ArticleProceedings of the National Academy of Sciences of the United States of America2021
Forward-looking serial intervals correctly link epidemic growth to reproduction numbers.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 2 of them syntheses that pooled it.
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Who cites it
44 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Decoding mpox: a systematic review and meta-analysis of the transmission and severity parameters of the 2022-2023 global outbreak.BMJ global health · 2025Pooled it
- Rapid review and meta-analysis of serial intervals for SARS-CoV-2 Delta and Omicron variants.BMC infectious diseases · 2023Pooled it
- A systematic comparison of methodologies for the estimation of the serial interval.Infectious Disease Modelling · 2026Article
- Inference of latent epidemic regimes and generative simulations reveal how inequality and mobility shape COVID-19 transmission.Scientific reports · 2026Article
- Serial Interval and Intervention Efficiency in Pertussis Outbreak, South Korea, 2024.Emerging infectious diseases · 2026Article
- Daily profile of COVID-19 infections in Europe - a biophysical perspective.Biophysical reviews · 2025Review
- Clinical progression parameters associated with SARS-CoV-2, influenza, and respiratory syncytial virus infections in a large US integrated healthcare population.PLoS computational biology · 2025Article
- Nonparametric serial interval estimation with uniform mixtures.PLoS computational biology · 2025Article
- Mathematical modelling for pandemic preparedness in Canada: Learning from COVID-19.Canada communicable disease report = Releve des maladies transmissibles au Canada · 2024Article
- Best practices for estimating and reporting epidemiological delay distributions of infectious diseases.PLoS computational biology · 2024Article
- rtestim: Time-varying reproduction number estimation with trend filtering.PLoS computational biology · 2024Article
- Incorporating testing volume into estimation of effective reproduction number dynamics.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2024Article
- Detecting changes in generation and serial intervals under varying pathogen biology, contact patterns and outbreak response.PLoS computational biology · 2024Article
- Marginal effects of public health measures and COVID-19 disease burden in China: A large-scale modelling study.PLoS computational biology · 2023Article
- Rates of SARS-CoV-2 transmission between and into California state prisons.medRxiv : the preprint server for health sciences · 2023Article
- Estimating generation time of SARS-CoV-2 variants in Italy from the daily incidence rate.Scientific reports · 2023Article
- Estimated incubation period distributions of mpox using cases from two international European festivals and outbreaks in a club in Berlin, May to June 2022.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2023Article
- Inferring the differences in incubation-period and generation-interval distributions of the Delta and Omicron variants of SARS-CoV-2.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Dining-Out Behavior as a Proxy for the Superspreading Potential of SARS-CoV-2 Infections: Modeling Analysis.JMIR public health and surveillance · 2023Article
- A new method for the joint estimation of instantaneous reproductive number and serial interval during epidemics.PLoS computational biology · 2023Article
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Authors and funding
9 authors.
Funding
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Abstract
The reproduction number R and the growth rate r are critical epidemiological quantities. They are linked by generation intervals, the time between infection and onward transmission. Because generation intervals are difficult to observe, epidemiologists often substitute serial intervals, the time between symptom onset in successive links in a transmission chain. Recent studies suggest that such substitution biases estimates of R based on r. Here we explore how these intervals vary over the course of an epidemic, and the implications for R estimation. Forward-looking serial intervals, measuring time forward from symptom onset of an infector, correctly describe the renewal process of symptomatic cases and therefore reliably link R with r. In contrast, backward-looking intervals, which measure time backward, and intrinsic intervals, which neglect population-level dynamics, give incorrect R estimates. Forward-looking intervals are affected both by epidemic dynamics and by censoring, changing in complex ways over the course of an epidemic. We present a heuristic method for addressing biases that arise from neglecting changes in serial intervals. We apply the method to early (21 January to February 8, 2020) serial interval-based estimates of R for the COVID-19 outbreak in China outside Hubei province; using improperly defined serial intervals in this context biases estimates of initial R by up to a factor of 2.6. This study demonstrates the importance of early contact tracing efforts and provides a framework for reassessing generation intervals, serial intervals, and R estimates for COVID-19.
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