ArticleJournal of the Royal Society, Interface2025
Estimating epidemic dynamics with genomic and time series data.
Article in Journal of the Royal Society, Interface, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Comparing methods to estimate time-varying reproduction numbers using genomic and epidemiological data.Infectious Disease Modelling · 2026Article
- Hidden Burden of a Measles Outbreak Revealed by Genomic and Transmission Models.medRxiv : the preprint server for health sciences · 2026Article
- Estimating epidemic dynamics with genomic and time series data.Journal of the Royal Society, Interface · 2025Article
- The EpiFusion Analysis Framework for joint phylodynamic and epidemiological analysis of outbreak characteristics.F1000Research · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurately estimating the prevalence and transmissibility of an infectious disease is an important task in genetic infectious disease epidemiology. However, generating accurate estimates of these quantities, that make use of both epidemic time series and pathogen genome sequence data, is a challenging problem. Phylogenetic birth-death processes are a popular choice for modelling the transmission of infectious diseases, but it is difficult to estimate the prevalence of infection with them. Here, we extended our approximate likelihood approach, which combines phylogenetic information from sampled pathogen genomes and epidemiological information from a time series of case counts, to estimate historical prevalence in addition to the effective reproduction number. We implement this new method in a BEAST2 package called Timtam. In a simulation study our approximation is seen to be well-calibrated and recovers the parameters of simulated data. To demonstrate how Timtam can be applied to real datasets, we carried out empirical analyses of data from two infectious disease outbreaks: the outbreak of SARS-CoV-2 onboard the
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