ArticlePLoS computational biology2022
EpiLPS: A fast and flexible Bayesian tool for estimation of the time-varying reproduction number.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
- Redefining and estimating the early-phase reproduction ratio for epidemic outbreaks in spatially structured populations.PLoS computational biology · 2026Article
- Unjustified Poisson assumptions lead to overconfident estimates of the effective reproductive number.Epidemiology and infection · 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
- Unlocking the power of time-since-infection models: data augmentation for improved instantaneous reproduction number estimation.Biostatistics (Oxford, England) · 2025Article
- Estimation of the epidemiological characteristics of scabies.Nature communications · 2025Review
- Robust uncertainty quantification in popular estimators of the instantaneous reproduction number.American journal of epidemiology · 2025Article
- A Primer on Inference and Prediction With Epidemic Renewal Models and Sequential Monte Carlo.Statistics in medicine · 2025Article
- Nonparametric serial interval estimation with uniform mixtures.PLoS computational biology · 2025Article
- Assessing the role of children in the COVID-19 pandemic in Belgium using perturbation analysis.Nature communications · 2025Article
- Flexible Bayesian estimation of incubation times.American journal of epidemiology · 2025Article
- A novel approach to estimatingFrontiers in public health · 2025Article
- Baseline nowcasting methods for handling delays in epidemiological data.Wellcome open research · 2025Article
- Time-varying reproduction number estimation: fusing compartmental models with generalized additive models.Journal of the Royal Society, Interface · 2025Article
- Estimating the time-varying effective reproduction number via Cycle Threshold-based Transformer.PLoS computational biology · 2024Article
- rtestim: Time-varying reproduction number estimation with trend filtering.PLoS computational biology · 2024Article
- Real-time estimation of the effective reproduction number of COVID-19 from behavioral data.Scientific reports · 2023Article
- Advancements in Defining and Estimating the Reproduction Number in Infectious Disease Epidemiology.China CDC weekly · 2023Article
- Estimating the epidemic reproduction number from temporally aggregated incidence data: A statistical modelling approach and software tool.PLoS computational biology · 2023Article
- Longitudinal social contact data analysis: insights from 2 years of data collection in Belgium during the COVID-19 pandemic.BMC public health · 2023Article
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5 authors.
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Abstract
In infectious disease epidemiology, the instantaneous reproduction number [Formula: see text] is a time-varying parameter defined as the average number of secondary infections generated by an infected individual at time t. It is therefore a crucial epidemiological statistic that assists public health decision makers in the management of an epidemic. We present a new Bayesian tool (EpiLPS) for robust estimation of the time-varying reproduction number. The proposed methodology smooths the epidemic curve and allows to obtain (approximate) point estimates and credible intervals of [Formula: see text] by employing the renewal equation, using Bayesian P-splines coupled with Laplace approximations of the conditional posterior of the spline vector. Two alternative approaches for inference are presented: (1) an approach based on a maximum a posteriori argument for the model hyperparameters, delivering estimates of [Formula: see text] in only a few seconds; and (2) an approach based on a Markov chain Monte Carlo (MCMC) scheme with underlying Langevin dynamics for efficient sampling of the posterior target distribution. Case counts per unit of time are assumed to follow a negative binomial distribution to account for potential overdispersion in the data that would not be captured by a classic Poisson model. Furthermore, after smoothing the epidemic curve, a "plug-in'' estimate of the reproduction number can be obtained from the renewal equation yielding a closed form expression of [Formula: see text] as a function of the spline parameters. The approach is extremely fast and free of arbitrary smoothing assumptions. EpiLPS is applied on data of SARS-CoV-1 in Hong-Kong (2003), influenza A H1N1 (2009) in the USA and on the SARS-CoV-2 pandemic (2020-2021) for Belgium, Portugal, Denmark and France.
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