ArticleGenetics2015
Inferring epidemiological dynamics with Bayesian coalescent inference: the merits of deterministic and stochastic models.
Article in Genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Data-Driven Models of Foot-and-Mouth Disease Dynamics: A Review.Transboundary and emerging diseases · 2017Pooled it
- Integrating mathematical modelling and artificial intelligence to combat emerging viral syndemics: A systematic review.New microbes and new infections · 2026Review
- Viral Simulation Reveals Overestimation Bias in Within-Host Phylodynamic Migration Rate Estimates Under Selection.Molecular biology and evolution · 2026Article
- Robust phylodynamic inference and model specification for HIV transmission dynamics.Epidemics · 2025Article
- MASCOT-Skyline integrates population and migration dynamics to enhance phylogeographic reconstructions.PLoS computational biology · 2025Article
- Leveraging insect-specific viruses to elucidate mosquito population structure and dynamics.PLoS pathogens · 2023Review
- Epidemiological inference for emerging viruses using segregating sites.Nature communications · 2023Article
- Statistical Challenges in Tracking the Evolution of SARS-CoV-2.Statistical science : a review journal of the Institute of Mathematical Statistics · 2022Article
- A computationally tractable birth-death model that combines phylogenetic and epidemiological data.PLoS computational biology · 2022Article
- Epidemiological inference from pathogen genomes: A review of phylodynamic models and applications.Virus evolution · 2022Review
- Bayesian reconstruction of transmission trees from genetic sequences and uncertain infection times.Statistical applications in genetics and molecular biology · 2020Article
- Applied phyloepidemiology: Detecting drivers of pathogen transmission from genomic signatures using density measures.Evolutionary applications · 2020Article
- Dispersal Kernels may be Scalable: Implications from a Plant Pathogen.Journal of biogeography · 2019Article
- BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis.PLoS computational biology · 2019Article
- Quantifying the Aftermath: Recent Outbreaks Among People Who Inject Drugs and the Utility of Phylodynamics.The Journal of infectious diseases · 2018Article
- Towards a genomics-informed, real-time, global pathogen surveillance system.Nature reviews. Genetics · 2018Review
- A model-based clustering method to detect infectious disease transmission outbreaks from sequence variation.PLoS computational biology · 2017Article
- Novel analytic tools for the study of porcine reproductive and respiratory syndrome virus (PRRSv) in endemic settings: lessons learned in the U.S.Porcine health management · 2016Review
- How well can the exponential-growth coalescent approximate constant-rate birth-death population dynamics?Proceedings. Biological sciences · 2015Article
Corrections and comments
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4 authors.
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Abstract
Estimation of epidemiological and population parameters from molecular sequence data has become central to the understanding of infectious disease dynamics. Various models have been proposed to infer details of the dynamics that describe epidemic progression. These include inference approaches derived from Kingman's coalescent theory. Here, we use recently described coalescent theory for epidemic dynamics to develop stochastic and deterministic coalescent susceptible-infected-removed (SIR) tree priors. We implement these in a Bayesian phylogenetic inference framework to permit joint estimation of SIR epidemic parameters and the sample genealogy. We assess the performance of the two coalescent models and also juxtapose results obtained with a recently published birth-death-sampling model for epidemic inference. Comparisons are made by analyzing sets of genealogies simulated under precisely known epidemiological parameters. Additionally, we analyze influenza A (H1N1) sequence data sampled in the Canterbury region of New Zealand and HIV-1 sequence data obtained from known United Kingdom infection clusters. We show that both coalescent SIR models are effective at estimating epidemiological parameters from data with large fundamental reproductive number [Formula: see text] and large population size [Formula: see text]. Furthermore, we find that the stochastic variant generally outperforms its deterministic counterpart in terms of error, bias, and highest posterior density coverage, particularly for smaller [Formula: see text] and [Formula: see text]. However, each of these inference models is shown to have undesirable properties in certain circumstances, especially for epidemic outbreaks with [Formula: see text] close to one or with small effective susceptible populations.
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