Inferring diagnostic serial intervals to understand COVID-19 transmission dynamics in Hong Kong and mainland China
File(s)
Published online version
Author(s)
Type
Journal Article
Abstract
Background. Estimating the instantaneous reproduction numbers (Rt) requires inferring generation time (GT, the interval between successive infections in the transmission chain), often approximated by the serial interval (SI, the interval between successive onsets in the transmission chain). The serial interval based on clinical outcomes is subject to recall biases, and such
clinical information is not always available. As a comparable metric, we defined the diagnostic serial interval (SId, the time between diagnostic reporting of case pairs in a transmission chain) and compared it with the traditional SI.
Methods. We analyzed confirmed coronavirus disease 2019 (COVID-19) cases from 3 ancestral waves in Hong Kong and the first wave in mainland China. Using Bayesian methods, we inferred the distributions of effective SI and SId, along with onset-to-reporting delays, and compared the resulting Rt estimates.
Results. The distributions of SI and SId were comparable across waves, with shorter means observed in SId. Reporting delays for infectors were longer than those for infectees, which was identified as a key factor influencing the temporal variation in SI and SId. Additionally, factors such as public health and social measures, case profile, and demographics were found to significantly impact the estimates. Time-varying estimates of the reproduction number derived from both SI and SId were highly consistent, with median absolute differences ranging from 0.12 to 0.19.
Conclusions. The diagnostic serial interval shows potentials as a comparable metric to the traditional serial interval for estimating transmissibility in assessing COVID-19 transmission dynamics, and this approach could be extended for other respiratory viruses.
clinical information is not always available. As a comparable metric, we defined the diagnostic serial interval (SId, the time between diagnostic reporting of case pairs in a transmission chain) and compared it with the traditional SI.
Methods. We analyzed confirmed coronavirus disease 2019 (COVID-19) cases from 3 ancestral waves in Hong Kong and the first wave in mainland China. Using Bayesian methods, we inferred the distributions of effective SI and SId, along with onset-to-reporting delays, and compared the resulting Rt estimates.
Results. The distributions of SI and SId were comparable across waves, with shorter means observed in SId. Reporting delays for infectors were longer than those for infectees, which was identified as a key factor influencing the temporal variation in SI and SId. Additionally, factors such as public health and social measures, case profile, and demographics were found to significantly impact the estimates. Time-varying estimates of the reproduction number derived from both SI and SId were highly consistent, with median absolute differences ranging from 0.12 to 0.19.
Conclusions. The diagnostic serial interval shows potentials as a comparable metric to the traditional serial interval for estimating transmissibility in assessing COVID-19 transmission dynamics, and this approach could be extended for other respiratory viruses.
Date Issued
2026-08-26
Date Acceptance
2026-08-21
Citation
The Journal of Infectious Diseases, 2026
ISSN
0022-1899
Publisher
Oxford University Press (OUP)
Start Page
jiag443
Journal / Book Title
The Journal of Infectious Diseases
Copyright Statement
© The Author(s) 2026. Published by Oxford University Press on behalf of Infectious Diseases Society of America. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/42644512
PII: 8770953
Publication Status
Published online
Coverage Spatial
United States
Article Number
jiag443
Date Publish Online
2026-08-26