SynthesisPhilosophical transactions of the Royal Society of London. Series B, Biological sciences2013
Coalescent inference for infectious disease: meta-analysis of hepatitis C.
Synthesis in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Assessing the effect of model specification and prior sensitivity on Bayesian tests of temporal signal.PLoS computational biology · 2024Article
- Estimating the fitness cost and benefit of antimicrobial resistance from pathogen genomic data.Journal of the Royal Society, Interface · 2023Article
- Fitting stochastic epidemic models to gene genealogies using linear noise approximation.The annals of applied statistics · 2023Article
- Model design for nonparametric phylodynamic inference and applications to pathogen surveillance.Virus evolution · 2023Article
- Genomic epidemiology ofThe Lancet regional health. Western Pacific · 2022Article
- ComputingInfectious Disease Modelling · 2022Article
- Statistical Challenges in Tracking the Evolution of SARS-CoV-2.Statistical science : a review journal of the Institute of Mathematical Statistics · 2022Article
- Markov genealogy processes.Theoretical population biology · 2022Article
- Epidemiological inference from pathogen genomes: A review of phylodynamic models and applications.Virus evolution · 2022Review
- Employing phylogenetic tree shape statistics to resolve the underlying host population structure.BMC bioinformatics · 2021Article
- Model design for non-parametric phylodynamic inference and applications to pathogen surveillance.bioRxiv : the preprint server for biology · 2021Article
- Factors influencing estimates of HIV-1 infection timing using BEAST.PLoS computational biology · 2021Article
- Agricultural and geographic factors shaped the North American 2015 highly pathogenic avian influenza H5N2 outbreak.PLoS pathogens · 2020Article
- Bayesian phylodynamic inference with complex models.PLoS computational biology · 2018Article
- Modeling the Growth and Decline of Pathogen Effective Population Size Provides Insight into Epidemic Dynamics and Drivers of Antimicrobial Resistance.Systematic biology · 2018Article
- Quantifying the Aftermath: Recent Outbreaks Among People Who Inject Drugs and the Utility of Phylodynamics.The Journal of infectious diseases · 2018Article
- Biased phylodynamic inferences from analysing clusters of viral sequences.Virus evolution · 2017Article
- Phylodynamic Inference across Epidemic Scales.Molecular biology and evolution · 2017Article
- Phylogenetic Tools for Generalized HIV-1 Epidemics: Findings from the PANGEA-HIV Methods Comparison.Molecular biology and evolution · 2017Article
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Abstract
Genetic analysis of pathogen genomes is a powerful approach to investigating the population dynamics and epidemic history of infectious diseases. However, the theoretical underpinnings of the most widely used, coalescent methods have been questioned, casting doubt on their interpretation. The aim of this study is to develop robust population genetic inference for compartmental models in epidemiology. Using a general approach based on the theory of metapopulations, we derive coalescent models under susceptible-infectious (SI), susceptible-infectious-susceptible (SIS) and susceptible-infectious-recovered (SIR) dynamics. We show that exponential and logistic growth models are equivalent to SI and SIS models, respectively, when co-infection is negligible. Implementing SI, SIS and SIR models in BEAST, we conduct a meta-analysis of hepatitis C epidemics, and show that we can directly estimate the basic reproductive number (R(0)) and prevalence under SIR dynamics. We find that differences in genetic diversity between epidemics can be explained by differences in underlying epidemiology (age of the epidemic and local population density) and viral subtype. Model comparison reveals SIR dynamics in three globally restricted epidemics, but most are better fit by the simpler SI dynamics. In summary, metapopulation models provide a general and practical framework for integrating epidemiology and population genetics for the purposes of joint inference.
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