ArticleTransboundary and emerging diseases2020
Estimating the serial interval of the novel coronavirus disease (COVID-19) based on the public surveillance data in Shenzhen, China, from 19 January to 22 February 2020.
Article in Transboundary and emerging diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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Who cites it
23 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
- Presymptomatic transmission of SARS-CoV-2 infection: a secondary analysis using published data.BMJ open · 2021Pooled it
- VIBES: A multiscale modeling approach integrating within-host and between-hosts dynamics in epidemics.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Variability in the serial interval of COVID-19 in South Korea: a comprehensive analysis of age and regional influences.Frontiers in public health · 2024Article
- Evaluation of the EsteR Toolkit for COVID-19 Decision Support: Sensitivity Analysis and Usability Study.JMIR formative research · 2023Article
- The non-pharmaceutical interventions may affect the advantage in transmission of mutated variants during epidemics: A conceptual model for COVID-19.Journal of theoretical biology · 2022Article
- Estimation of the Serial Interval and the Effective Reproductive Number of COVID-19 Outbreak Using Contact Data in Burkina Faso, a Sub-Saharan African Country.Computational and mathematical methods in medicine · 2022Article
- Shrinkage in serial intervals across transmission generations of COVID-19.Journal of theoretical biology · 2021Article
- Estimation of novel coronavirus (COVID-19) reproduction number and case fatality rate: A systematic review and meta-analysis.Health science reports · 2021Article
- Attach importance of the bootstrapEpidemiology and infection · 2021Article
- Inferring the Association between the Risk of COVID-19 Case Fatality and N501Y Substitution in SARS-CoV-2.Viruses · 2021Article
- Modelling the association between COVID-19 transmissibility and D614G substitution in SARS-CoV-2 spike protein: using the surveillance data in California as an example.Theoretical biology & medical modelling · 2021Article
- Inferencing superspreading potential using zero-truncated negative binomial model: exemplification with COVID-19.BMC medical research methodology · 2021Article
- Subcritical Transmission in the Early Stage of COVID-19 in Korea.International journal of environmental research and public health · 2021Article
- Grappling with COVID-19 by imposing and lifting non-pharmaceutical interventions in Sri Lanka: A modeling perspective.Infectious Disease Modelling · 2021Article
- Estimating the Instantaneous Asymptomatic Proportion With a Simple Approach: Exemplified With the Publicly Available COVID-19 Surveillance Data in Hong Kong.Frontiers in public health · 2021Article
- Sensitivity analysis of the infection transmissibility in the UK during the COVID-19 pandemic.PeerJ · 2021Article
- Mathematical modeling of COVID-19 epidemic with effect of awareness programs.Infectious Disease Modelling · 2021Article
- To avoid the noncausal association between environmental factor and COVID-19 when using aggregated data: Simulation-based counterexamples for demonstration.The Science of the total environment · 2020Article
- Rapid review of available evidence on the serial interval and generation time of COVID-19.BMJ open · 2020Review
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13 authors.
Funding
Abstract
The novel coronavirus disease (COVID-19) poses a serious threat to global public health and economics. Serial interval (SI), time between the onset of symptoms of a primary case and a secondary case, is a key epidemiological parameter. We estimated SI of COVID-19 in Shenzhen, China based on 27 records of transmission chains. We adopted three parametric models: Weibull, lognormal and gamma distributions, and an interval-censored likelihood framework. The three models were compared using the corrected Akaike information criterion (AICc). We also fitted the epidemic curve of COVID-19 to the logistic growth model to estimate the reproduction number. Using a Weibull distribution, we estimated the mean SI to be 5.9 days (95% CI: 3.9-9.6) with a standard deviation (SD) of 4.8 days (95% CI: 3.1-10.1). Using a logistic growth model, we estimated the basic reproduction number in Shenzhen to be 2.6 (95% CI: 2.4-2.8). The SI of COVID-19 is relatively shorter than that of SARS and MERS, the other two betacoronavirus diseases, which suggests the iteration of the transmission may be rapid. Thus, it is crucial to isolate close contacts promptly to effectively control the spread of COVID-19.
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