SynthesisStatistical methods in medical research2022
Meta-analysis of the severe acute respiratory syndrome coronavirus 2 serial intervals and the impact of parameter uncertainty on the coronavirus disease 2019 reproduction number.
Synthesis in Statistical methods in medical research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Large-scale genomic surveillance reveals immunosuppression drives mutation dynamics in persistent SARS-CoV-2 infections.Nature communications · 2026Article
- Estimating the effectiveness of non-pharmaceutical interventions against COVID-19 transmission in the Netherlands.PLoS computational biology · 2025Article
- Simulation-based inference of the time-dependent reproduction number from temporally aggregated and under-reported disease incidence time series data.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Predicting subnational incidence of COVID-19 cases and deaths in EU countries.BMC infectious diseases · 2024Article
- ern: AnPloS one · 2024Article
- Impact of vaccinations, boosters and lockdowns on COVID-19 waves in French Polynesia.Nature communications · 2023Article
- Uncertainty and error in SARS-CoV-2 epidemiological parameters inferred from population-level epidemic models.Journal of theoretical biology · 2023Article
- Assessing the heterogeneity in the transmission of infectious diseases from time series of epidemiological data.PloS one · 2023Article
- Instantaneous reproduction number and epidemic growth rate for predicting COVID-19 waves: the first 2 years of the pandemic in Spain.Frontiers in public health · 2023Article
- Keeping Politics out of Doctors' Offices: A Reply to Fogelson.Psychotherapy and psychosomatics · 2023Article
- Estimation of the incubation period and generation time of SARS-CoV-2 Alpha and Delta variants from contact tracing data.Epidemiology and infection · 2022Article
- Estimation of reproduction numbers in real time: Conceptual and statistical challenges.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2022Article
- Empirical evidence on the efficiency of backward contact tracing in COVID-19.Nature communications · 2022Article
- Effectiveness of controlling COVID-19 epidemic by implementing soft lockdown policy and extensive community screening in Taiwan.Scientific reports · 2022Article
- Inferring the reproduction number using the renewal equation in heterogeneous epidemics.Journal of the Royal Society, Interface · 2022Article
- Article
- Rapid incidence estimation from SARS-CoV-2 genomes reveals decreased case detection in Europe during summer 2020.Nature communications · 2021Article
- Predicting increases in COVID-19 incidence to identify locations for targeted testing in West Virginia: A machine learning enhanced approach.medRxiv : the preprint server for health sciences · 2021Article
- Understanding the effectiveness of government interventions against the resurgence of COVID-19 in Europe.Nature communications · 2021Article
- Assessing the extent of community spread caused by mink-derived SARS-CoV-2 variants.Innovation (Cambridge (Mass.)) · 2021Article
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4 authors.
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Abstract
The serial interval of an infectious disease, commonly interpreted as the time between the onset of symptoms in sequentially infected individuals within a chain of transmission, is a key epidemiological quantity involved in estimating the reproduction number. The serial interval is closely related to other key quantities, including the incubation period, the generation interval (the time between sequential infections), and time delays between infection and the observations associated with monitoring an outbreak such as confirmed cases, hospital admissions, and deaths. Estimates of these quantities are often based on small data sets from early contact tracing and are subject to considerable uncertainty, which is especially true for early coronavirus disease 2019 data. In this paper, we estimate these key quantities in the context of coronavirus disease 2019 for the UK, including a meta-analysis of early estimates of the serial interval. We estimate distributions for the serial interval with a mean of 5.9 (95% CI 5.2; 6.7) and SD 4.1 (95% CI 3.8; 4.7) days (empirical distribution), the generation interval with a mean of 4.9 (95% CI 4.2; 5.5) and SD 2.0 (95% CI 0.5; 3.2) days (fitted gamma distribution), and the incubation period with a mean 5.2 (95% CI 4.9; 5.5) and SD 5.5 (95% CI 5.1; 5.9) days (fitted log-normal distribution). We quantify the impact of the uncertainty surrounding the serial interval, generation interval, incubation period, and time delays, on the subsequent estimation of the reproduction number, when pragmatic and more formal approaches are taken. These estimates place empirical bounds on the estimates of most relevant model parameters and are expected to contribute to modeling coronavirus disease 2019 transmission.
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