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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

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.

Original languageEnglish
Article number110861
Pages (from-to)1-11
Number of pages11
JournalJournal of Theoretical Biology
Volume529
DOIs
Publication statusPublished - 21 Nov 2021

Keywords

  • Contact tracing
  • COVID-19
  • Serial interval
  • Statistical modelling
  • Transmission generation

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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