ArticlePLoS computational biology2023
Estimating the epidemic reproduction number from temporally aggregated incidence data: A statistical modelling approach and software tool.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- A systematic comparison of methodologies for the estimation of the serial interval.Infectious Disease Modelling · 2026Article
- Estimating time-varying cholera transmission and oral cholera vaccine effectiveness in Haiti and Cameroon, 2021-2023.BMJ open · 2026Article
- Unjustified Poisson assumptions lead to overconfident estimates of the effective reproductive number.Epidemiology and infection · 2026Article
- Robust estimation of the time-dependent reproduction number in the presence of weekend reporting effects.BMC global and public health · 2026Article
- Comparing Wastewater-Based and Case-BasedEpidemiology and infection · 2026Article
- Article
- Spatio-temporal analysis of dengue outbreaks in India (2021-2025): evidence from the secondary analysis of Integrated Disease Surveillance Programme.BMJ public health · 2026Article
- Influenza A/H3N2 epidemiology in England during the 2025 to 2026 season: a mathematical modelling study.Communications health · 2026Article
- Measuring real-time disease transmissibility with temperature-dependent generation intervals.PLoS computational biology · 2026Article
- Review
- Interpreting epidemiological surveillance data: a modelling study based on Pune city.Scientific reports · 2025Article
- Incorporating human mobility to enhance epidemic response and estimate real-time reproduction numbers.PLoS computational biology · 2025Article
- Unexpected Transmission Dynamics in a University Town: Lessons From COVID-19.Epidemiology (Cambridge, Mass.) · 2025Article
- Community and Hospital-Based Laboratory Surveillance for Influenza, Respiratory Syncytial Virus, and SARS-CoV-2 During the 2023-2024 Season, Lyon, France.Journal of medical virology · 2025Article
- Real-time inference of the end of an outbreak: Temporally aggregated disease incidence data and under-reporting.Infectious Disease Modelling · 2025Article
- A Primer on Inference and Prediction With Epidemic Renewal Models and Sequential Monte Carlo.Statistics in medicine · 2025Article
- The time-dependent reproduction number for epidemics in heterogeneous populations.Journal of the Royal Society, Interface · 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
- A flexible framework for local-level estimation of the effective reproductive number in geographic regions with sparse data.BMC medical research methodology · 2025Article
- A Flexible Framework for Local-Level Estimation of the Effective Reproductive Number in Geographic Regions with Sparse Data.medRxiv : the preprint server for health sciences · 2025Article
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Authors and funding
4 authors.
Funding
Abstract
The time-varying reproduction number (Rt) is an important measure of epidemic transmissibility that directly informs policy decisions and the optimisation of control measures. EpiEstim is a widely used opensource software tool that uses case incidence and the serial interval (SI, time between symptoms in a case and their infector) to estimate Rt in real-time. The incidence and the SI distribution must be provided at the same temporal resolution, which can limit the applicability of EpiEstim and other similar methods, e.g. for contexts where the time window of incidence reporting is longer than the mean SI. In the EpiEstim R package, we implement an expectation-maximisation algorithm to reconstruct daily incidence from temporally aggregated data, from which Rt can then be estimated. We assess the validity of our method using an extensive simulation study and apply it to COVID-19 and influenza data. For all datasets, the influence of intra-weekly variability in reported data was mitigated by using aggregated weekly data. Rt estimated on weekly sliding windows using incidence reconstructed from weekly data was strongly correlated with estimates from the original daily data. The simulation study revealed that Rt was well estimated in all scenarios and regardless of the temporal aggregation of the data. In the presence of weekend effects, Rt estimates from reconstructed data were more successful at recovering the true value of Rt than those obtained from reported daily data. These results show that this novel method allows Rt to be successfully recovered from aggregated data using a simple approach with very few data requirements. Additionally, by removing administrative noise when daily incidence data are reconstructed, the accuracy of Rt estimates can be improved.
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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.