ArticleEpidemics2019
Improved inference of time-varying reproduction numbers during infectious disease outbreaks.
Article in Epidemics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 246 papers, 2 of them syntheses that pooled it.
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Who cites it
246 citing papers in PubMed, 2 syntheses or guidelines pooled it, 569 citations in OpenAlex.
- 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.Statistical methods in medical research · 2022Pooled it
- Systematic review of empirical studies comparing the effectiveness of non-pharmaceutical interventions against COVID-19.The Journal of infection · 2021Pooled it
- Comparing methods to estimate time-varying reproduction numbers using genomic and epidemiological data.Infectious Disease Modelling · 2026Article
- Estimating the epidemic size of chikungunya virus infection in Guangzhou, China, from July to September 2025: a single-center cross-sectional study.Emerging microbes & infections · 2026Article
- Redefining and estimating the early-phase reproduction ratio for epidemic outbreaks in spatially structured populations.PLoS computational biology · 2026Article
- Machine learning and probabilistic approaches for forecasting infectious disease transmission and cases.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2026Article
- Robust estimation of the time-dependent reproduction number in the presence of weekend reporting effects.BMC global and public health · 2026Article
- Real-time analytical insights for disease surveillance and response during the severe drought and food security crisis, Somalia 2022-2023.Conflict and health · 2026Article
- Estimating influenza transmission parameters: Comparing two study designs, 2023-2024.Epidemics · 2026Article
- Enhancing time-varying reproduction number estimates for COVID-19 with behavior and surveillance data in South Korea, 2020-2022.Archives of public health = Archives belges de sante publique · 2026Article
- Article
- Evaluation and estimation of epidemic trajectories for SARS-CoV-2 from clinical and wastewater data in Gauteng Province, South Africa.PLOS global public health · 2026Article
- Robust uncertainty quantification in popular estimators of the instantaneous reproduction number.American journal of epidemiology · 2025Article
- Real-time inference of the end of an outbreak: Temporally aggregated disease incidence data and under-reporting.Infectious Disease Modelling · 2025Article
- Estimated transmission dynamics of SARS-CoV-2 variants from wastewater are unbiased and robust to differential shedding.Nature communications · 2025Article
- Nonparametric serial interval estimation with uniform mixtures.PLoS computational biology · 2025Article
- A Primer on Inference and Prediction With Epidemic Renewal Models and Sequential Monte Carlo.Statistics in medicine · 2025Article
- Evolving infectious disease dynamics shape school-based intervention effectiveness.Nature communications · 2025Article
- Revealing spatiotemporal variations in areas potentially linked to COVID-19 spread using fine-grained population data.Scientific reports · 2025Article
- The time-dependent reproduction number for epidemics in heterogeneous populations.Journal of the Royal Society, Interface · 2025Article
186 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
13 authors at 11 institutions in 7 countries.
Funding
Abstract
Accurate estimation of the parameters characterising infectious disease transmission is vital for optimising control interventions during epidemics. A valuable metric for assessing the current threat posed by an outbreak is the time-dependent reproduction number, i.e. the expected number of secondary cases caused by each infected individual. This quantity can be estimated using data on the numbers of observed new cases at successive times during an epidemic and the distribution of the serial interval (the time between symptomatic cases in a transmission chain). Some methods for estimating the reproduction number rely on pre-existing estimates of the serial interval distribution and assume that the entire outbreak is driven by local transmission. Here we show that accurate inference of current transmissibility, and the uncertainty associated with this estimate, requires: (i) up-to-date observations of the serial interval to be included, and; (ii) cases arising from local transmission to be distinguished from those imported from elsewhere. We demonstrate how pathogen transmissibility can be inferred appropriately using datasets from outbreaks of H1N1 influenza, Ebola virus disease and Middle-East Respiratory Syndrome. We present a tool for estimating the reproduction number in real-time during infectious disease outbreaks accurately, which is available as an R software package (EpiEstim 2.2). It is also accessible as an interactive, user-friendly online interface (EpiEstim App), permitting its use by non-specialists. Our tool is easy to apply for assessing the transmission potential, and hence informing control, during future outbreaks of a wide range of invading pathogens.
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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.