyin Normal University, Huaian, China. Electronic address: weimingwang2003@163.com.
¹⁰ Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China. Electronic address: daihai.he@polyu.edu.hk.
¹¹ JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China; CUHK Shenzhen Research Institute, Shenzhen, China. Electronic address: maggiew@cuhk.edu.hk.

Abstract

One of the key epidemiological characteristics that shape the transmission of coronavirus disease 2019 (COVID-19) is the serial interval (SI). Although SI is commonly considered following a probability distribution at a population scale, recent studies reported a slight shrinkage (or contraction) of the mean of effective SI across transmission generations or over time. Here, we develop a likelihood-based statistical inference framework with truncation to explore the change in SI across transmission generations after adjusting the impacts of case isolation. The COVID-19 contact tracing surveillance data in Hong Kong are used for exemplification. We find that for COVID-19, the mean of individual SI is likely to shrink with a factor at 0.72 per generation (95%CI: 0.54, 0.96) as the transmission generation increases, where a threshold may exist as the lower boundary of this shrinking process. We speculate that one of the probable explanations for the shrinkage in SI might be an outcome due to the competition among multiple candidate infectors within the same case cluster. Thus, the nonpharmaceutical interventive strategies are crucially important to block the transmission chains, and mitigate the COVID-19 epidemic.

Keywords: COVID-19; Contact tracing; Serial interval; Statistical modelling; Transmission generation.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

COVID-19*
Contact Tracing
Hong Kong
Humans
Likelihood Functions
SARS-CoV-2