Evidence map›Paper›PMID 34390731›Full record

ArticleJournal of theoretical biology2021

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 and 1 more

Abstract read
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Article in Journal of theoretical biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Shi ZhaoJC 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.
Yu ZhaoSchool 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.
Biao TangSchool 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.
Daozhou GaoDepartment of Mathematics, Shanghai Normal University, Shanghai, China. Electronic address: dzgao@shnu.edu.cn.
Zihao GuoJC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China. Electronic address: marc@cuhk.edu.hk.
Marc K C ChongJC 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.
Salihu S MusaDepartment 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.
Yongli CaiSchool of Mathematics and Statistics, Huaiyin Normal University, Huaian, China. Electronic address: yonglicai@hytc.edu.cn.
Weiming WangSchool of Mathematics and Statistics, Huaiyin Normal University, Huaian, China. Electronic address: weimingwang2003@163.com.
Daihai HeDepartment of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China. Electronic address: daihai.he@polyu.edu.hk.
Maggie H WangJC 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.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

COVID-19Contact TracingHong KongHumansLikelihood FunctionsSARS-CoV-2Contact tracingCOVID-19Serial intervalStatistical modellingTransmission generation

Identifiers

PMID34390731
PMCPMC8356772

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.