ArticlePLoS computational biology2021
Improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
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Who cites it
62 citing papers in PubMed.
- Comparing methods to estimate time-varying reproduction numbers using genomic and epidemiological data.Infectious Disease Modelling · 2026Article
- Results of the epidemiological measurement of endemics, epidemics, and pandemics.Infectious Disease Modelling · 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
- Genomic epidemiology of the 2025 mpox epidemic in Sierra Leone.Nature medicine · 2026Article
- Viral introductions and return to baseline sexual behaviors maintain low-level mpox incidence in Los Angeles.Nature communications · 2026Article
- Spatial and social determinants of the 1857 yellow fever epidemic in Lisbon.PLoS neglected tropical diseases · 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
- Measuring real-time disease transmissibility with temperature-dependent generation intervals.PLoS computational biology · 2026Article
- SARS-CoV-2 lineage-specific disease symptoms and disease severity in a city in southeastern Brazil.Revista do Instituto de Medicina Tropical de Sao Paulo · 2026Article
- Data assimilation for estimating time-varying reproduction numbers.Journal of the Royal Society, Interface · 2025Article
- Article
- Robust uncertainty quantification in popular estimators of the instantaneous reproduction number.American journal of epidemiology · 2025Article
- Inferring pathogen superspreading potential using early spatial spread patterns.medRxiv : the preprint server for health sciences · 2025Article
- Epidemiological indices with multiple circulating pathogen strains.Infectious Disease Modelling · 2025Article
- A Primer on Inference and Prediction With Epidemic Renewal Models and Sequential Monte Carlo.Statistics in medicine · 2025Article
- Viral introductions and return to baseline sexual behaviors maintain low-level mpox incidence in Los Angeles County, USA, 2023-2024.medRxiv : the preprint server for health sciences · 2025Article
- Spatio-temporal dynamics of hand, foot and mouth disease in Malaysia, 2009-2019.PLoS neglected tropical diseases · 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 renewal-equation approach to estimatingPhilosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
2 more citing papers are in PubMed but not listed here.
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1 author.
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Abstract
We construct a recursive Bayesian smoother, termed EpiFilter, for estimating the effective reproduction number, R, from the incidence of an infectious disease in real time and retrospectively. Our approach borrows from Kalman filtering theory, is quick and easy to compute, generalisable, deterministic and unlike many current methods, requires no change-point or window size assumptions. We model R as a flexible, hidden Markov state process and exactly solve forward-backward algorithms, to derive R estimates that incorporate all available incidence information. This unifies and extends two popular methods, EpiEstim, which considers past incidence, and the Wallinga-Teunis method, which looks forward in time. We find that this combination of maximising information and minimising assumptions significantly reduces the bias and variance of R estimates. Moreover, these properties make EpiFilter more statistically robust in periods of low incidence, where several existing methods can become destabilised. As a result, EpiFilter offers improved inference of time-varying transmission patterns that are advantageous for assessing the risk of upcoming waves of infection or the influence of interventions, in real time and at various spatial scales.
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