ArticlePLoS computational biology2015
A Systematic Bayesian Integration of Epidemiological and Genetic Data.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- Article
- Epidemiology and economics of foot-and-mouth disease: current understanding and knowledge gaps.Veterinary research · 2025Review
- ScITree: Scalable Bayesian inference of transmission tree from epidemiological and genomic data.PLoS computational biology · 2025Article
- JUNIPER: Reconstructing Transmission Events from Next-Generation Sequencing Data at Scale.Research square · 2025Article
- JUNIPER: Reconstructing Transmission Events from Next-Generation Sequencing Data at Scale.medRxiv : the preprint server for health sciences · 2025Article
- Bayesian estimation of transmission networks for infectious diseases.Journal of mathematical biology · 2025Article
- Methods to Estimate the Between-Population Level Effective Reproductive Number for Infectious Disease Epidemics: Foot-And-Mouth Disease (FMD) in Vietnam.Transboundary and emerging diseases · 2024Article
- Inferring Viral Transmission Pathways from Within-Host Variation.medRxiv : the preprint server for health sciences · 2023Article
- Reconstruction of the Transmission Chain of COVID-19 Outbreak in Beijing's Xinfadi Market, China.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2022Article
- Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling.Epidemics · 2022Article
- Methods Combining Genomic and Epidemiological Data in the Reconstruction of Transmission Trees: A Systematic Review.Pathogens (Basel, Switzerland) · 2022Review
- A computationally tractable birth-death model that combines phylogenetic and epidemiological data.PLoS computational biology · 2022Article
- Characterizing viral within-host diversity in fast and non-equilibrium demo-genetic dynamics.Frontiers in microbiology · 2022Article
- Integrating epidemiological and genetic data with different sampling intensities into a dynamic model of respiratory syncytial virus transmission.Scientific reports · 2021Article
- Identifying likely transmissions in Mycobacterium bovis infected populations of cattle and badgers using the Kolmogorov Forward Equations.Scientific reports · 2020Article
- Bayesian reconstruction of transmission trees from genetic sequences and uncertain infection times.Statistical applications in genetics and molecular biology · 2020Article
- Characterizing superspreading events and age-specific infectiousness of SARS-CoV-2 transmission in Georgia, USA.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Probabilistic reconstruction of measles transmission clusters from routinely collected surveillance data.Journal of the Royal Society, Interface · 2020Article
- Modelling, Bayesian inference, and model assessment for nosocomial pathogens using whole-genome-sequence data.Statistics in medicine · 2020Article
- Transmission network reconstruction for foot-and-mouth disease outbreaks incorporating farm-level covariates.PloS one · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genetic sequence data on pathogens have great potential to inform inference of their transmission dynamics ultimately leading to better disease control. Where genetic change and disease transmission occur on comparable timescales additional information can be inferred via the joint analysis of such genetic sequence data and epidemiological observations based on clinical symptoms and diagnostic tests. Although recently introduced approaches represent substantial progress, for computational reasons they approximate genuine joint inference of disease dynamics and genetic change in the pathogen population, capturing partially the joint epidemiological-evolutionary dynamics. Improved methods are needed to fully integrate such genetic data with epidemiological observations, for achieving a more robust inference of the transmission tree and other key epidemiological parameters such as latent periods. Here, building on current literature, a novel Bayesian framework is proposed that infers simultaneously and explicitly the transmission tree and unobserved transmitted pathogen sequences. Our framework facilitates the use of realistic likelihood functions and enables systematic and genuine joint inference of the epidemiological-evolutionary process from partially observed outbreaks. Using simulated data it is shown that this approach is able to infer accurately joint epidemiological-evolutionary dynamics, even when pathogen sequences and epidemiological data are incomplete, and when sequences are available for only a fraction of exposures. These results also characterise and quantify the value of incomplete and partial sequence data, which has important implications for sampling design, and demonstrate the abilities of the introduced method to identify multiple clusters within an outbreak. The framework is used to analyse an outbreak of foot-and-mouth disease in the UK, enhancing current understanding of its transmission dynamics and evolutionary process.
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