ArticleNature communications2022
Inferring time-varying generation time, serial interval, and incubation period distributions for COVID-19.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 25 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Assessing changes in incubation period, serial interval, and generation time of SARS-CoV-2 variants of concern: a systematic review and meta-analysis.BMC medicine · 2023Pooled it
- Rapid review and meta-analysis of serial intervals for SARS-CoV-2 Delta and Omicron variants.BMC infectious diseases · 2023Pooled it
- Generation time in a discrete epidemic model with asymptomatic carriers: beyond geometric waiting times.Infectious Disease Modelling · 2027Article
- A systematic comparison of methodologies for the estimation of the serial interval.Infectious Disease Modelling · 2026Article
- The impact of shielding on COVID-19 hospitalisations and deaths during 2020 in England: a transmission modelling evaluation using the OpenSAFELY platform.BMC medicine · 2026Article
- Network structure induced bias in estimates of intrinsic generation times.PLoS computational biology · 2026Article
- Chronic infections can generate SARS-CoV-2-like bursts of viral evolution without epistasis.Virus evolution · 2026Article
- Unlocking the power of time-since-infection models: data augmentation for improved instantaneous reproduction number estimation.Biostatistics (Oxford, England) · 2025Article
- Epidemiological indices with multiple circulating pathogen strains.Infectious Disease Modelling · 2025Article
- Optimal algorithms for controlling infectious diseases in real time using noisy infection data.PLoS computational biology · 2025Article
- Nonparametric serial interval estimation with uniform mixtures.PLoS computational biology · 2025Article
- Back-projection improves inference from sparsely sampled genomic surveillance data.bioRxiv : the preprint server for biology · 2025Article
- The time-dependent reproduction number for epidemics in heterogeneous populations.Journal of the Royal Society, Interface · 2025Article
- Transmission dynamics of the 2022 mpox epidemic in New York City.Nature medicine · 2025Article
- The importance of playing the long game when it comes to pandemic surveillance.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- The latency time of SARS-CoV- 2 Delta variant in infection- and vaccine-naive individuals from Vietnam.BMC infectious diseases · 2025Article
- Evaluating a novel reproduction number estimation method: a comparative analysis.Scientific reports · 2025Article
- Flexible Bayesian estimation of incubation times.American journal of epidemiology · 2025Article
- Integrating Contact Tracing Data to Enhance Outbreak Phylodynamic Inference: A Deep Learning Approach.Molecular biology and evolution · 2024Article
- The Effectiveness of Face Mask Mandates on COVID-19 Transmission in Utah.Public health reports (Washington, D.C. : 1974) · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The generation time distribution, reflecting the time between successive infections in transmission chains, is a key epidemiological parameter for describing COVID-19 transmission dynamics. However, because exact infection times are rarely known, it is often approximated by the serial interval distribution. This approximation holds under the assumption that infectors and infectees share the same incubation period distribution, which may not always be true. We estimated incubation period and serial interval distributions using 629 transmission pairs reconstructed by investigating 2989 confirmed cases in China in January-February 2020, and developed an inferential framework to estimate the generation time distribution that accounts for variation over time due to changes in epidemiology, sampling biases and public health and social measures. We identified substantial reductions over time in the serial interval and generation time distributions. Our proposed method provides more reliable estimation of the temporal variation in the generation time distribution, improving assessment of transmission dynamics.
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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.